https://ojs.aaai.org/index.php/SOCS <p>Heuristic search and combinatorial optimization are currently very active areas of research. For example, researchers investigate how to search in real-time, how to search with limited (possibly external) memory, how to solve sequences of similar search problems faster than with isolated searches, how to improve the runtime of the searches over time, how to trade-off between the runtime and memory consumption of the search and the resulting solution quality, and how to focus the searches with sophisticated heuristics such as pattern databases. Their results are published in different conferences such as IJCAI, AAAI, ICAPS, NIPS, ICRA, and IROS. The International Symposium on Combinatorial Search (SoCS) is meant to bring these researchers together to exchange their ideas and cross-fertilize the field. Thus, in addition to seeking separate answers to questions like how to design more accurate memory-based heuristics, more I/O-efficient disk-based search algorithms, or more efficient clause-learning strategies, the symposium will stimulate thoughts on combining various techniques originated from different areas of search.</p> AAAI Press en-US Proceedings of the International Symposium on Combinatorial Search 2832-9171 https://ojs.aaai.org/index.php/SOCS/article/view/27277 In constraint optimization many problem instances remain challenging to current technology. We focus on the Romanian Crosswords Competition Problem. It is a challenging, NP-hard constraint optimization problem where state-of-the-art AI has been lagging significantly behind top human performance. We present an approach that first builds a core, a portion of the problem that will have a high contribution to the objective function. A core is grown into a seed, a partial solution with a subset of variables defined and instantiated. Seeds are further extended into full solutions. Our approach takes as input the size of a rectangular core to consider, and the locations of zero or more black cells inside the core. The results advance state-of-the-art substantially. We report a boost in the scores obtained, bringing our top solutions in the vicinity of top human entries. Adi Botea Vadim Bulitko Copyright (c) 2023 Association for the Advancement of Artificial Intelligence 2023-07-02 2023-07-02 16 1 2 10 10.1609/socs.v16i1.27277 https://ojs.aaai.org/index.php/SOCS/article/view/27278 Multi-Agent Path Finding (MAPF) is the problem of finding collision-free paths, one for each agent, in a shared environment, while minimizing their sum of travel times. Since solving MAPF optimally is NP-hard, researchers have explored algorithms that solve MAPF suboptimally but efficiently. Priority-Based Search (PBS) is the leading algorithm for this purpose. It finds paths for individual agents, one at a time, and resolves collisions by assigning priorities to the colliding agents and replanning their paths during its search. However, PBS becomes ineffective for MAPF instances with high densities of agents and obstacles. Therefore, we introduce Greedy PBS (GPBS), which uses greedy strategies to speed up PBS by minimizing the number of collisions between agents. We then propose techniques that speed up GPBS further, namely partial expansions, target reasoning, induced constraints, and soft restarts. We show that GPBS with all these improvements has a higher success rate than the state-of-the-art suboptimal algorithm for a 1-minute runtime limit, especially for MAPF instances with small maps and dense obstacles. Shao-Hung Chan Roni Stern Ariel Felner Sven Koenig Copyright (c) 2023 Association for the Advancement of Artificial Intelligence 2023-07-02 2023-07-02 16 1 11 19 10.1609/socs.v16i1.27278 https://ojs.aaai.org/index.php/SOCS/article/view/27279 Multi-Agent Path Finding (MAPF) is a fundamental motion coordination problem arising in multi-agent systems with a wide range of applications. The problem's intractability has led to extensive research on improving the scalability of solvers for it. Since optimal solvers can struggle to scale, a major challenge that arises is understanding what makes MAPF hard. We tackle this challenge through a fine-grained complexity analysis of time-optimal MAPF on 2D grids, thereby closing two gaps and identifying a new tractability frontier. First, we show that 2-colored MAPF, i.e., where the agents are divided into two teams, each with its own set of targets, remains NP-hard. Second, for the flowtime objective (also called sum-of-costs), we show that it remains NP-hard to find a solution in which agents have an individually optimal cost, which we call an individually optimal solution. The previously tightest results for these MAPF variants are for (non-grid) planar graphs. We use a single hardness construction that replaces, strengthens, and unifies previous proofs. We believe that it is also simpler than previous proofs for the planar case as it employs minimal gadgets that enable its full visualization in one figure. Finally, for the flowtime objective, we establish a tractability frontier based on the number of directions agents can move in. Namely, we complement our hardness result, which holds for three directions, with an efficient algorithm for finding an individually optimal solution if only two directions are allowed. This result sheds new light on the structure of optimal solutions, which may help guide algorithm design for the