Proceedings of the International Conference on Automated Planning and Scheduling
The annual ICAPS conference series was formed in 2003 through the merger of two preexisting biennial conferences, the International Conference on Artificial Intelligence Planning and Scheduling (AIPS) and the European Conference on Planning (ECP). ICAPS continues the traditional high standards of AIPS and ECP as an archival forum for new research in the field of automated planning and scheduling. The Proceedings of the International Conference on Automated Planning and Scheduling contains the annual, archival published work of the ICAPS conference.
Association for the Advancement of Artificial Intelligence
The International Conference on Automated Planning and Scheduling (ICAPS) is the premier forum for new research results on the theory and applications of planning and scheduling technology. ICAPS 2026, the 36th International Conference on Automated Planning and Scheduling, was held in Dublin, Ireland from June 27 to July 2, 2026. Conference co-chairs were Adi Botea and Alfonso Emilio Gerevini. The conference program co-chairs were Amanda Coles, Wheeler Ruml, and Sandhya Saisubramanian. The papers published at ICAPS 2026 include papers from the main track and four special tracks: Human-Aware Planning and Scheduling, Applications of Planning & Scheduling, Learning for Planning & Scheduling, and Planning & Scheduling in Robotics. All of these special tracks were chaired by two experts in the relevant disciplines. All papers present the latest advances in the field of automated planning and scheduling, ranging in scope from theoretical analyses of planning and scheduling problems and processes, to new algorithms for planning and scheduling under various constraints and assumptions, to empirical evaluation of planning and scheduling techniques in practical applications.
Copyright (c) 2026 Proceedings of the International Conference on Automated Planning and Scheduling
Simplifying Planning Tasks with Fact-Level Relevance Analysis
Planning problems described in formal languages often contain information that is irrelevant to reaching the specified goal, either by design (to account for multiple possible goals) or as a result of partially automated construction. Consequently, modern planners prune (some) extraneous information by performing safe relevance analysis of state variables prior to search, improving search performance. We show that much more aggressive pruning is possible by reasoning about relevance not at the level of variables, but rather variable *values*, i.e. *facts*. Our approach iteratively identifies relevant facts whenever they appear in the task goal or in the precondition of a relevant action, and identifies relevant actions whenever they achieve a relevant fact (rather than when they simply modify a relevant variable). This already reduces task size relative to variable-level analysis, but it also offers several opportunities for further algorithmic improvements. We formally prove that fact-level relevance analysis, and each subsequent algorithmic improvement we introduce, preserves all shortest optimal plans, without introducing new ones. We show empirically that our approach significantly reduces task size and consequently planning time across a wide range of planning tasks.
Copyright (c) 2026 Association for the Advancement of Artificial Intelligence
Subgoaling Relaxation-based Heuristics for Numeric Planning with Infinite Actions
Numeric planning with control parameters extends the standard numeric planning model by introducing action parameters as free numeric variables that must be instantiated during planning. This results in a potentially infinite number of applicable actions in a state. In this setting, off-the-shelf numeric heuristics that leverage the action structure are not feasible. In this paper, we identify a tractable subset of these problems—namely, controllable simple numeric problems—and propose an optimistic compilation approach that transforms them into simple numeric tasks. To do so, we abstract control-dependent expressions into bounded constant effects and relaxed preconditions. The proposed compilation makes it possible to effectively use subgoaling heuristics to estimate goal distance in numeric planning problems involving control parameters. Our results demonstrate that this approach is an effective and computationally feasible way of applying traditional numeric heuristics to settings with an infinite number of possible actions, pushing the boundaries of the current state of the art.
A Constraint Formulation for Domain Repair with Ground or Lifted Test Plans
Engineering automated planning domains is a meticulous and time-consuming process. In recent years, much attention has been given to techniques assisting domain designers in both writing new domains and ensuring their correctness. This paper addresses the problem of repairing a flawed planning domain to comply with a given example problem and plan. The goal is to suggest a minimal set of repairs, which can either be the removal of a condition or effect, or the addition of a new effect in some of the domain's action schemas. The cases of fully specified (ground) and partially specified (lifted) plans, whose actions' parameters can be variables, are handled separately in previous approaches. We introduce an alternative method that covers both ground and lifted cases in a unified way by repurposing an existing encoding of planning as constraint satisfaction. Despite its simplicity, we show the approach to be competitive with the ground case baseline solver and substantially outperforming the lifted one.
Optimal Solutions for the Moving Target Vehicle Routing Problem via Branch-and-Price with Relaxed Continuity
The Moving Target Vehicle Routing Problem (MT-VRP) seeks trajectories for several agents that intercept a set of moving targets, subject to speed, time window, and capacity constraints. We introduce an exact algorithm, Branch-and-Price with Relaxed Continuity (BPRC), for the MT-VRP. The main challenge in a branch-and-price approach for the MT-VRP is the pricing subproblem, which is complicated by moving targets and time-dependent travel costs between targets. Our key contribution is a new labeling algorithm that solves this subproblem by means of a novel dominance criterion tailored for problems with moving targets. Numerical results on instances with up to 25 targets show that our algorithm finds optimal solutions more than an order of magnitude faster than a baseline based on previous work, showing particular strength in scenarios with limited agent capacities.
Complete and Optimal Robust Planning Against Nature
Planning and acting in environments in which "nature" can modify the environment by exogenous events presents a challenge for intelligent agents that have to make sure their plans can still be executed. In principle, events of nature might disrupt the agent's plan, or worse, cause damage to the agent. In this paper, we build upon a recent concept of robust plans that are guaranteed to be successfully executed even if nature acts adversarially. The advantages of robust plans involve better understandability and not needing to sense the state of the environment during plan execution. We address the gap in prior work by presenting two methods for generating robust plans that are complete and guarantee optimality if an admissible heuristic is used with the A* algorithm. The methods are evaluated on five domains.
Compiling Expressive Planning with Data Types
Planning involves selecting action sequences to achieve goals from initial conditions, a fundamental task arising in contexts ranging from daily tasks to industrial processes. The Planning Domain Definition Language (PDDL) is the standard formalism for describing such problems, yet previous work has highlighted some of its limitations in expressivity. In particular, many real-world planning problems inherently exhibit structural patterns such as sets, arrays, and counting. These are features of the problem domain itself that are cumbersome to model directly in PDDL. We present a high-level modelling language that compiles to PDDL, supporting richer data structures and operations including arrays, sets, Boolean counting expressions, integer range variables, and bounded integer parameters in actions. This allows users to express problems in terms closer to their natural structure, without committing to a particular low-level encoding. From a single high-level specification, our framework can automatically generate and explore multiple PDDL encodings with different performance trade-offs in solving time, memory usage, and plan quality. We demonstrate our approach on a set of benchmark problems involving complex structural patterns. Our results show that compiled models are competitive with established hand-crafted models from the literature, and can sometimes outperform them.
In this paper, we study incremental LTLf synthesis – a form of reactive synthesis where the goals are given incrementally while in execution. In other words, the protagonist agent is already executing a strategy for a certain goal when it receives a new goal: at this point, the agent has to abandon the current strategy and synthesize a new strategy still fulfilling the original goal, which was given at the beginning, as well as the new goal, starting from the current instant. In this paper, we formally define the problem of incremental synthesis and study its solution. We propose a solution technique that efficiently performs incremental synthesis for multiple LTLf goals by leveraging auxiliary data structures constructed during automata-based synthesis. We also consider an alternative solution based on LTLf formula progression. We show that, in spite of the fact that formula progression can generate formulas that are exponentially larger than the original ones, their minimal automata remain bounded in size by that of the original formula. On the other hand, we show experimentally that, if implemented naively, i.e., by actually computing the automaton of the progressed formulas at each step, this formula progression-based solution is not competitive.
When Both Grounding and Not Grounding Are Bad – a Partially Grounded Encoding of Planning into SAT
Classical planning problems are typically defined using lifted first-order representations, which offer compactness and generality. While most planners ground these representations to simplify reasoning, this can cause an exponential blowup in size. Recent approaches instead operate directly on the lifted level to avoid full grounding. We explore a middle ground between fully lifted and fully grounded planning by introducing three SAT encodings that keep actions lifted while partially grounding predicates. Unlike previous SAT encodings, which scale quadratically with plan length, our approach scales linearly, enabling better performance on longer plans. Empirically, our best encoding outperforms the state of the art in length-optimal planning on hard-to-ground domains.
Dynamic Shelf Arrangement and Task Assignment with Stable Matching for Multi-Agent Pickup and Delivery with Multi-Item Packing Problem
The multi-agent pickup and delivery (MAPD) problem requires not only planning collision-free paths for carrier agents to carry items while avoiding obstacles but also optimizing task allocation among agents, and it has become a key problem in automated warehouse environments. Most existing studies rely on simplified models that fail to capture the constraints of real-world warehousing. We investigate a more realistic online variant, MAPD with multi-item packing problem (MAPD-MP), where agents process picking lists of multiple tasks, each requiring an agent to carry a shelf containing items to a picking station and return it to the original location. We incorporate the fact that each shelf has a distinct probability of being requested. To solve this problem, we integrated conventional planning methods with three components: shelf arrangement optimization, which optimizes shelf arrangement based on probabilistic demand; task assignment using the Gale–Shapley algorithm, which uses stable matching for task allocation; and holding-node integration, in which shelves are temporarily placed at holding nodes for subsequent tasks. Our experiments demonstrate that our method improves cooperative efficiency in large-scale agent teams and increases the picking-list processing throughput.
Fully Packed and Ready to Go: High-Density, Rearrangement-Free, Grid-Based Storage and Retrieval
We consider an ordered storage and retrieval problem: a set of uniform-sized, labeled loads (e.g., containers, pallets, or totes) must be placed in a 2D grid storage area as they arrive sequentially and then retrieved in a different sequence. Each load occupies a grid cell and may be moved by a robot or manipulator along the cardinal directions. Such storage systems arise in logistics, industrial, and transportation domains, where space utilization and retrieval time are critical metrics. To maximize space utilization, loads must be densely packed with some loads blocking access to others, which raises a key question: How should one store the loads to minimize costly rearrangements, i.e., the number of relocated loads? We identify conditions, alongside efficient algorithms, for achieving either zero or near-optimal rearrangements under different knowledge assumptions. While the online case (i.e., no prior knowledge of the storage and retrieval sequences) induces a trade-off between density and rearrangement, we show that even partial prior knowledge essentially eliminates the trade-off. When the sequences are fully known, we further provide an intriguing characterization: rearrangement can always be eliminated if the grid's open side (used to access the storage area) is at least 3 cells wide, even for storage at full capacity.