general problem. Tzvika Geft Copyright (c) 2023 Association for the Advancement of Artificial Intelligence 2023-07-02 2023-07-02 16 1 20 28 10.1609/socs.v16i1.27279 https://ojs.aaai.org/index.php/SOCS/article/view/27280 Recent advances in data-driven approaches in AI planning demand more and more planning tasks. The supply, however, is somewhat limited. Past International Planning Competitions (IPCs) have introduced the de-facto standard benchmarks with the domains written by domain experts. The few existing methods for sampling random planning tasks severely limit the resulting problem structure. In this work we show a method for generating planning tasks of any requested causal graph structure, alleviating the shortage in existing planning benchmarks. We present an algorithm for constructing random SAS+ planning tasks given an arbitrary causal graph and offer random task generators for the well-explored causal graph structures in the planning literature. We further allow to generate a planning task equivalent in causal structure to an input SAS+ planning task. We generate two benchmark sets: 26 collections for select well-explored causal graph structures and 42 collections for existing IPC domains. We evaluate both benchmark sets with the state-of-the-art optimal planners, showing the adequacy for adopting them as benchmarks in cost-optimal classical planning. The benchmark sets and the task generator code are publicly available at https://github.com/IBM/fdr-generator. Michael Katz Junkyu Lee Shirin Sohrabi Copyright (c) 2023 Association for the Advancement of Artificial Intelligence 2023-07-02 2023-07-02 16 1 29 37 10.1609/socs.v16i1.27280 https://ojs.aaai.org/index.php/SOCS/article/view/27281 Finding multiple high-quality plans is essential in many planning applications, and top-k planning asks for finding the k best plans, naturally extending cost-optimal classical planning. Several attempts have been made to formulate top-k classical planning as a k-shortest paths finding problem and apply K* search, which alternates between A* and Eppstein's algorithm. However, earlier work had shortcomings, among which were failing to handle inconsistent heuristics and degraded performance in Eppstein's algorithm implementations. As a result, existing evaluation results severely underrate the performance of the K* based approach to top-k planning. In this paper, we present a new top-k planner based on a novel variant of K* search. We address the following three aspects. First, we show an alternative implementation of Eppstein's algorithm for classical planning, which resolves a major bottleneck in earlier attempts. Second, we present a new strategy for alternating A* and Eppstein's algorithm, that improves the performance of K* on the classical planning benchmarks. Last, we introduce a simple mitigation of the limitation of K* to tasks with a single goal state, allowing us to preserve heuristic informativeness in face of imposed task reformulation. Empirical evaluation results show that the proposed approach achieves the state-of-the-art performance on the classical planning benchmarks. The code is available at https://github.com/IBM/kstar. Junkyu Lee Michael Katz Shirin Sohrabi Copyright (c) 2023 Association for the Advancement of Artificial Intelligence 2023-07-02 2023-07-02 16 1 38 46 10.1609/socs.v16i1.27281 https://ojs.aaai.org/index.php/SOCS/article/view/27282 Multi-Agent Path Finding (MAPF) is the problem of finding collision-free paths for multiple agents operating in the same environment. In Lifelong MAPF (LMAPF), these agents continuously receive new destinations, and the task is to constantly update their paths while optimizing for a high throughput over time. Therefore, many MAPF sub-problems must be solved over time in order to solve a single LMAPF problem. LMAPF problems manifest in real-world applications, such as automated warehouses, where strict responsiveness requirements limit the amount of time allocated to planning. MAPF algorithms occasionally fail to produce a plan within the allotted time. We propose a system design for LMAPF that is robust to such planning failures. Then, we explore different approaches to avoid planning failures, reduce their severity, and handle them when they occur. In particular, we describe and analyze different Fail Policies that are applied when planning failures occur and ensure collisions and unnecessary degradation of throughput are avoided. To our knowledge, while such Fail Policies are used in practice in the industry, they have yet to be researched academically. Jonathan Morag Roni Stern Ariel Felner Copyright (c) 2023 Association for the Advancement of Artificial Intelligence 2023-07-02 2023-07-02 16 1 47 55 10.1609/socs.v16i1.27282 https://ojs.aaai.org/index.php/SOCS/article/view/27283 Voxel grids are an increasingly common enabler for pathfinding in 3D spaces. Currently in this area there exists only a limited number of publicly available benchmarks. This makes it difficult to establish state-of-the-art performance and to compare the strengths and weaknesses of competing search techniques. In this work, we introduce three new and diverse sets of voxel benchmarks intended to help fill this gap. We further describe our methodology for generating and selecting a representative set of pathfinding queries. Our dataset comprises 46 distinct voxel maps and 92,000 problem instances. The data is drawn from