Scalable Algorithms with Provable Optimality Bounds for the Multiple Watchman Route Problem
In this paper, we tackle the Multiple Watchman Route Problem (MWRP), which aims to find a set of paths that M watchmen can follow such that every location on the map can be seen by at least one watchman. First, we propose multiple methods to reduce the state space over which a search needs to be conducted by pruning map areas that are guaranteed to be seen en route to other areas. Next, we introduce MWRP-CP3, an efficient optimal planner that combines these methods with techniques that improve the quality and calculation time of existing heuristics. We present several suboptimal algorithms with bounds on solution quality, including MxWA*, a general variant of weighted A* for makespan problems. We also present anytime variations of our suboptimal algorithms, as well as techniques to improve an existing suboptimal solution by solving multiple decomposed sub-problems. We show that MWRP-CP3 can reduce the search space by more than 95% and runs more than 200x faster than existing optimal algorithms on 2D grid maps. We also show that our suboptimal algorithms solve maps 3x larger than those solvable by MWRP-CP3. See mwrp-cp3.github.io for the open source codebase and video demonstrations.
PDDL Axioms Are Equivalent to Least Fixed Point Logic
Axioms are a feature of the Planning Domain Definition Language PDDL that can be considered as a generalization of database query languages such as Datalog. The PDDL standard restricts negative occurrences of predicates in axiom bodies to predicates that are directly set by actions and not derived by axioms. In the literature, authors often deviate from this limitation and only require that the set of axioms is stratifiable. We show that both variants can express exactly the same queries as least fixed point logic. They are thus strictly more expressive than stratified Datalog, which aligns with another restriction on axioms occasionally considered in the planning literature. Complementing this theoretical analysis, we also present a compilation that eliminates negative occurrences of derived predicates from PDDL axioms.
PING: A Physics-Informed Neuro-Symbolic Generator for Continuous-Time Planning
We present PING (Physics-Informed Neuro-Symbolic Generator), a novel, continuous planning framework that leverages untrained neural networks as generative function approximators to synthesize high-fidelity trajectory candidates without data or training. Departing from conventional hybrid planners that oscillate between discrete search and numerical optimization, PING operates natively in continuous function-valued action spaces, embedding symbolic constraints directly into the generation process. We introduce a generative mechanism where neural networks produce function-space candidates that are structurally guaranteed to satisfy boundary conditions using symbolic rules, thereby circumventing discretization artifacts and the local minima traps inherent to gradient-based trajectory optimization. To ensure feasibility, a rigorous verification engine exploits automatic differentiation to validate candidates against domain-specific differential equations and manifold constraints. This architecture enables a dual-mode strategy: a primary generation phase for rapid solution synthesis, backed by an iterative refinement mechanism when validation fails. By decoupling generation from optimization, PING provides a training-free framework that can be instantiated for different domains via domain-specific dynamics and constraints. Empirical evaluation across navigation, reservoir control, and HVAC domains demonstrates highly efficient runtime performance and broader coverage, with planning latencies reduced to seconds through massive parallel verification of thousands of symbolic candidates.
Optimal Path Planning in Hostile Environments
Coordinating agents through hazardous environments, such as aid-delivering drones navigating conflict zones or field robots traversing deployment areas filled with obstacles, poses fundamental planning challenges. We introduce and analyze the computational complexity of a new multi-agent path planning problem that captures this setting. A group of identical agents begins at a common start location and must navigate a graph-based environment to reach a common target. The graph contains hazards that eliminate agents upon contact but then enter a known cooldown period before reactivating. In this discrete-time, fully-observable, deterministic setting, the planning task is to compute a movement schedule that maximizes the number of agents reaching the target. We first prove that, despite the exponentially large space of feasible plans, optimal plans require only polynomially many steps, establishing membership in NP. We then show that the problem is NP-hard even when the environment graph is a tree. On the positive side, we present a polynomial-time algorithm for graphs consisting of vertex-disjoint paths from start to target. Our results establish a rich computational landscape for this problem, identifying both intractable and tractable fragments.
Sampling-Based Multi-Agent Path Planning Guided by Spatio-Temporal Logic Mission Objectives
Multi-agent systems operate in shared environments where they must communicate and coordinate to accomplish complex missions. Designing motion plans that guarantee such cooperative behavior is challenging, particularly in continuous state spaces with tightly coupled spatial interactions. Sampling-based planners such as Probabilistic Roadmaps (PRM) scale well to high-dimensional domains but lack mechanisms to enforce mission-level behavioral requirements. Spatio-Temporal Reach and Escape Logic (STREL) enables reasoning about dynamic spatial relations among agents, making it well-suited for specifying coordinated multi-agent tasks. We construct dynamics-aware PRMs for each agent and use them to build a timeless abstraction of the joint multi-agent transition system. The mission objectives are represented using STREL and are symbolically encoded using an SMT solver to synthesize joint trajectories that are both dynamically consistent and logically compliant with the specification. We demonstrate the effectiveness of our approach in the SwarmLab drone swarm simulator, where the framework consistently generates coordinated and specification-compliant paths.
Memory Assignment for Finite-Memory Strategies in Adversarial Patrolling Games
Adversarial Patrolling games form a subclass of Security games where a Defender moves between locations, guarding vulnerable targets. The main algorithmic problem is constructing a strategy for the Defender that minimizes the worst damage an Attacker can cause. We focus on the class of finite-memory (also known as regular) Defender’s strategies that experimentally outperformed other competing classes. A finite-memory strategy can be seen as a positional strategy on a finite set of states. Each state consists of a pair of a location and a certain integer value–called memory. Existing algorithms improve the transitional probabilities between the states, but require that the available memory size itself is assigned at each location manually. Choosing the right memory assignment is a well-known open and hard problem that hinders the usability of finite-memory strategies. We solve this issue by developing a general method that iteratively changes the memory assignment. Our algorithm can be used in connection with any black-box strategy optimization tool. We evaluate our method on various experiments and show its robustness by solving instances of various patrolling models.
Leveraging the Value of Information in POMDP Planning
Partially observable Markov decision processes (POMDPs) offer a principled formalism for planning under state and transition uncertainty. Despite advances made towards solving large POMDPs, obtaining performant policies under limited planning time remains a major challenge due to the curse of dimensionality and the curse of history. For many POMDP problems, the value of information (VOI) - the expected performance gain from reasoning about observations - varies over the belief space. We introduce a dynamic programming framework that exploits this structure by conditionally processing observations based on the value of information at each belief. Building on this framework, we propose Value of Information Monte Carlo planning (VOIMCP), a Monte Carlo Tree Search algorithm that allocates computational effort more efficiently by selectively disregarding observation information when the VOI is low, avoiding unnecessary branching of observations. We provide theoretical guarantees on the near-optimality of our VOI reasoning framework and derive non-asymptotic convergence bounds for VOIMCP. Simulation evaluations demonstrate that VOIMCP outperforms baselines on several POMDP benchmarks.
I Always Told My Mom That Order Is Overrated: Unordered HTN Planning Is in PSPACE and Models Problems Beyond STRIPS
Hierarchical Task Network planning is (in)famous for offering great modeling power but making plan existence undecidable. To lift the computational barrier, it is common to enforce orders between all tasks, which allows for an EXPTIME computation. We show that the opposite extreme, using no ordering constraints, reduces complexity further to PSPACE. This unites benefits of STRIPS and HTN planning in a single formalism: It matches the complexity of STRIPS planning while provably giving modelers more expressive power. We observe the same benefit for more, partially new, planning formalisms along the way to the main result. This motivates that these formalisms have similar merit on their own.
Domain-Independent Dynamic Programming with Constraint Propagation
There are two prevalent model-based paradigms for combinatorial problems: 1) state-based representations, such as heuristic search, dynamic programming (DP), and decision diagrams, and 2) constraint and domain-based representations, such as constraint programming (CP), (mixed-)integer programming, and Boolean satisfiability. In this paper, we bridge the gap between the DP and CP paradigms by integrating constraint propagation into DP, enabling a DP solver to prune states and transitions using constraint propagation. To this end, we implement constraint propagation using a general-purpose CP solver in the Domain-Independent Dynamic Programming framework and evaluate using heuristic search on three combinatorial optimisation problems: Single Machine Scheduling with Time Windows, the Resource Constrained Project Scheduling Problem (RCPSP), and the Travelling Salesperson Problem with Time Windows (TSPTW). Our evaluation shows that constraint propagation significantly reduces the number of state expansions, causing our approach to solve more instances than a DP solver for Single Machine Scheduling and RCPSP, and showing similar improvements for tightly constrained TSPTW instances. The runtime performance indicates that the benefits of propagation outweigh the overhead for constrained instances, but that further work into reducing propagation overhead could improve performance further. Our work is a key step in understanding the value of constraint propagation in DP solvers, providing a model-based approach to integrating DP and CP.
Reversibility and Reachability in HTN Planning: Formalization and Computational Complexities in the Totally-Ordered Setting
Action reversibility, that is, whether it is possible to undo effects of an action by other actions, has been studied in classical planning and, recently, in non-deterministic planning. In this paper, we formalize the notions of method and primitive task reversibility in the context of hierarchical task network (HTN) planning and provide complexity results. On top of that, we introduce various notions of reachability in the HTN setting, for which the reachability is still an unexplored area (in contrast to classical planning, in which reachability is well studied) We divide the reachability into two classes based on two perspectives, one restricting the allowed progression rules (i.e., reachability using executions of primitive tasks, or reachability using decompositions of compound tasks), and the other focusing on the desired target (i.e., a state, independently on a task network; a task network, independently of a state; or both at once). We show that the complexity of these problems varies significantly, ranging from EXPTIME-complete to constant-time. We also show that the introduced reversibility problems exhibit theoretical properties and complexity results analogous to the broader reachability classes.
Relaxing Is Hard: Complexity Results for Lifted Partial Order Causal Link Planning
We investigate Partial Order Causal Link (POCL) planning in the lifted setting, where plan steps are represented as parameterised action schemas. While delete relaxation has been shown to reduce the complexity of plan existence in the ground POCL setting, operating on lifted representations may at times be necessary to avoid the prohibitive cost of grounding. This motivates a separate complexity analysis for the lifted case. We formalise delete-relaxation via filter functions that selectively suppress delete effects, yielding differently strong relaxation semantics, with and without respect for pre-existing delete effects and causal links of the input plan. We prove that plan existence is EXPTIME--complete for most variants, even when the input plan is totally ordered. Further, we prove results ranging from NP--completeness to PSPACE--completeness for fixed-schema plan existence, with a tractable case when planning is done from scratch.
Risk-Bounded Multi-Agent Visual Navigation via Iterative Risk Allocation
Safe navigation is essential for autonomous systems operating in hazardous environments, especially when multiple agents must coordinate using only high-dimensional visual observations. While recent approaches successfully combine Goal-Conditioned RL (GCRL) for graph construction with Conflict-Based Search (CBS) for planning, they typically rely on deleting edges with high risk before running CBS to enforce safety. This binary strategy is overly conservative, precluding feasible missions that require traversing high-risk regions, even when the aggregate risk is acceptable. To address this, we introduce a framework for Risk-Bounded Multi-Agent Path Finding (∆-MAPF), where agents share a user-specified global risk budget (∆). Rather than permanently discarding edges, our framework dynamically distributes per-agent risk budgets (δi) during search via an Iterative Risk Allocation (IRA) layer that integrates with a standard CBS planner. We investigate two distribution strategies: a greedy surplus-deficit scheme for rapid feasibility repair, and a market-inspired mechanism that treats risk as a priced resource to guide improved allocation. The market-based mechanism yields a tunable trade-off wherein agents exploit available risk to secure shorter, more efficient paths, but revert to longer, safer detours under tighter budgets. Experiments in complex visual environments show that our dynamic allocation framework achieves higher success rates than baselines and effectively leverages the available safety budget to reduce travel time.
H-ANTS: Hierarchical Ant System with Insert-and-Prune Charging for Capacitated Electric Vehicle Routing
The rising popularity of electric vehicles (EVs) has led to the emergence of the capacitated electric vehicle routing problem (CEVRP), where routing decisions and charging schedules are tightly coupled. Existing single-level models suffer from complex search spaces, while decoupled models frequently yield suboptimal solutions due to myopic decision-making and weak coordination. To address these limitations, this study proposes the hierarchical ant system (H-ANTS), a hierarchical algorithm that systematically enhances the decoupled framework. At the high-level abstract planning stage, an improved max-min ant system (MMAS) is combined with a batch-improvement variable neighborhood search (BI-VNS) to generate high-quality abstract customer sequences. Subsequently, at the low-level plan refinement stage, an insert-prune heuristic (IPH) is employed, utilizing a saturate-and-prune mechanism to globally optimize charging schedules, effectively overcoming the limitations of traditional greedy repairs. The cost of the refined solution acts as a topology-aware feedback signal to guide high-level pheromone updates, enabling routing decisions to adapt effectively to energy constraints. Experimental results on benchmark instances demonstrate that H-ANTS achieves superior solution quality compared with several representative algorithms, confirming the effectiveness and practicality of the proposed method.
In many planning applications, we might be interested in finding plans that minimally modify the initial state to achieve the goals. We refer to this concept as plan disruption. In this paper, we formally introduce it, and define various planning-based compilations that aim to jointly optimize both the sum of action costs and plan disruption. Experimental results in different benchmarks show that the reformulated task can be effectively solved in practice to generate plans that balance both objectives.
Planning as Goal Recognition: Deriving Heuristics from Intention Models
Classical planning aims to find a sequence of actions, a plan, that maps a starting state into one of the goal states. If a trajectory appears to be leading to the goal, should we prioritise exploring it? Seminal work in goal recognition (GR) has defined GR in terms of a classical planning problem, adopting classical solvers and heuristics to recognise plans. We come full circle, and study the adoption and properties of GR-derived heuristics for seeking solutions to classical planning problems. We propose a new divergence-based framework for assessing goal intention, which informs a new class of efficiently-computable heuristics. As a proof of concept, we derive two such heuristics, and show that they can already yield improvements for top-scoring classical planners. Our work provides foundational knowledge for understanding and deriving probabilistic intention-based heuristics for planning.
Beyond Pruning: Leveraging Dominance Relations for Heuristic Propagation
In optimal heuristic search, A* is known to be optimally efficient in terms of node expansions when the only source of information available to guide the search is a heuristic function. However, when a dominance relation is available, dominance pruning can further reduce the number of node expansions while preserving optimality. In this work, we show that pruning nodes does not fully leverage the information dominance relations provide. Instead, one can use dominance to propagate heuristic information between states. This results in a search algorithm that shares information among different regions of the search space. This is always as informative as dominance pruning, and can potentially reduce search effort.
Finding a Dominating State in a Haystack: Efficient Data Structures for Dominance Pruning
Dominance pruning is a technique to reduce search effort by comparing states against each other and discarding dominated states. However, despite being effective at significantly reducing the number of expansions in many domains, the overhead of comparing states may lead to reduced performance. This is specially critical in hard problems, as the overhead of comparing all pairs of states grows quadratically with the number of explored states. In this paper, we investigate how to use data structures that exploit the inner structure of dominance relations in order to perform this check efficiently. We show that these data structures can avoid the quadratic worst-case in many domains, significantly improving the performance of search with dominance pruning.
AUPO – Abstracted Until Proven Otherwise: A Reward Distribution Based Abstraction Algorithm
We introduce a novel, drop-in modification to Monte Carlo Tree Search's (MCTS) decision policy that we call AUPO. Comparisons based on a range of IPPC benchmark problems show that AUPO outperforms vanilla MCTS in domains with dense rewards and value-equivalent sibling actions under finite iteration budgets. AUPO is an automatic action abstraction algorithm that solely relies on reward distribution statistics acquired during the MCTS. Thus, unlike other automatic abstraction algorithms, AUPO requires neither access to transition probabilities nor does AUPO require a directed acyclic search graph to build its abstraction, allowing AUPO to detect symmetric actions that state-of-the-art frameworks like ASAP struggle with when the resulting symmetric states are far apart in state space. Furthermore, as AUPO only affects the decision policy, it is not mutually exclusive with other abstraction techniques that only affect the tree search.
With the growing interest in practical quantum computing on noisy quantum hardware, quantum circuit optimization is becoming increasingly important. We consider the optimal synthesis of Clifford circuits, which are important for quantum error correction. While a scalable SAT encoding for Clifford synthesis exists, it only optimizes for the most noisy, i.e., binary quantum gates. That solution tends to add too many unary gates, thus introducing unnecessary noise sources. In this paper, we aim at synthesizing Clifford circuits with minimal binary gate count, and minimal unary gate count as a secondary optimization criterion. Expressing this in a performant SAT encoding is non-trivial. It turns out that planning specifications using PDDL with conditional effects are elegant and allow a flexible specification of costs. However, existing planners struggle on domains with conditional effects. The contribution of this paper is a number of different planning domains to solve the optimal binary/unary Clifford synthesis problem. The best performing solution combines the SAT approach for binary gates with a planning approach for unary gate minimization. As far as we know, this is the first approach that optimizes unary gates without increasing the binary gate count for Clifford circuits. With this hybrid approach, we present improved circuits for famous quantum error correcting codes, such as the Shor code and the Steane code. We believe that our various planning domains with conditional effects and costs form a challenging benchmark for contemporary planners.
Proactive Assistance Agent with Online Goal Recognition
Effective human–AI collaboration requires agents to do more than simply follow instructions; they must also infer and anticipate human intentions. This ability to understand and predict others’ goals is also important in decentralized multi-agent settings, where communication is limited. Although goal recognition has been widely studied in the planning community, its integration with downstream collaborative behaviors, such as determining when and how an agent should assist, remains underexplored. This paper presents a unified model-based framework that combines online goal recognition with a proactive assistance module, enabling an agent to provide efficient support without prior knowledge of another agent’s true objective. Experiments in a grid domain show our proposed approach significantly improves collaborative efficiency over existing algorithms and demonstrates strong robustness across diverse configurations. By integrating goal recognition with other decision-making components, this work establishes a solid algorithmic foundation and an extensible framework for the capabilities required in next-generation human–AI collaboration.
Complexity Results for Fixing Classical Models Using LTL to Express Which Solutions Are (Un)Desired
Model repair is the task of modifying a given planning model so that it satisfies a set of validity constraints. Prior frameworks encode constraints as finite sets of desired and undesired plans, which cannot capture properties of infinite sets of plans or patterns shared by all (non)solutions, thereby limiting their usefulness. We propose a unified formalism that subsumes prior approaches by incorporating validity constraints specified in Linear Temporal Logic over process traces (LTLp), thereby capturing temporal and structural properties of plans. We further present a comprehensive complexity analysis that begins with a highly restricted fragment of LTLp and progressively lifts restrictions, tracking how increasing expressivity impacts the complexity of model repair. This yields a complexity landscape ranging from NP through Sigma_2^p up to PSPACE. We also highlight connections between our framework and some well-studied problems, including planning with temporally extended goals (TEGs), model repair using desired and undesired plans, the model reconciliation explanation (MRE) problem, and counterfactual scenarios for automated planning.
Operator-Counting Heuristics for Domain-Independent Dynamic Programming
Domain-independent Dynamic Programming (DIDP) recently emerged as a model-and-solve framework. Its performance heavily depends on a user-specified dual bound function. We introduce a way to automatically derive such bounds generalizing operator-counting heuristics from classical planning. The method approximates by how much an operator can change the value of a feature, which we compute using SAT modulo theories (SMT). We tighten the operator-counting LP using invariants derived by Abstract Interpretation. The dual bounds derived this way match well-known domain-specific dual bounds in TSP and Bin Packing. We evaluate our new dual bounds empirically in an established set of benchmarks.
Computing Planning Width: How Hard Is It, Really?
The width of a classical planning instance, among other metrics, indicates the computational difficulty of solving the instance. However, no result exists on the complexity of computing the width itself. In this paper, we address this open problem by considering two versions: non-parameterised and parameterised. In the non-parameterised version, a positive integer is given as an input along a classical planning instance, and it is PSPACE-complete to decide if this number is an upper bound of the instance's width. In the parameterised version, it is coNP-complete to decide if the width is no greater than the fixed parameter. These tight results resolve the long standing question of how hard it is to compute the width of planning instances, and call for further research on width approximations, generalisation of width computation over multiple instances and islands of tractability.
Compiling Model Reconciliation Explanation Problems into Stackelberg and FOND Planning Problems
Despite its popularity, most model reconciliation explanation generation methods rely on blind breadth-first search, and even available heuristics are rudimentary at best. In this paper, we propose two novel approaches to compile the problem of generating bounded model-reconciliation explanations into existing planning formalisms. First, we compile the problem into a Stackelberg planning problem, which is an adversarial problem consisting of a leader and follower agent. Here, the leader agent is responsible for identifying the explanation, while the follower checks the validity of the identified explanation. In the second approach, we see how the same problem can also be converted into a fully observable non-deterministic (FOND) planning problem. Here, the nondeterministic actions are used to generate and test the possibility of a shorter plan. We show the effectiveness of the proposed approaches by comparing them against each other and two existing baselines on standard planning benchmark problems.