distinct application domains: computer video games, industrial plant layouts and sandstone porosity scans. Featuring distinctive geometric properties and a variety of challenging query types, these new datasets allow practitioners to evaluate algorithmic performance across a variety of settings encountered when pathfinding in practice. Thomas K. Nobes Daniel Harabor Michael Wybrow Stuart D.C. Walsh Copyright (c) 2023 Association for the Advancement of Artificial Intelligence 2023-07-02 2023-07-02 16 1 56 64 10.1609/socs.v16i1.27283 https://ojs.aaai.org/index.php/SOCS/article/view/27284 In HTN planning the choice of decomposition methods used to refine compound tasks is key to finding a valid plan. Based on inferred preconditions and effects of compound tasks, we propose a look-ahead technique for search-based total-order HTN planning that can identify inevitable refinement choices and in some cases dead-ends. The former occurs when all but one decomposition method for some task are proven infeasible for turning a task network into a solution, whereas the latter occurs when all methods are proven infeasible. We show how it can be used for pruning, as well as to strengthen heuristics and to reduce the search branching factor. An empirical evaluation proves its potential as incorporating it improves an existing HTN planner such that it is the currently best performing one in terms of coverage and IPC score. Conny Olz Pascal Bercher Copyright (c) 2023 Association for the Advancement of Artificial Intelligence 2023-07-02 2023-07-02 16 1 65 73 10.1609/socs.v16i1.27284 https://ojs.aaai.org/index.php/SOCS/article/view/27285 While parallelization of A* is fairly well-understood, parallelization of GBFS has been much less understood. Recent work has proposed PUHF, a parallel GBFS which restricts search to exploration of the Bench Transition System (BTS), which is the set of states that can be expanded by GBFS under some tie-breaking policy. However, PUHF causes threads to spend much of the time waiting so that only states which are guaranteed to be in the BTS are expanded. We propose improvements to PUHF which significantly reduce idle time and allow more rapid exploration of the BTS, resulting in better search performance. Takumi Shimoda Alex Fukunaga Copyright (c) 2023 Association for the Advancement of Artificial Intelligence 2023-07-02 2023-07-02 16 1 74 82 10.1609/socs.v16i1.27285 https://ojs.aaai.org/index.php/SOCS/article/view/27286 The pickup and delivery problem with transfers generalizes the classical pickup and delivery problem (PDP) by allowing the vehicles to exchange request loads at designated transfer points. Transfers often lead to substantial reductions in transportation costs, yet they come with a significant burden of additional computational complexity. Even meta-heuristic methods are thus limited to instances of at most lower hundreds of requests leaving the desirable benefits unreachable for larger instances. Our approach bypasses the complexities inherent to current methods by deciding about the transfers apriori and thus reducing the problem to a PDP instance. To make as informed decisions as possible, we analyze a broader set of characteristics that may be used to carry out the apriori decisions. We opt to derive and examine multiple such PDP instances to cover different transfer choices. Our analysis of the derived PDP instances then allows their efficient processing in parallel. The proposed framework addresses a large-scale freight transportation problem with real-world characteristics and transfers where typical instances count over 1,200 requests and 300 vehicles. We show the potential of the proposed framework on both real-world and synthetic instances with up to 1,500 requests. The experiments demonstrate that substantial savings may be achieved within favorable runtimes even for very large instances. Václav Sobotka Hana Rudová Copyright (c) 2023 Association for the Advancement of Artificial Intelligence 2023-07-02 2023-07-02 16 1 83 91 10.1609/socs.v16i1.27286 https://ojs.aaai.org/index.php/SOCS/article/view/27287 In automated warehouses, teams of mobile robots fulfill the packaging process by transferring inventory pods to designated workstations while navigating narrow aisles formed by tightly packed pods. This problem is typically modeled as a Multi-Agent Pickup and Delivery (MAPD) problem, which is then solved by repeatedly planning collision-free paths for agents on a fixed graph, as in the Rolling-Horizon Collision Resolution (RHCR) algorithm. However, existing approaches make the limiting assumption that agents are only allowed to move pods that correspond to their current task, while considering the other pods as stationary obstacles (even though all pods are movable). This behavior can result in unnecessarily long paths which could otherwise be avoided by opening additional corridors via pod manipulation. To this end, we explore the implications of allowing agents the flexibility of dynamically relocating pods. We call this new challenging problem Terraforming MAPD (tMAPD) and develop an RHCR-based approach to tackle it. As the extra flexibility of terraforming comes at a significant computational cost, we utilize this capability judiciously by identifying situations where it could make a significant impact on the solution quality. In particular, we invoke terraforming in response to disruptions that often occur in automated warehouses, e.g., when an item is dropped from a pod or when agents malfunction. Empirically, using our approach for tMAPD, where disruptions are modeled via a stochastic process, we improve throughput by over 10%, reduce the maximum service time (the difference between the drop-off time and the pickup time of a pod) by more than 50%, without drastically increasing the runtime, compared to the MAPD setting. David Vainshtein Yaakov Sherma Kiril Solovey Oren Salzman Copyright (c) 2023 Association for the Advancement of Artificial Intelligence 2023-07-02 2023-07-02 16 1 92 100 10.1609/socs.v16i1.27287 https://ojs.aaai.org/index.php/SOCS/article/view/27288 In bi-objective graph search, each edge is annotated with a cost pair, where each cost corresponds to an objective to optimize. We are interested in finding all undominated paths from a given start state to a given goal state (called the Pareto front). Almost all existing works of bi-objective search use single-valued heuristics, which use one number for each objective, to estimate the cost between any given state and the goal state. However, single-valued heuristics cannot reflect the trade-offs between the two costs. On the other hand, multi-valued heuristics use a set of pairs to estimate the Pareto front between any given state and the goal state and are more informed than single-valued heuristics. However, they are rarely studied and have yet to be investigated in explicit state spaces by any existing work. In this paper, we are interested in using multi-valued heuristics to improve bi-objective search algorithms in explicit state spaces. More specifically, we generalize Differential Heuristics (DHs), a class of memory-based heuristics for single-objective search, to bi-objective search, resulting in Bi-objective Differential Heuristics (BO-DHs). We propose several techniques to reduce the memory usage and computational overhead of BO-DHs significantly. Our experimental results show that, with suggested improvement and tuned parameters, BO-DHs can reduce the node expansion and runtime of a bi-objective search algorithm by up to an order of magnitude, paving the way for more effective multi-valued heuristics. Han Zhang Oren Salzman Ariel Felner T. K. Satish Kumar Shawn Skyler Carlos Hernández Ulloa Sven Koenig Copyright (c) 2023 Association for the Advancement of Artificial Intelligence 2023-07-02 2023-07-02 16 1 101 109 10.1609/socs.v16i1.27288 https://ojs.aaai.org/index.php/SOCS/article/view/27289 This paper introduces three improvements to the conformant planner CPCES, which continuously searches candidate plans and counter-examples against the current candidate plan until a valid plan (no counter-example exists) is found. First, we identify and merge equivalent PDDL facts to accelerate candidate plan generation. Second, we warm-start CPCES by generating multiple carefully selected counter-examples at the beginning of the procedure, which reduces the number of calls to the classical planner. Third, we investigate the use Fast Downward (FD) as the candidate plan generator; in particular, we propose an incremental procedure to generate the SAS+ file used by FD. Our experimental results show significant improvements for each technique. Xiaodi Zhang Alban Grastien Copyright (c) 2023 Association for the Advancement of Artificial Intelligence 2023-07-02 2023-07-02 16 1 110 118 10.1609/socs.v16i1.27289 https://ojs.aaai.org/index.php/SOCS/article/view/27290 Current approaches for real-world Multi-Agent Path Finding (MAPF) usually start with a simplified MAPF model and modify the resulting plans so they are kinematically feasible. We investigate one such problem, called MAPF with turn actions MAPF_T, and show that ignoring the kinematic constraints significantly increases solution cost. A first modification of the popular Conflict-Based Search algorithm to MAPF_T yields significantly better plans but comes at the cost of substantial decreases in scalability. We then introduce several techniques that can improve the performance of CBS for MAPF_T, including stronger and generalised versions of existing symmetry-breaking constraints and a novel pruning technique that eliminates redundant branches in the CBS constraint tree. Experimental results on six popular MAPF domains show convincing improvements for CBS success rate and substantial reductions in node expansions and runtime. Yue Zhang Daniel Harabor Pierre Le Bodic Peter J. Stuckey Copyright (c) 2023 Association for the Advancement of Artificial Intelligence 2023-07-02 2023-07-02 16 1 119 127 10.1609/socs.v16i1.27290 https://ojs.aaai.org/index.php/SOCS/article/view/27291 JPS (Jump Point Search) is a state-of-the-art optimal algorithm for online grid-based pathfinding. Widely used in games and other navigation scenarios, JPS nevertheless can exhibit pathological behaviours which are not well studied: (i) it may repeatedly scan the same area of the map to find successors; (ii) it may generate and expand suboptimal search nodes. In this work, we examine the source of these pathological behaviours, show how they can occur in practice, and propose a purely online approach, called Constrained JPS (CJPS), to tackle them efficiently. Experimental results show that CJPS has low overheads and is often faster than JPS in dynamically changing grid environments: by up to 7x in large game maps and up to 14x in pathological scenarios. Shizhe Zhao Daniel