Polynomial-time Configuration Generator for Connected Unlabeled Multi-Agent Pathfinding
We consider Connected Unlabeled Multi-Agent Pathfinding (CUMAPF), a variant of MAPF where interchangeable agents must be connected at all times. This problem is fundamental to swarm robotics applications such as self-reconfiguration and marching, where standard MAPF is insufficient as it does not guarantee the connectivity constraint. Despite its simple structure, CUMAPF remains understudied and lacks practical algorithms. We first develop an Integer Linear Programming (ILP) reduction to solve CUMAPF. Although this formulation provides a makespan-optimal plan, it is severely limited in terms of scalability and real-time responsiveness due to the large number of variables. We therefore propose a suboptimal but complete algorithm named PULL. It is based on a rule-based one-step function that computes a subsequent configuration that preserves connectivity and advances towards the target configuration. PULL is lightweight, and runs in O(n^2) time per step in 2D grid, where n is the number of agents. Empirically, PULL can quickly solve randomly generated instances containing hundreds of agents, which ILP cannot handle. Furthermore, PULL's solution substantially improves upon a naive approach to CUMAPF.
Successor-Generator Planning with LLM-generated Heuristics
Heuristics are a central component of deterministic planning, particularly in domain-independent settings where general applicability is prioritized over task-specific tuning. This work revisits that paradigm in light of recent advances in large language models (LLMs), which enable the automatic synthesis of heuristics directly from problem definitions -- bypassing the need for handcrafted domain knowledge. We present a method that employs LLMs to generate problem-specific heuristic functions from planning tasks specified through successor generators, goal tests, and initial states written in a general-purpose programming language. These heuristics are compiled and integrated into standard heuristic search algorithms, such as greedy best-first search. Our approach achieves competitive, and in many cases state-of-the-art, performance across a broad range of established planning benchmarks. Moreover, it enables the solution of problems that are difficult to express in traditional formalisms, including those with complex numeric constraints or custom transition dynamics. We provide an extensive empirical evaluation that characterizes the strengths and limitations of the approach across diverse planning settings, demonstrating its effectiveness.
Online Learning for Decentralized Multi-Agent Planning in Repeated Hedonic Skill Games
Coalition formation is a fundamental capability in decentralized multi-agent planning, where heterogeneous agents must coordinate to execute tasks whose feasibility depends on complementary capabilities. Hedonic Skill Games (HSGs) offer a compact model for representing such structured interactions, but existing results rely on the unrealistic assumption that agents possess full knowledge of both task requirements and the skills of their coalition members. This assumption breaks down in many planning domains, such as crowdsourced task allocation or distributed multi-robot task execution, where agents must plan under uncertainty and learn from partial observations. We introduce a repeated Hedonic Skill Games in which agents repeatedly form coalitions, execute feasible tasks, and receive only bandit feedback corresponding to their realized utilities. We develop a Upper Confidence Bound (UCB)-driven online learning algorithm that enables decentralized agents to jointly plan coalition choices despite incomplete information, balancing exploration of unknown coalitions with exploitation of realized utilities. We show that the resulting dynamics achieve sublinear Nash regret and converge to ε-approximate Nash equilibria. Experiments on synthetic and real-world problem instances demonstrate convergence behavior, improved social welfare, and the practicality of the approach for large-scale distributed planning scenarios.
Beyond Message Passing: Modern GNN Architectures for Online Planner Selection
As planning is computationally hard, the performance of automated planners varies greatly across planning tasks. Thus, the ability to predict planner performance on a given task is of great importance. While various learning methods have been applied in cost-optimal planning, Graph Neural Networks (GNNs) were found to perform well. However, existing work only explores a limited range of homogeneous GNN architectures and focuses primarily on the model perspective. We address these limitations by approaching the problem from both data and model perspectives. From the data perspective, we analyze the planners' performance data in terms of Shapley values to assess the potential contribution of individual planners to a portfolio. Our insights enable us to effectively reduce the portfolio from 17 to 6 planners, improving practicality and performance. From the model perspective, our work extends previous investigations of homogeneous graphs by modeling planning tasks as heterogeneous graphs and applying the heterogeneous Relational Graph Convolutional Network (RGCN) and Relational Graph Attention Network (RGAT) models. To analyze the problem in more depth, we thoroughly investigate the impact of GNN model, graph representation, node features, and prediction task. Going further, we propose a hybrid approach in which graph representations obtained by GNNs are used as input to a classical machine learning model (XGBoost), resulting in both a more resource-efficient and accurate approach. Our best model (RGCN+XGBoost) achieves 91.7% accuracy, a substantial improvement over previous methods with 87%, while requiring fewer computational resources. Overall, we demonstrate the effectiveness of heterogeneous GNN-based online planner selection methods, opening up new exciting avenues for future research.
On-Demand Mutex Constraints for Numeric Planning as SMT
Planning as Satisfiability Modulo Theories (SMT) has been shown to be a competitive approach for solving numeric planning problems, leveraging the expressiveness of SMT to handle numeric variables and constraints. Being able to set more than one action in a single timestep is crucial for performance, allowing short makespan plans and reducing the number of solver queries required. However, encoding action interference constraints can cause significant formula size blow-up, with mutex constraints dominating the encoding size and limiting scalability. In this paper we present a tool that implements a lazy approach to handling action interference in planning as SMT, exploiting recent developments in solver technology to integrate custom propagators directly into the SMT solver's search process. Rather than eagerly encoding all interference constraints upfront, our propagators add mutex clauses on demand, generating them only when the solver attempts to execute two mutually interfering actions in parallel. Experimental evaluation on standard numeric planning benchmarks demonstrates that our lazy approach is significantly more scalable than the eager approach while maintaining correctness, leading to improved solver performance and making this tool a competitive planner in the numeric setting.
Planning Under Distribution Shifts with Causal POMDPs
In the real world, planning is often challenged by distribution shifts. As such, a model of the environment obtained under one set of conditions may no longer remain valid as the distribution of states or the environment dynamics change, which in turn causes previously learned strategies to fail. In this work, we propose a theoretical framework for planning under partial observability using Partially Observable Markov Decision Processes (POMDPs) formulated using causal knowledge. By representing shifts in the environment as interventions on this causal POMDP, the framework enables evaluating plans under hypothesized changes and actively identifying which components of the environment have been altered. We show how to maintain and update a belief over both the latent state and the underlying domain, and we prove that the value function remains piecewise linear and convex (PWLC) in this augmented belief space. Preservation of PWLC under distribution shifts has the advantage of maintaining the tractability of planning via α-vector-based POMDP methods.
Potential Heuristics as Real-Valued Multilinear Polynomials
Potential functions are a compact and easy way to describe heuristics in classical planning. They are declarative, simple to understand, and clear to explain. Further, they are used as a tool to study the computational complexity of planning problems, and have connections to other concepts such as novelty measures and generalized planning. In this work, we cast potential functions as real-valued multilinear polynomials over the Booleans. While this might seem as a simple syntactic game at first glance, this automatically brings insights to the theory of potential heuristics. For example, we show that the multilinear polynomial representation can be computed using Fourier expansions. This immediately gives us a unique representation for each heuristic function, which allows us to study concepts such as correlation complexity more directly. Moreover, this also connects potential heuristics to a whole new field of computer science -- analysis of Boolean functions -- leading to consequences ranging from learning theory to performance prediction.
Finding Optimal Cost-Bounded Plan Reductions
In some real applications a plan may later become unfeasible due to newly imposed budget constraints, yet, at the same time, using only the original actions of the plan and their order is mandatory. In this paper, we study the problem of extracting, from a precomputed plan, a valid subplan that maximizes utility while respecting a cost bound. Each goal is given a utility value and the plan is reduced by removing actions that support low-utility goals, while preserving both executability and the original action order. We show the decision variant is NP-complete and propose two exact methods to solve it: one via oversubscription planning (OSP) and another via Integer Linear Programming (ILP). Experiments show that ILP outperforms OSP, providing a practical solution in domains where maintaining the structure of the original plan is essential.
LaCAM* Variants for Minimizing Makespan in Multi-Agent Path Finding
Multi-Agent Path Finding (MAPF) requires conflict-free paths. Optimal MAPF solutions often minimize the sum of the costs of the paths (SOC), or their maximum (makespan, MKS). LaCAM* is a recent anytime MAPF solver, eventually converging to the optimal solution. However, LaCAM* was reported to have a considerably slow convergence speed to the optimum. Although this is true for SOC, in this paper, we show that LaCAM* can quickly find optimal MKS solutions. Additionally, currently, due to its anytime nature, LaCAM* uses a branch-and-bound search mechanism. We introduce a version of LaCAM* that uses IDA* and show that it has superb performance for finding optimal MKS solutions, even with thousands of agents. We explain all these phenomena.
Planning in the LLM Era: Building for Reliability and Efficiency
Growing attention to intelligent agents has put a spotlight on one of their central capabilities: planning. Early attempts to leverage large language models (LLMs) for planning relied on single-shot plan generation, followed by hybrid approaches that coupled LLMs with limited external search. These methods, unsound and incomplete by their very nature, often require substantial resources without yielding better solutions on unseen problems. As the limitations of LLMs become clearer, recent work has shifted toward using them at solution construction time – generating symbolic solvers for a family of problems that can be verified and then used efficiently at inference time. This trend reflects the growing need for agents that are both reliable and resource-efficient. It also offers a path towards generating maintainable planners with minimal dependence on language models at inference time. In this paper, we argue that this shift reflects a broader realignment of the planning field in the LLM era. We examine three major categories of planner-generation methods, discuss their current limitations, and outline research steps towards a more reliable and efficient LLM-based generation of planners.
Planner Museum: Evaluating Classical Planners Over Time
The International Planning Competition (IPC) has been running since 1998, providing a standardized platform on which different planners can compete on equal footing. Over the years, the competition has showcased diverse approaches to planning and has thus become a record of the paradigm shifts the field has undergone. In this paper, we introduce the Planner Museum, a project that collects distinguished planning systems from past competitions and enables them to run on modern computers. By evaluating these historical planners alongside modern ones on challenging benchmark problems from multiple IPCs, we quantify the rate of improvement of state-of-the-art planners over the past 28 years and show that progress has been steady.
Compiling Temporal Numeric Planning into Discrete PDDL+
Since the introduction of the PDDL+ modeling language, it was known that temporal planning with durative actions (as in PDDL 2.1) could be compiled into PDDL+. However, no practical compilation was presented in the literature ever since. We present a practical compilation from temporal planning with durative actions into PDDL+, fully capturing the semantics and only assuming the non-self-overlapping of actions. Our compilation is polynomial, retains the plan length up to a constant factor and is experimentally shown to be of practical relevance for hard temporal numeric problems.