Harabor Peter J. Stuckey Copyright (c) 2023 Association for the Advancement of Artificial Intelligence 2023-07-02 2023-07-02 16 1 128 136 10.1609/socs.v16i1.27291 https://ojs.aaai.org/index.php/SOCS/article/view/27292 The extraction of meaningful features from CNF instances is crucial to applying machine learning to SAT solving, enabling algorithm selection and configuration for solver portfolios and satisfiability classification. While many approaches have been proposed for feature extraction, their relevance to these tasks is unclear. Their applicability and comparison of the information extracted and the computational effort needed are complicated by the lack of working or updated implementations, negatively affecting reproducibility. In this paper, we analyse the performance of five sets of features presented in the literature on SAT/UNSAT and problem category classification over a dataset of 3000 instances across ten problem classes distributed equally between SAT and UNSAT. To increase reproducibility and encourage research in this area, we released a Python library containing an updated and clear implementation of structural, graph-based, statistical and probing features presented in the literature for SAT CNF instances; and we define a clear pipeline to compare feature sets in a given learning task robustly. We analysed which of the computed features are relevant for the specific task and the tradeoff they provide between accuracy and computational effort. The results of the analysis provide insights into which features mostly affect an instance's satisfiability and which can be used to identify the problem's type. These insights can be used to develop more effective solver portfolios and satisfiability classification algorithms. Marco Dalla Benjamin Provan-Bessell Andrea Visentin Barry O'Sullivan Copyright (c) 2023 Association for the Advancement of Artificial Intelligence 2023-07-02 2023-07-02 16 1 138 142 10.1609/socs.v16i1.27292 https://ojs.aaai.org/index.php/SOCS/article/view/27293 Partial order reduction techniques are successfully used for various settings in planning, such as classical planning with A* search or with decoupled search, fully-observable non-deterministic planning with LAO*, planning with resources, or even goal recognition design. Here, we continue this trend and show that partial order reduction can be used for top-quality planning with K* search. We discuss the possible pitfalls of using stubborn sets for top-quality planning and the guarantees provided. We perform an empirical evaluation that shows the proposed approach to significantly improve over the current state of the art in unordered top-quality planning. The code is available at https://github.com/IBM/kstar. Michael Katz Junkyu Lee Copyright (c) 2023 Association for the Advancement of Artificial Intelligence 2023-07-02 2023-07-02 16 1 143 147 10.1609/socs.v16i1.27293 https://ojs.aaai.org/index.php/SOCS/article/view/27294 It has been shown recently that successful techniques in classical planning, such as goal-oriented heuristics and landmarks, can improve the ability to compute planning programs for generalized planning (GP) problems. In this work, we introduce the notion of action novelty rank, which computes novelty with respect to a planning program, and propose novelty-based generalized planning solvers, which prune a newly generated planning program if its most frequent action repetition is greater than a given bound v, implemented by novelty-based best-first search BFS(v) and its progressive variant PGP(v). Besides, we introduce lifted helpful actions in GP derived from action schemes, and propose new evaluation functions and structural program restrictions to scale up the search. Our experiments show that the new algorithms BFS(v) and PGP(v) outperform the state-of-the-art in GP over the standard generalized planning benchmarks. Practical findings on the above-mentioned methods in generalized planning are briefly discussed. Chao Lei Nir Lipovetzky Krista A. Ehinger Copyright (c) 2023 Association for the Advancement of Artificial Intelligence 2023-07-02 2023-07-02 16 1 148 152 10.1609/socs.v16i1.27294 https://ojs.aaai.org/index.php/SOCS/article/view/27295 Parallel search algorithms have been shown to improve planning speed by harnessing the multithreading capability of modern processors. One such algorithm PA*SE achieves this by parallelizing state expansions, whereas another algorithm ePA*SE achieves this by effectively parallelizing edge evaluations. ePA*SE targets domains in which the action space comprises actions with expensive but similar evaluation times. However, in a number of robotics domains, the action space is heterogenous in the computational effort required to evaluate the cost of an action and its outcome. Motivated by this, we introduce GePA*SE: Generalized Edge-based Parallel A* for Slow Evaluations, which generalizes the key ideas of PA*SE and ePA*SE, i.e., parallelization of state expansions and edge evaluations, respectively. This extends its applicability to domains that have actions requiring varying computational effort to evaluate them. The open-source code for GePA*SE, along with the baselines, is available here: https://github.com/shohinm/parallel_search Shohin Mukherjee Maxim Likhachev Copyright (c) 2023 Association for the Advancement of Artificial Intelligence 2023-07-02 2023-07-02 16 1 153 157 10.1609/socs.v16i1.27295 