Boltzmann-based Exploration for Robust Decentralized Multi-Agent Planning
Decentralized Monte Carlo Tree Search (Dec-MCTS) is widely used for cooperative multi-agent planning but struggles in sparse or skewed reward environments. We introduce Coordinated Boltzmann MCTS (CB-MCTS), which replaces deterministic UCT with a stochastic Boltzmann policy and a decaying entropy bonus for sustained yet focused exploration. While Boltzmann exploration has been studied in single-agent MCTS, applying it in multi-agent systems poses unique challenges. CB-MCTS is the first to address this. We analyze CB‑MCTS in the simple-regret setting and show in simulations that it outperforms Dec-MCTS in deceptive scenarios and remains competitive on standard benchmarks, providing a robust solution for multi-agent planning.
Enhancing Lifelong Multi-Agent Path-finding by Using Artificial Potential Fields
We investigate the use of Artificial Potential Fields (APFs) for improving online multi-agent pathfinding in lifelong settings, where agents are continuously assigned new goals upon completing previous tasks. We present APF-enhanced variants of several online coordination methods, including prioritized planning, large-neighborhood search, and priority-inheritance–based schemes. Our results show that integrating APFs into these algorithms substantially increases overall system throughput, achieving up to a 7-fold improvement in task completion rate in densely populated environments.
Monotonic Variants of Saturated Cost Partitioning
One of the strongest approaches for optimal classical planning is to compute multiple abstractions and combine them admissibly with cost partitioning. Ideally, a cost partitioning algorithm should be monotonic: refining an abstraction and adding it to the set of abstractions should never decrease any heuristic estimates. However, due to its greedy nature, the state-of-the-art saturated cost partitioning (SCP) algorithm lacks this monotonicity property. To address this, we introduce two algorithms that guarantee monotonicity for any cost partitioning scheme. Empirically, we show that the monotonic SCP variant outperforms standard SCP for Cartesian abstractions and pattern databases.
Cartesian Abstraction Refinement for Simple Numeric Planning
Cartesian abstractions are a successful approach for obtaining admissible heuristics in optimal classical planning. In this paper, we generalize the counterexample-guided abstraction refinement (CEGAR) algorithm to compute Cartesian abstractions for simple numeric planning. Specifically, our CEGAR algorithm operates on a special form of Cartesian states consisting of a single interval per numeric variable. We prove that, in the absence of zero-cost actions, this algorithm is semi-complete and optimal. Our experimental evaluation shows that our approach performs similarly to other abstraction heuristics and provides better heuristic estimates in multiple domains.
Algorithms for Deciding the Safety of States in Fully Observable Non-deterministic Problems
Learned action policies are increasingly popular in sequential decision-making, but suffer from a lack of safety guarantees. Recent work introduced a pipeline for testing the safety of such policies under initial-state and action-outcome non-determinism. At the pipeline's core, is the problem of deciding whether a state is safe (a safe policy exists from the state) and finding faults, which are state-action pairs that transition from a safe state to an unsafe one. Their most effective algorithm for deciding safety, TarjanSafe, is effective on their benchmarks, but we show that it has exponential worst-case runtime with respect to the state space. A linear-time alternative exists, but it is slower in practice. We close this gap with a new policy-iteration algorithm iPI, that combines the best of both: it matches TarjanSafe's best-case runtime while guaranteeing a polynomial worst-case. Experiments confirm our theory and show that in problems amenable to TarjanSafe iPI has similar performance, whereas in ill-suited problems iPI scales exponentially better.
Policy Comparison Oracles for Action Policy Testing
Testing is a natural quality assurance technique for learned action policies π. In classical planning, the testing process attempts to find states, called bugs, on which the plan generated by π is sub-optimal. A major challenge in this context is the design of test oracles, sufficient criteria for identifying bugs. Here, we introduce a new type of such oracles, that we call policy comparison oracles (PCOs). These are based on comparing π with a set of policies π' produced during the training process for π. Trivially, on a given state s, if any π' is better than π, then s is a bug in π. But the potential of policy comparison reaches far beyond that. For example, π' may produce a better sub-plan on s even if it does not reach the goal at all. We introduce a combination method that allows to arbitrarily alternate between policies at testing time, thus leveraging their combined potential. We run experiments using ASNets policies. PCOs turn out to be competitive with state-of-the-art test oracles on their own, and their integration with other oracles is superior in our evaluation.
Analyzing Planner Design Trade-Offs for MAPF Under ADG-Based Realistic Execution
Multi-Agent Path Finding (MAPF) algorithms are increasingly deployed in industrial warehouses and automated manufacturing facilities, where robots must operate reliably under real-world physical constraints. However, existing MAPF evaluation frameworks typically rely on simplified robot models, leaving a substantial gap between algorithmic benchmarks and practical performance. Recent frameworks, such as SMART, incorporate kinodynamic modeling and offer the MAPF community a platform for large-scale, realistic evaluation. Building on this capability, this work investigates how key planner design choices influence performance under realistic execution settings. We systematically study three fundamental factors: (1) the relationship between solution optimality and execution performance, (2) the sensitivity of system performance to inaccuracies in kinodynamic modeling, and (3) the interaction between model accuracy and plan optimality. Empirically, we examine these factors to understand how these design choices affect performance in realistic scenarios. We highlight open challenges and research directions to steer the community toward practical, real-world deployment.
How Humans Explain the Difference in the Quality of Plans – A User Study
Recent advances in plan explanation have used abstractions to produce explanations. We consider the task of explaining why there is a difference in the quality of plans produced for a planning problem, Pi, and the same problem constrained in some way, Pi + c. The method involves abstracting away details of the planning problems until the difference in the quality of plans they support is minimised. It is not known whether humans use abstractions to explain these differences, and if so, what types of properties these abstractions have. We present the results of a qualitative user study investigating this. We tasked participants with explaining the difference in the quality of plans and found that users do indeed use abstractions to explain differences. We extract a set of properties that these abstractions satisfy, which can be used in automatic abstraction for explanation generation.
Finding Human-Aligned Abstractions Efficiently for Explaining Plan Quality Differences
Explaining differences in the quality of plans is an effective method for helping users understand the decisions made by planners. When a user suggests an alternative plan that is lower in quality, these explanations can clarify why the original plan was preferable, or reveal hidden preferences and trade-offs in the model. Recent advances in plan explanation have used abstraction to generate explanations for differences in plan quality. Details of planning problems are abstracted until the quality of plans they produce are equal in cost. These abstractions are then used as causal factors in explanations. We build on this work in two ways. Firstly, we introduce a number of pruning techniques based on properties of abstractions, extracted from the human-subject study, to improve the efficiency of finding human-aligned abstractions. Secondly, we formalise and implement a number of heuristics, derived from a human-subject study capturing how people explain differences in plan quality.
Markovian Models Anxious to Stay on the Beaten Path. A Psychology-Grounded Approach to Minimising Exposure to Path Uncertainty
Mainstream formalizations of uncertainty in Automated Planning (AP) research tend to revolve on rational probability management and reward maximization, often overlooking psychological factors involved in how humans handle uncertainty, where reward maximization is often discounted in favor of simpler plans yielding to predictable trajectories for the sake of reducing exposure to anxiety. This paper pushes the research frontier by introducing and formalizing the concept of Path Anxiety, which quantifies how much various policies may expose the AP to uncertainty in regards to future trajectories, which we call Path anxiety-sensitive Markov Decision Processes (PAS-MDP). Then, we propose practical PAS-MDP algorithms for generating optimized policies that balance utilitarian reward maximization with path uncertainty minimization. The practical relevance of PAS-MDPs is empirically validated by showing that PAS-MDP policies optimized by our algorithms can dramatically reduce path uncertainty with minimal reduction in utility as well as reducing the number of possible paths the system may reach by several orders of magnitude. The psychological accuracy of PAS-MDPs is validated by showing that the optimized policies replicate human-like anxiety-sensitive behaviors identified by psychology research. Altogether, this research highlights a promising approach towards developing human-friendly AP systems that account for human anxiety in their decision-making processes, hence addressing one of the most central factor of mental health and wellbeing.
MO-VLA: Preference Adaptation for Vision-Language-Action Models via Multi-Objective Reinforcement Learning
Vision-Language-Action (VLA) models trained with large-scale behavior cloning (BC) have achieved substantial progress in producing diverse and complex robotic behaviors, and have been widely deployed on robot manipulation, human-robot cooperation, and autonomous driving tasks. However, existing VLA models focus on generating complex behaviors from generalized human instructions, at the expense of capturing the critical preference and intent information (regarding speed, smoothness, safety, etc. ) conveyed by users. To address this limitation, we propose MO-VLA, a two-stage framework that integrates Multi-Objective Reinforcement Learning (MORL) into VLA training. Our framework is designed to maintain the performance of the pre-trained model while accelerating convergence when modeling user preferences. It operates in two stages. First, behavioral cloning (BC) on large-scale demonstrations is used for equipping the model with general operational skills. Subsequently, a multi-objective reinforcement learning (MORL) based fine-tuning stage is used for adapting this policy to user-specific preferences. Here, a Feature-wise Linear Modulation (FiLM) mechanism is integrated into the action head to explicitly inject preference signals into the policy generation process. Experimental results on the Meta-World benchmark demonstrate that our method achieves superior multi-objective performance while maintaining a high task success rate. These results validate its capability for preference-aware action generation for various robotic tasks.
Near-Future Traffic Forecasting for Planning-based Traffic Signal Optimisation
Accurately forecasting traffic evolution is critical for effective urban traffic management, especially when integrated with operational control. While high-fidelity traffic simulators offer valuable insights, their high computational cost prevents daily use and real-time deployment. Data-driven approaches offer a computationally feasible alternative, at the cost of lower fidelity. This work bridges this gap by exploring the capabilities of near-future traffic forecasting tailored for traffic signal optimisation. We train data-driven surrogate models to predict the impact of various traffic signal configurations on network traffic over short horizons (up to 360 seconds). Our extensive evaluation on real-world data from a UK urban corridor demonstrates remarkable accuracy for multi-horizon predictions. Further, we demonstrate how these forecasts can be integrated into a planning-based traffic signal optimisation framework. We develop two novel, dedicated heuristics that leverage these predictions to guide the search. Our empirical results on real-world data demonstrate substantial improvements: the forecast-informed heuristics consistently improve solution quality while reducing the number of expanded nodes compared to state-of-the-art domain-independent heuristics.
GPMS: A Generalized Parallel Machine Scheduling Framework with Rich Temporal and Resource Constraints
Classical scheduling problems such as Unrelated Parallel Machine Scheduling (UPMSP), Flexible Job Shop Scheduling (FJSP), and Resource-Constrained Project Scheduling (RCPSP) each capture important aspects of industrial scheduling, but real-world applications often require a combination of constraints from several of these formulations and additional requirements that are typically not supported in standard models. We propose a Generalized Parallel Machine Scheduling (GPMS) framework that, to the best of our knowledge, is the most general machine scheduling formulation to date and unifies machine eligibility with machine-dependent processing times, sequence- and machine-dependent setup times, rich temporal constraints, and secondary-resource constraints in a single formulation. Temporal constraints include machine calendars and precedence relations with min/max time lags and conditional machine eligibilities. Secondary resources are modeled with capacity calendars and pulse or step demands. To model and solve this generalized problem, we investigate a constraint programming approach and propose two CP models: a solver-agnostic high-level model and an interval-variable model. We also develop a randomized construction heuristic that finds feasible, high-quality schedules even for large and highly constrained instances; these schedules serve as warm starts for the interval-variable model. We evaluate 216 generated and 91 real-world instances, comparing the interval-variable model to the solver-agnostic model and to the construction heuristic. The warm-start interval-variable model attains the best objective on the majority of both generated and real-world instances, proves optimality for many small-to-medium instances, and finds near-optimal schedules for most industrial instances within a one-hour time limit. We release all models, code, and instances to support reproducible research.
Rating Composite AI Models for Robustness Through Probabilistic Planning
Many real-world AI systems combine several primitive component models, such as translators and sentiment analyzers, into larger composite models, like chatbots. Understanding how these compositions behave under uncertainty and how properties like bias or instability move through a composite model is increasingly important, yet most evaluation methods still focus on primitive models. We introduce a new use of probabilistic planning to assess the robustness of composite AI models. Each component model call is represented as a stochastic action in the RDDL domain, and the reward combines robustness metrics to the cost of components (actions). The planner runs each primitive model on randomly drawn data batches, allowing robustness to be assessed under variation in both the data and the model outputs induced by those data. We demonstrate via case studies and experiments in multilingual sentiment analysis and a synthetic domain, the planner consistently identifies more stable composite configurations than baseline methods, showing that probabilistic planning can serve as a practical, scalable approach for reasoning about reliability in complex, composite AI models.
Robust Crop Planning Under Uncertainty: Aligning Economic Optimality with Agronomic Sustainability
Long-horizon agricultural planning requires optimizing crop allocation under complex spatial heterogeneity, temporal agronomic dependencies, and multi-source environmental uncertainty. Existing approaches often either address crop interactions, such as legume-cereal complementarity, only implicitly or rely on static deterministic formulations that fail to ensure resilience against market and climate volatility. To address these challenges, we propose a Multi-Layer Robust Crop Planning Framework (MLRCPF) that integrates spatial reasoning, temporal dynamics, and robust optimization. Specifically, we formalize crop-to-crop relationships through a structured interaction matrix embedded within the state-transition logic, and employ a distributionally robust optimization layer to mitigate worst-case risks defined by a data-driven ambiguity set. Evaluations on a real-world high-mix farming dataset from North China demonstrate the effectiveness of the proposed approach. The framework autonomously generates sustainable checkerboard rotation patterns that restore soil fertility, significantly increasing the legume planting ratio compared to deterministic baselines. Economically, it successfully resolves the trade-off between optimality and stability. These results highlight the importance of explicitly encoding domain-specific structural priors into optimization models for resilient decision-making in complex agricultural systems.
Worksworld: A Domain for Integrated Numeric Planning and Scheduling of Distributed Pipelined Workflows
This work pursues automated planning and scheduling of distributed data pipelines, or workflows. We develop a general workflow and resource graph representation that includes both data processing and sharing components with corresponding network interfaces for scheduling. Leveraging these graphs, we introduce WORKSWORLD, a new domain for numeric domain-independent planners designed for permanently scheduled workflows, like ingest pipelines. Our framework permits users to define data sources, available workflow components, and desired data destinations and formats without explicitly declaring the entire workflow graph as a goal. The planner solves a joint planning and scheduling problem, producing a plan that both builds the workflow graph and schedules its components on the resource graph. We empirically show that a state-of-the-art numeric planner running on commodity hardware with one hour of CPU time and 30GB of memory can solve linear-chain workflows of up to 14 components across eight sites.
Automated Planning for Production Routines in Semiconductor Manufacturing
Semiconductor device manufacturing involves complex production routines comprising hundreds of sophisticated steps, e.g., Lithography, Epitaxy, and Ion Implantation. Formulating these routines is challenging, as it requires a deep understanding of the technological interdependencies between operations and the ability to anticipate the consequences of long operation sequences. Currently, this process remains largely manual, with minimal tool support. This paper presents a knowledge-driven architecture for automated manufacturing routine composition, integrating expert knowledge acquisition through ontologies and Knowledge Graphs. The system enables the automatic generation of classical and hierarchical planning domain specifications, which are then used by automated planners for routine construction. Domain experts utilised the developed prototype to effectively model real-world semiconductor manufacturing use cases and compute corresponding routines, thereby demonstrating the practical applicability of the proposed approach.
Deep RL for Fast Long-Horizon Operations Scheduling on NASA’s Carruthers Geocorona Observatory Mission
Spacecraft operations scheduling is a highly constrained, long-horizon combinatorial optimization problem that traditionally relies on heuristics, constraint programming, or manual planning. We present a scalable deep reinforcement learning framework developed and deployed for NASA's Carruthers Geocorona Observatory mission. Our framework introduces a macro-action abstraction known as activity blocks coupled with dynamic action-masking to navigate the intractably large search space and strictly enforce complex power, thermal, and instrument constraints. The resulting architecture generates globally feasible schedules with overwhelming probability, establishes operational trust, and executes a full training cycle in under six hours, circumventing the need for policy robustness by enabling rapid, on-demand retraining. Further, resulting schedules outperform baseline heuristics in scheduled science quality. The deep reinforcement learning framework was deployed as the default operational scheduler for the Carruthers Geocorona Observatory mission from the outset of the mission, demonstrating that deep reinforcement learning can be trusted for real spacecraft operations under complex, evolving constraints.
Learning-Based Peak Temperature Optimization for Multicore Pipelined Hard Real-Time Systems
Thermal-timing coupling in multicore pipelined systems makes it difficult to reduce peak temperature while satisfying hard real-time constraints. Periodic Thermal Management (PTM), which cyclically switches cores into low power mode, offers a deterministic and analyzable control strategy for thermal management in multicore processors. While existing PTM algorithms based on threshold or heuristic methods simplify the problem into a linearly constrained optimization for theoretical analysis, thereby neglecting the inherently nonlinear nature of the original optimization problem. To address this, we propose Autonomous Learning PTM (ALPTM), an online framework based on TD3 that formulates PTM optimization as a continuous-action Markov decision process, explicitly modeling the nonlinear coupled dynamics between thermal evolution, service curves, and queueing behavior. Experiments on representative streaming applications demonstrate that ALPTM consistently preserves hard real-time correctness and achieves significantly lower peak temperatures compared to existing PTM-based methods.
RASO: Role-Aware Shared Reflection for Multi-Agent Orchestration in E-Commerce Long-Horizon Planning
E-commerce platforms increasingly deploy automated product management systems to help sellers maximize long-term profitability through multi-day planning and scheduling of operational actions. Despite promising results in long-horizon e-commerce planning, existing LLM-based multi-agent systems with self-reflection suffer from ambiguous credit assignment across agents and shallow reflection that fails to diagnose root causes of poor operational outcomes in practice. We propose RASO, a novel multi-agent framework that enhances long-term planning via role-aware and rule-based shared reflection. RASO addresses these challenges through a hybrid reward mechanism that combines global and role-specific counterfactual rewards to enable precise credit attribution across functionally distinct agents, as well as a rule-decision paradigm that requires agents to formalize their reasoning into auditable, structured rules prior to action execution to support logic-level error diagnosis during reflection. Evaluated on a real-world e-commerce platform over extended planning horizons, RASO significantly outperforms baselines in cumulative profit with transparent and interpretable decision processes. Our results demonstrate that integrating role-aware collaboration with structured reflection empowers LLM agents to effectively manage complex, long-term business objectives.
Learning Numeric Action Models with Anytime Guarantees
Model-based planning requires a predictive model of the world, yet such models are often hand-crafted. We address this by presenting a framework that learns PDDL action models with numeric fluents automatically from demonstrations. Our approach compactly represents the infinite space of linear numeric preconditions and effects consistent with the data, learning each action modularly and exploiting simpler structures when present. The framework provides anytime soundness and completeness guarantees with respect to the true hidden model, yielding conservative under-approximations and optimistic over-approximations at every stage of learning. Our findings across standard benchmark domains shows the advantages of our framework over existing approaches.
Learning-Guided Simulated Annealing for the Capacitated Vehicle Routing Problem
The Capacitated Vehicle Routing Problem (CVRP) is a fun- damental combinatorial optimization challenge with broad industrial relevance. Classical metaheuristics such as Simu- lated Annealing (SA) offer asymptotic convergence guaran- tees but suffer from inefficient random neighborhood explo- ration. Conversely, recent deep learning approaches generate solutions rapidly but often struggle to generalize beyond the instance sizes encountered during training. In this paper, we bridge this gap by proposing Learning-Guided Simulated An- nealing (LG-SA), a hybrid framework that augments SA with a very lightweight neural module, trained to select promis- ing neighboring solutions instead of relying on uniform sam- pling. The move-selection policy is learned through rein- forcement learning (RL) using Proximal Policy Optimization (PPO). Through extensive experiments, we analyze the sta- bility of the model as well as the impact of diverse feasibil- ity mechanisms, initialization strategies, neighborhood oper- ators, and action parameterizations (joint vs. conditional). We further show that LG-SA excels at finding high-quality solu- tions rapidly. In addition to achieving a 42.5% cost reduction over classical SA and generalizing well to larger instances, LG-SA outperforms or performs on par with widely used methods like OR-Tools, attention-based GNNs, and advanced RL methods within comparable or shorter timeframes.
Beyond Hard Constraints: Budget-Conditioned Reachability for Safe Offline Reinforcement Learning
Sequential decision-making using Markov Decision Process underpins many real-world applications. Both model-based and model-free methods have achieved strong results in these settings. However, real-world tasks must balance reward maximization with safety constraints, often conflicting objectives, that can lead to unstable min–max, adversarial optimization. A promising alternative is safety reachability analysis, which precomputes a forward-invariant safe state–action set, ensuring that an agent starting inside this set remains safe indefinitely. Yet, most reachability-based methods address only hard safety constraints, and little work extends reachability to cumulative cost constraints. To address this, first, we define a safety-conditioned reachability set that decouples reward maximization from cumulative safety cost constraints. Second, we show how this set enforces safety constraints without unstable min–max or Lagrangian optimization, yielding a novel offline safe RL algorithm that learns a safe policy from a fixed dataset without environment interaction. Finally, experiments on standard offline safe-RL benchmarks, and a real-world maritime navigation task demonstrate that our method matches or outperforms state-of-the-art baselines while maintaining safety.