https://ojs.aaai.org/index.php/SOCS/article/view/27296 Most recent theoretical and algorithmic work in bidirectional heuristic search (BiHS) used front-to-end (F2E) heuristics that estimate the distance to the start and goal states. In this paper, we start exploring front-to-front (F2F) heuristics, which estimate the distance between any pair of states. Devising efficient algorithms that use F2F heuristics is a challenging task. Thus, it is important to first understand the benefits of using F2F heuristics compared to F2E heuristics. To this end, we theoretically and experimentally demonstrate that there is a great potential in using F2F heuristics implying that F2F BiHS is a promising area of future research. Lior Siag Shahaf Shperberg Ariel Felner Nathan R. Sturtevant Copyright (c) 2023 Association for the Advancement of Artificial Intelligence 2023-07-02 2023-07-02 16 1 158 162 10.1609/socs.v16i1.27296 https://ojs.aaai.org/index.php/SOCS/article/view/27297 Anytime search algorithms are useful for planning problems where a solution is desired under a limited time budget. Anytime algorithms first aim to provide a feasible solution quickly and then attempt to improve it until the time budget expires. On the other hand, parallel search algorithms utilize the multithreading capability of modern processors to speed up the search. One such algorithm, ePA*SE (Edge-Based Parallel A* for Slow Evaluations), parallelizes edge evaluations to achieve faster planning and is especially useful in domains with expensive-to-compute edges. In this work, we propose an extension that brings the anytime property to ePA*SE, resulting in A-ePA*SE. We evaluate A-ePA*SE experimentally and show that it is significantly more efficient than other anytime search methods. The open-source code for A-ePA*SE, along with the baselines, is available here: https://github.com/shohinm/parallel_search Hanlan Yang Shohin Mukherjee Maxim Likhachev Copyright (c) 2023 Association for the Advancement of Artificial Intelligence 2023-07-02 2023-07-02 16 1 163 167 10.1609/socs.v16i1.27297 https://ojs.aaai.org/index.php/SOCS/article/view/27298 The Boolean Satisfiability Problem (SAT) can be framed as a binary classification task. Recently, numerous machine and deep learning techniques have been successfully deployed to predict whether a CNF has a solution. However, these approaches do not provide a variables assignment when the instance is satisfiable and have not been used as part of SAT solvers. In this work, we investigate the possibility of using a machine-learning SAT/UNSAT classifier to assign a truth value to a variable. A heuristic solver can be created by iteratively assigning one variable to the value that leads to higher predicted satisfiability. We test our approach with and without probing features and compare it to a heuristic assignment based on the variable's purity. We consider as objective the maximisation of the number of literals fixed before making the CNF unsatisfiable. The preliminary results show that this iterative procedure can consistently fix variables without compromising the formula's satisfiability, finding a complete assignment in almost all test instances. Ruth Helen Bergin Marco Dalla Andrea Visentin Barry O'Sullivan Gregory Provan Copyright (c) 2023 Association for the Advancement of Artificial Intelligence 2023-07-02 2023-07-02 16 1 169 170 10.1609/socs.v16i1.27298 https://ojs.aaai.org/index.php/SOCS/article/view/27299 Route planning for autonomous vehicles is a challenging task, especially in dense road networks with multiple delivery points. Additional external constraints can quickly add overhead to this already-difficult problem that often requires prompt, on-the-fly decisions. This work introduces a hybrid method combining machine learning and Constraint Programming (CP) to improve search performance. A new message passing-based graph neural network tailored to constraint solving and global search is defined. Once trained, a single neural network inference is enough to guide CP search while ensuring solution optimality. Large-scale experiments using real road networks from cities worldwide are presented. The hybrid method is effective in solving complex routing problems, addressing larger problems than those used for model training. Marc-Emmanuel Coupvent des Graviers Kevin Osanlou Christophe Guettier Tristan Cazenave Copyright (c) 2023 Association for the Advancement of Artificial Intelligence 2023-07-02 2023-07-02 16 1 171 172 10.1609/socs.v16i1.27299 https://ojs.aaai.org/index.php/SOCS/article/view/27300 In a nutshell, Automated Planning deals with finding sequences of actions that achieve a required goal while scheduling deals with allocating activities on (limited) resources meeting specified constraints. Activities, however, might resemble actions in planning as we might capture what they can produce and under what conditions. That said, the "planning'' part represents selecting proper activities as well as their ordering which the "scheduling'' part represents allocating the activities to the resources. This extended abstract formalises the concept of "combined" planning and scheduling tasks and proposes the idea how these tasks can be compiled to classical planning tasks. Our idea is evaluated on tasks involving scheduling activities on reconfigurable machines. Andrii Nyporko Lukáš Chrpa Copyright (c) 2023 Association for the Advancement of