Automating Thought of Search: A Journey Towards Soundness and Completeness
Large language models (LLMs) are being used to solve planning problems that require search. Most of the literature uses LLMs as world models to define the search space, forgoing soundness for the sake of flexibility. A recent work, Thought of Search (ToS), proposed defining the search space with code, having LLMs produce that code. ToS requires a human in the loop, collaboratively producing a sound successor function and goal test. The result, however, is worth the effort: all the tested datasets were solved with 100% accuracy. Consequently, there is great potential to automate the ToS process. We take a first major step towards automating ToS (AutoToS), taking the human out of the loop of interactions with the language model. AutoToS guides the language model step by step towards the generation of sound and complete search components, through feedback from both generic and domain specific unit tests. We show that AutoToS is able to achieve 100% accuracy on all the evaluated domains with a small number of LLM calls.
Self-Improvement for Fast, High-Quality Plan Generation
Generative models trained on synthetic plan data are a promising approach to generalized planning. Recent work has focused on finding any valid plan, rather than a high-quality solution. We address the challenge of producing high-quality plans, a computationally hard problem, in sub-exponential time. First, we demonstrate that, given optimal data, a decoder-only transformer can generate high-quality plans for unseen problem instances. Second, we show how to self-improve an initial model trained on sub-optimal data. Each round of self-improvement combines multiple model calls with graph search to generate improved plans, used for model fine-tuning. An experimental study on four domains: Blocksworld, Logistics, Labyrinth, and Sokoban, shows on average a 30% reduction in plan length over the source symbolic planner, with over 80% of plans being optimal, where the optimum is known. Plan quality is further improved by inference-time search. The model's latency scales sub-exponentially in contrast to the satisficing and optimal symbolic planners to which we compare. Together, these results suggest that self-improvement with generative models offers a scalable approach for high-quality plan generation.
Domain Model Acquisition from Binary Traces
Automated planning systems require symbolic models, but real-world data is often collected in subsymbolic formats such as binary encodings. We address this gap by introducing a domain model acquisition algorithm that handles subsymbolic state representations. Our algorithm takes as input plan trajectories where states are represented as binary vectors rather than fluent sets, along with action signatures, predicate definitions, and type hierarchies. It then simultaneously learns the mapping from binary state representations to symbolic fluents and constructs lifted action models with preconditions and effects. Across all evaluated benchmark domains from the International Planning Competition, our algorithm accurately maps bits to fluents and the learned action models closely match reference representations, despite having access only to binary encodings.
Planning Under Observation Mismatch for Traffic Signal Control via Adaptive Modular World Models
Deploying learned decision-making systems often requires transferring to new sites where the sensing pipeline differs. In such cases, observations can change in semantics and dimensionality even when action primitives and objectives remain comparable. In this work, we study transferable model-based planning under this observation mismatch, which remains challenging for existing learning-based approaches. We propose Adaptive Modularized Model (AMM), a modular planning architecture that separates a domain-specific observation adapter from a shared internal dynamics model defined in a common planning state space. The dynamics model is meta-learned from multiple source domains to enable fast adaptation with limited target interaction. At run time, AMM performs receding-horizon planning by rolling out candidate action sequences under the learned dynamics and selecting actions that optimize a task-specific objective over predicted futures. We instantiate the approach on cross-domain traffic signal control, where actions correspond to signal phases and the planning objective captures congestion. Experiments show that AMM improves both performance and data efficiency compared with existing conventional controllers and learning-based baselines.
Sample-Efficient Hypergradient Estimation for Decentralized Bi-Level Reinforcement Learning
Many strategic decision-making problems, such as environment design for warehouse robots, can be naturally formulated as bi-level reinforcement learning (RL), where a leader agent optimizes its objective while a follower solves a Markov decision process (MDP) conditioned on the leader's decisions. In many situations, a fundamental challenge arises when the leader cannot intervene in the follower's optimization process; it can only observe the optimization outcome. We address this decentralized setting by deriving the hypergradient of the leader’s objective, i.e., the gradient of the leader’s strategy that accounts for changes in the follower’s optimal policy. Unlike prior hypergradient-based methods that require extensive data for repeated state visits or rely on gradient estimators whose complexity can increase substantially with the high-dimensional leader's decision space, we leverage the Boltzmann covariance trick to derive an alternative hypergradient formulation. This enables efficient hypergradient estimation solely from interaction samples, even when the leader's decision space is high-dimensional. Additionally, to our knowledge, this is the first method that enables hypergradient-based optimization for 2-player Markov games in decentralized settings. Experiments highlight the impact of hypergradient updates and demonstrate our method's effectiveness in both discrete and continuous state tasks.
QSIM: Mitigating Overestimation in Multi-Agent Reinforcement Learning via Action Similarity Weighted Q-Learning
Value decomposition (VD) methods have achieved remarkable success in cooperative multi-agent reinforcement learning (MARL). However, their reliance on the max operator for temporal-difference (TD) target calculation leads to systematic Q-value overestimation. This issue is particularly severe in MARL due to the combinatorial explosion of the joint action space, which often results in unstable learning and suboptimal policies. To address this problem, we propose QSIM, a similarity weighted Q-learning framework that reconstructs the TD target using action similarity. Instead of using the greedy joint action directly, QSIM forms a similarity weighted expectation over a structured near-greedy joint action space. This formulation allows the target to integrate Q-values from diverse yet behaviorally related actions while assigning greater influence to those that are more similar to the greedy choice. By smoothing the target with structurally relevant alternatives, QSIM effectively mitigates overestimation and improves learning stability. Extensive experiments demonstrate that QSIM can be seamlessly integrated with various VD methods, consistently yielding superior performance and stability compared to the original algorithms. Furthermore, empirical analysis confirms that QSIM significantly mitigates the systematic value overestimation in MARL.
Learning Distributed Scheduling via LLM-Augmented Reinforcement Learning
The distributed flexible job-shop scheduling problem (DFJSP) aims to coordinate job execution across distributed factories to achieve production goals. Existing reinforcement learning (RL)-based scheduling algorithms have made processes in learning adaptive scheduling polices, but rely on shallow networks and simple handcrafted rewards. These designs limit global state reasoning and accurate credit assignment under sparse rewards, thereby hindering the balanced workload distribution and efficient policy learning. To address these limitations, we propose a Large Language Model (LLM)-augmented RL algorithm tailored for DFJSP by leveraging the contextual reasoning and prior knowledge of LLM. Specifically, we propose an LLM-driven factory assignment mechanism that encodes global factory states and job features into structured queries, enabling context-aware and effective coordination among factories. Furthermore, we design an LLM-informed reward model that encodes scheduling-aware semantics into multi-dimensional proxy rewards for precise credit assignment during training. Theoretical analysis establishes bounds on the reward approximation error and demonstrates that the designed factory assignment can effectively reduce global workload variance. Moreover, extensive experiments on two benchmarks (i.e., Hurink and Brandimarte) and simulation-based DFJSP instances of varying scales demonstrate that our algorithm outperforms state-of-the-art RL algorithms, achieving the average makespan improvement ranging from 0.61% up to 25.78%.
GammaZero: Learning to Guide Belief-Space Search for Long-Horizon POMDPs with Generalizable Graph Representations
We introduce an uncertainty-aware graph representation framework for learning to guide planning in Partially Observable Markov Decision Processes (POMDPs). Unlike existing approaches that require domain or problem size specific neural architectures, GammaZero leverages a unified graph-based belief representation that enables generalization across problem sizes within a domain. Our key insight is that belief states can be systematically transformed into uncertainty-aware graphs where structural patterns learned on small problems transfer to larger instances. We employ a graph neural network with a decoder architecture to learn value functions and policies from expert demonstrations on computationally tractable problems, then apply these learned heuristics to guide Monte Carlo tree search on larger problems. Experimental results on standard POMDP benchmarks demonstrate that GammaZero achieves comparable performance to BetaZero when trained and tested on the same-sized problems, while enabling zero-shot generalization to problems 2-6× larger than those seen during training.
GenePlan: Evolving Better Generalized PDDL Plans Using Large Language Models
We present GenePlan (GENeralized Evolutionary Planner), a novel framework that leverages large language model (LLM) assisted evolutionary algorithms to generate domain-dependent generalized planners for classical planning tasks described in PDDL. By casting generalized planning as an optimization problem, GenePlan iteratively evolves interpretable Python planners that minimize plan length across diverse problem instances. In empirical evaluation across six existing benchmark domains and two new domains, GenePlan achieved an average SAT score of 0.91, closely matching the performance of the state-of-the-art planners (SAT score 0.93), and significantly outperforming other LLM-based baselines such as chain-of-thought prompting (average SAT score 0.64). The generated planners solve new instances rapidly (average 0.49 seconds per task) and at low cost (average $1.82 per domain using GPT-4o).
Improved Generalized Planning with LLMs Through Strategy Refinement and Reflection
LLMs have recently been used to generate Python programs representing generalized plans in PDDL planning, i.e., plans that generalize across the tasks of a given PDDL domain. Previous work proposed a framework consisting of three steps: the LLM first generates a summary and then a strategy for the domain, both in natural language, and then implements that strategy as a Python program, that gets debugged on example planning tasks. In that work, only one strategy is generated and passed directly to the program generation. If the strategy is incorrect, its implementation will therefore result in an incorrect generalized plan. Here, we introduce an approach that generates the strategy in the form of pseudocode and enables automatic debugging of the pseudocode, hence allowing us to identify and fix errors prior to the generation of the generalized plan itself. Additionally, we extend the Python debugging phase with a reflection step prompting the LLM to pinpoint the reason for the observed plan failure. Finally, we take inspiration from LLM code generation to produce several program variants and pick the best one. Running experiments on 17 benchmark domains with two reasoning and two non-reasoning LLMs, we show that these extensions substantially improve the quality of the generalized plans. Our best performing configuration achieves an average coverage of 82% across the domains.
Learning Lifted Action Models from Unsupervised Visual Traces
Efficient construction of models capturing the preconditions and effects of actions is essential for applying AI planning in real-world domains. Extensive prior work has explored learning such models from high-level descriptions of state and/or action sequences. In this paper, we tackle a more challenging setting: learning lifted action models from sequences of state images, without action observation. We propose a deep learning framework that jointly learns state prediction, action prediction, and a lifted action model. We also introduce a mixed-integer linear program (MILP) to prevent prediction collapse and self-reinforcing errors among predictions. The MILP takes the predicted states, actions, and action model over a subset of traces and solves for logically consistent states, actions, and action model that are as close as possible to the original predictions. Pseudo-labels extracted from the MILP solution are then used to guide further training. Experiments across multiple domains show that integrating MILP-based correction helps the model escape local optima and converge toward globally consistent solutions.
On Sample-Efficient Generalized Planning via Learned Transition Models
In this work, we formulate generalized planning as transition-model learning: a neural model approximates the successor-state function and generates plans by rolling out symbolic state trajectories. To achieve size-invariant generalization, we evaluate multiple state representations, including graph embeddings. Our results show that learning explicit transition models yields higher out-of-distribution success than action-sequence prediction in multiple domains, despite significantly fewer training instances and smaller models.
Per-Domain Generalizing Policies: On Learning Efficient and Robust Q-Value Functions
Learning per-domain generalizing policies is a key challenge in learning for planning. Standard approaches learn state-value functions represented as graph neural networks using supervised learning on optimal plans generated by a teacher planner. In this work, we advocate for learning Q-value functions instead. Such policies are drastically cheaper to evaluate for a given state, as they need to process only the current state rather than every successor. Surprisingly, vanilla supervised learning of Q-values performs poorly as it does not learn to distinguish between the actions taken and those not taken by the teacher. We address this by using regularization terms that enforce this distinction, resulting in Q-value policies that consistently outperform state-value policies across a range of 10 domains and are competitive with the planner LAMA-first.
Mitigating Deception and Interference in Online Goal Recognition Systems
Online Goal Recognition (OGR) is the task of recognizing an agent's goal while that agent is executing a plan. Several OGR systems have been designed to observe and analyze an agent's actions in order to infer its goal. However, these systems generally assume that the agent is either unaware of being observed or is cooperating with the system. In this paper, we analyze two scenarios in which this assumption does not hold: Deception, where an agent deliberately hides its true goal by computing a deceptive plan; and Interference, where another agent tampers with the original plan. In particular, we evaluate the performance of the two state-of-the-art systems for OGR (ORL and CLERNet) in these two scenarios, gathering and extending different types of deceptive and interfering attacks. Moreover, we propose a framework (PAC-OGR) that mitigates the effect of the attacks by amending the manipulated plan and reasoning about the agent's behaviour. An experimental evaluation over several classical planning domains shows that PAC-OGR can be effectively integrated into existing OGR systems, making them more robust and reliable.
Revisiting Tree Search for LLMs: Gumbel and Sequential Halving for Budget-Scalable Reasoning
Neural tree search is a powerful decision-making algorithm widely used in complex domains such as game playing and model-based reinforcement learning. Recent work has applied AlphaZero-style tree search to enhance the reasoning capabilities of Large Language Models (LLMs) during inference, but we find that this approach suffers from a scaling failure: on GSM8K and Game24, accuracy drops as the search budget increases. In this paper, we present ReSCALE, an adaptation of Gumbel AlphaZero MCTS that replaces Dirichlet noise and PUCT selection with Gumbel sampling and Sequential Halving, restoring monotonic scaling without changes to the model or its training. ReSCALE reaches 58.4% on GSM8K and 85.3% on Game24 at budgets where the baseline degrades. Ablations confirm that Sequential Halving is the primary driver of the improvement.
Temporally Decoupled Diffusion Planning for Autonomous Driving
Motion planning in dynamic urban environments requires balancing immediate safety with long-term goals. While diffusion models effectively capture multi-modal decision-making, existing approaches treat trajectories as monolithic entities, overlooking heterogeneous temporal dependencies where near-term plans are constrained by instantaneous dynamics and far-term plans by navigational goals. To address this, we propose Temporally Decoupled Diffusion Model (TDDM), which reformulates trajectory generation via a noise-as-mask paradigm. By partitioning trajectories into segments with independent noise levels, we implicitly treat high noise as information voids and weak noise as contextual cues. This compels the model to reconstruct corrupted near-term states by leveraging internal correlations with better-preserved temporal contexts. Architecturally, we introduce a Temporally Decoupled Adaptive Layer Normalization (TD-AdaLN) to inject segment-specific timesteps. During inference, our Asymmetric Temporal Classifier-Free Guidance utilizes weakly noised far-term priors to guide immediate path generation. Evaluations on the nuPlan benchmark show TDDM approaches or exceeds state-of-the-art baselines, particularly excelling in the challenging Test14-hard subset.
Grounding Vision and Language to 3D Masks for Long-Horizon Box Rearrangement
We study long-horizon planning in 3D environments from under-specified natural-language goals using only visual observations, focusing on multi-step 3D box rearrangement tasks. Existing approaches typically rely on symbolic planners with brittle relational grounding of states and goals, or on direct action-sequence generation from 2D vision-language models (VLMs). Both approaches struggle with reasoning over many objects, rich 3D geometry, and implicit semantic constraints. Recent advances in 3D VLMs demonstrate strong grounding of natural-language referents to 3D segmentation masks, suggesting the potential for more general planning capabilities. We extend existing 3D grounding models and propose Reactive Action Mask Planner (RAMP-3D), which formulates long-horizon planning as sequential reactive prediction of paired 3D masks: a "which-object" mask indicating what to pick and a "which-target-region" mask specifying where to place it. The resulting system processes RGB-D observations and natural-language task specifications to reactively generate multi-step pick-and-place actions for 3D box rearrangement. We conduct experiments across 11 task variants in warehouse-style environments with 1-30 boxes and diverse natural-language constraints. RAMP-3D achieves 79.5% success rate on long-horizon rearrangement tasks and significantly outperforms 2D VLM-based baselines, establishing mask-based reactive policies as a promising alternative to symbolic pipelines for long-horizon planning.
db-LaCAM: Fast and Scalable Multi-Robot Kinodynamic Motion Planning with Discontinuity-Bounded Search and Lightweight MAPF
State-of-the-art multi-robot kinodynamic motion planners struggle to handle more than a few robots due to high computational burden, which limits their scalability and results in slow planning time. In this work, we combine the scalability and speed of modern multi-agent path finding (MAPF) algorithms with the dynamic-awareness of kinodynamic planners to address these limitations. To this end, we propose discontinuity-Bounded LaCAM (db-LaCAM), a planner that utilizes a precomputed set of motion primitives that respect robot dynamics to generate horizon-length motion sequences, while allowing a user-defined discontinuity between successive motions. The planner db-LaCAM is resolution-complete with respect to motion primitives and supports arbitrary robot dynamics. Extensive experiments demonstrate that db-LaCAM scales efficiently to scenarios with up to 50 robots, achieving up to ten times faster runtime compared to state-of-the-art planners, while maintaining comparable solution quality. The approach is validated in both 2D and 3D environments with dynamics such as the unicycle and 3D double integrator. We demonstrate the safe execution of trajectories planned with db-LaCAM in two distinct physical experiments involving teams of flying robots and car-with-trailer robots.
DGV: Fusing Dynamic Graphs and Vision-Language Models for Collaborative Dual-Arm Task Planning
Dual-arm collaborative manipulation in dynamic, unstructured environments is profoundly challenging, requiring real-time handling of high-dimensional physical constraints alongside dynamic scene understanding and adaptation to high-level natural language instructions. To address these challenges, we propose the Dynamic Graph Vision-Language Model (DGV), a novel dynamic task planning framework that seamlessly integrates GNNs and VLMs. It first leverages a pre-trained VLM to integrate perceptual and semantic processing, accurately extracting object states and complex manipulation intents from the environment. This extracted information is then encoded into a dynamic spatio-temporal graph that models the robot's kinematic structure, environmental object relations, and temporal dependencies within a single, unified representation. We propose a real-time local subgraph update mechanism, which is designed to cope with rapid environmental changes. This mechanism ensures immediate action adjustments and efficient replanning based on fresh visual feedback, dramatically improving dynamic adaptability. Utilizing the updated graph structure, DGV performs efficient reasoning to generate continuous, stable, and robust dual-arm collaborative motion sequences. Our experimental results across both simulation and real-world robot platforms demonstrate that DGV achieves a task success rate nearly 20% higher than current state-of-the-art methods, while exhibiting superior performance in dynamic adaptability and robustness.
SaferSAC: A Deep Reinforcement Learning Framework for Autonomous Obstacle Avoidance Navigation in UAVs
Autonomous obstacle avoidance and navigation for Unmanned Aerial Vehicles (UAVs) in dynamic environments presents a significant challenge in the field of autonomous planning. Traditional sampling- and optimization-based methods typically rely on global maps and necessitate frequent replanning, rendering them often ill-suited for real-time applications in dynamic scenarios. While deep reinforcement learning (DRL) approaches offer end-to-end perception-decision mapping, they often suffer from limitations in three-dimensional (3D) perception accuracy, sample efficiency, and action space adaptability. To address these challenges, this paper proposes SaferSAC, a DRL framework specifically tailored for robust UAV obstacle avoidance and navigation. First, we design a depth-semantic fusion-based 3D obstacle detection module that achieves precise spatial awareness by jointly processing depth images and semantic segmentation results. Second, we introduce a four-buffer prioritized experience replay mechanism that differentially stores and samples experiences based on distinct categories (e.g., success, obstacle, and precision), thereby significantly enhancing sample efficiency. Finally, we propose an optimization-based adaptive action space planning method. By solving a constrained optimization problem to dynamically adjust action boundaries for velocity and yaw rate, this method enhances the safety and flexibility of local avoidance maneuvers. Experimental results demonstrate that, compared to baseline methods, our approach yields more robust navigation strategies and substantially improves success rates in complex environments.
Interleaving Scheduling and Motion Planning with Incremental Learning of Symbolic Space-Time Motion Abstractions
Task and Motion Planning combines high-level task sequencing (what to do) with low-level motion planning (how to do it) to generate feasible, collision-free execution plans. However, in many real-world domains, such as automated warehouses, tasks are predefined, shifting the challenge to if, when, and how to execute them safely and efficiently under resource, time and motion constraints. In this paper, we formalize this as the Scheduling and Motion Planning problem for multi-object navigation in shared workspaces. We propose a novel solution framework that interleaves off-the-shelf schedulers and motion planners in an incremental learning loop. The scheduler generates candidate plans, while the motion planner checks feasibility and returns symbolic feedback, i.e., spatial conflicts and timing adjustments, to guide the scheduler towards motion-feasible solutions. We validate our proposal on logistics and job-shop scheduling benchmarks augmented with motion tasks, using state-of-the-art schedulers and sampling-based motion planners. Our results show the effectiveness of our framework in generating valid plans under complex temporal and spatial constraints, where synchronized motion is critical.