Artificial Intelligence 2023-07-02 2023-07-02 16 1 173 174 10.1609/socs.v16i1.27300 https://ojs.aaai.org/index.php/SOCS/article/view/27301 One area of research in Multi-Agent Path Finding (MAPF) is to determine how re-planning can be efficiently achieved in the case of the delay of an agent. One option is to determine a new wait ordering to find the most optimal new solution that can be produced by re-ordering the wait order. We propose to use an Edge-Switchable Temporal Plan Graph and an augmented A* algorithm, called Switchable-Edge Search, to approach finding a new optimal wait order. While this is a work in progress still, we have discovered several optimizations for this algorithm, and the results show promising increases in efficiency for the algorithm. We have analyzed our present efficiency in a variety of conditions by measuring re-planning speed in different maps, with varying numbers of agents and randomized scenarios for agents' start and goal locations. We hope that, as we proceed with optimization, we can show that such an approach can be more efficient in practice and be used instead of re-running MAPF to perform cheap re-planning in such situations. Adittyo Paul Ying Feng Jiaoyang Li Copyright (c) 2023 Association for the Advancement of Artificial Intelligence 2023-07-02 2023-07-02 16 1 175 176 10.1609/socs.v16i1.27301 https://ojs.aaai.org/index.php/SOCS/article/view/27302 PDDL+ is an expressive formalism that allows for the use of planning in hybrid discrete-continuous domains. To cope with unexpected situations, it is crucial for deployed planning-based systems to efficiently repair existing plans. In this paper, we revisit a recently proposed FIXABILITY framework for expressing and solving problems from validation to rescheduling of actions in PDDL+ plans. Francesco Percassi Enrico Scala Mauro Vallati Copyright (c) 2023 Association for the Advancement of Artificial Intelligence 2023-07-02 2023-07-02 16 1 177 178 10.1609/socs.v16i1.27302 https://ojs.aaai.org/index.php/SOCS/article/view/27303 Angry Birds is a very popular game that requires reasoning about sequential actions in a continuous world with discrete exogenous events. Different versions of the game are hard computationally, and the reigning world champion is still a human despite a long-running yearly competition in IJCAI conferences. In this work, we present the Hydra, the first successful game-playing agent for Angry Birds that uses a domain-independent planner and combinatorial search techniques. Hydra models the game using PDDL+, a rich planning language designed for mixed discrete/continuous domains. To reason about continuous aspects of the domain, Hydra employs time discretization techniques that raise a combinatorial search challenge. To meet this challenge, we propose domain-specific heuristics and a novel "preferred states" mechanism similar to the preferred operators mechanism from classical planning. We compared Hydra with state-of-the-art Angry Birds agents. The results show Hydra can solve a greater diversity of Angry Birds levels compared to other agents and highlight its current limitations. Wiktor Piotrowski Yoni Sher Sachin Grover Roni Stern Shiwali Mohan Copyright (c) 2023 Association for the Advancement of Artificial Intelligence 2023-07-02 2023-07-02 16 1 179 180 10.1609/socs.v16i1.27303 https://ojs.aaai.org/index.php/SOCS/article/view/27304 Multi-Agent Path Finding (MA-PF) finds collision-free paths for multiple agents from their respective start to goal locations. This paper investigates a generalization of MA-PF called Multi-Agent Teamwise Cooperative Path Finding (MA-TC-PF), where agents are grouped as multiple teams and each team has its own objective to minimize. In general, there is more than one team, and MA-TC-PF is thus a multi-objective planning problem with the goal of finding the entire Pareto-optimal front that represents all possible trade-offs among the objectives of the teams. We show that the existing CBS and M* for MA-PF can be modified to solve MA-TC-PF, which is verified with tests. We discuss the conditions under which the proposed algorithms are complete and are guaranteed to find the Pareto-optimal front for MA-TC-PF. Zhongqiang Ren Sivakumar Rathinam Howie Choset Copyright (c) 2023 Association for the Advancement of Artificial Intelligence 2023-07-02 2023-07-02 16 1 181 182 10.1609/socs.v16i1.27304 https://ojs.aaai.org/index.php/SOCS/article/view/27305 This extended abstract presents a theoretical analysis of node expansions in Multi-Objective Search. We define three categories of nodes, Must-Expand Nodes, Maybe-Expand Nodes, and Never-Expand Nodes. Our analysis establishes that regardless of the Ordering Function or Multi-Objective Search algorithm used, any Multi-Objective Search algorithm must expand all Must-Expand Nodes, some or none of Maybe-Expand Nodes, and none of Never-Expand Nodes. In addition, we conduct experimental evaluations of various Ordering Functions, revealing that they all expand the same number of nodes and compare their efficiency at finding solutions at various stages of the search. Shawn Skyler Shahaf Shperberg Dor Atzmon Ariel Felner Oren Salzman Shao-Hung Chan Han Zhang Sven Koenig William Yeoh Carlos Hernández Ulloa Copyright (c) 2023 Association for the Advancement of Artificial Intelligence 2023-07-02 2023-07-02 16 1 183 184 10.1609/socs.v16i1.27305 https://ojs.aaai.org/index.php/SOCS/article/view/27306 Multi-agent pathfinding is the task of navigating a set of agents in a shared environment without collisions. Finding an optimal plan is a computationally hard problem, therefore, one may want to sacrifice optimality for faster computation time. In this paper, we present our preliminary work on finding a valid solution using only a predefined path for each agent with the possibility of adding wait actions. This restriction makes some instances unsolvable, however, we show instances where this approach is guaranteed to find a solution. Jiří Švancara Etienne Tignon Roman Barták Torsten Schaub Philipp Wanko Roland Kaminski Copyright (c) 2023 Association for the Advancement of Artificial Intelligence 2023-07-02 2023-07-02 16 1 185 186 10.1609/socs.v16i1.27306 https://ojs.aaai.org/index.php/SOCS/article/view/27307 The 4x4 and 8x2 Sliding Tile Puzzles have more than ten trillion solvable states, making complete brute force search very challenging: best existing solutions take weeks to run or require expensive hardware. We propose and implement a set of optimizations of the frontier search algorithm, that are efficient on a modern personal computer. We run a number of complete searches, each taking about 3 days on our hardware, for both puzzles in single-tile and multi-tile metrics, verifying previously known results about the radiuses of the puzzles. We also discover that the diameter of the 4x4 Puzzle is 80 single-tile moves, and the radius of the 8x2 Puzzle is 57 multi-tile moves. Oleg Tarakanov Copyright (c) 2023 Association for the Advancement of Artificial Intelligence 2023-07-02 2023-07-02 16 1 187 188 10.1609/socs.v16i1.27307 https://ojs.aaai.org/index.php/SOCS/article/view/27308 I specialise in conducting research in Multi-Agent Path- Finding (MAPF) and Algorithmic Graph Theory. Specifically, I investigate the impact of geometric constraints on a given instance of MAPF, as well as the expansion of MAPF to include resource constraints, target assignment path-finding (TAPF), and academic problems that are relevant to industry. In Algorithmic Graph Theory, I extend the capabilities of standard and novel MAPF solvers to temporal graphs, and explore clustering techniques and ideas that utilise Graph Classifications on MAPF domains to reduce the computational complexity of MAPF. Furthermore, I research the implementation and application of massively parallelized computing techniques on MAPF, especially in relation to distance matrix computation and parallelized centralized MAPF. Aside from my PhD research, I have the pleasure to collaborate with researchers at Tharsus Limited, to directly apply my research to novel industrial problems and develop benchmarks that are relevant to both the industry and the research community for Multi-Agent Systems. David Fairbairn Copyright (c) 2023 Association for the Advancement of Artificial Intelligence 2023-07-02 2023-07-02 16 1 190 191 10.1609/socs.v16i1.27308 https://ojs.aaai.org/index.php/SOCS/article/view/27309 In my dissertation, I will propose Domain-Independent Dynamic Programming (DIDP), a novel model-based paradigm for combinatorial optimization (CO) based on dynamic programming (DP). In DIDP, a problem is first formulated as a declarative DP model and then solved by a general-purpose solver. The goal of my dissertation is to develop an algorithm-independent modeling formalism to define a DP model and general-purpose solvers for it and demonstrate that DIDP is promising for CO in practice. In particular, I will propose a modeling formalism based on a state transition system and heuristic search solvers for it. Ryo Kuroiwa Copyright (c) 2023 Association for the Advancement of Artificial Intelligence 2023-07-02 2023-07-02 16 1 192 193 10.1609/socs.v16i1.27309 https://ojs.aaai.org/index.php/SOCS/article/view/27310 Pipe-routing in 3D is a common problem in the design of industrial plant layouts. Here, we aim to minimise the structural cost of the plant (which can have multi-billion dollar budgets), while maintaining safety and engineering constraints. We tackle this problem by developing efficient methods for optimal 3D search. We contribute an adaption of Jump Point Search, a well-known symmetry-breaking technique for 2D grids, into 3D: in contrast to related work, our algorithm preserves path feasibility. In combination with a novel method for limiting over-scanning, we report search time speedups of up to an order of magnitude on benchmarks in the literature. We further develop three new and varied voxel benchmark data sets sourced from 3D applications in the literature in order to provide better opportunities for differentiating competing techniques. Towards pipe-routing, this work also identifies several remaining issues for translating the size of industrial domains and their associated constraints into 3D search. Thomas K. Nobes Copyright (c) 2023 Association for the Advancement of Artificial Intelligence 2023-07-02 2023-07-02 16 1 194 195 10.1609/socs.v16i1.27310 https://ojs.aaai.org/index.php/SOCS/article/view/27311 In this line of work, we consider the possibility that some fast heuristic search methods introduce structural bias, which can cause problems similar to sampling-bias for downstream statistical learning methods. We seek to understand the source of this kind of bias and to develop efficient alternatives. Here we present some preliminary results in developing a variation of canonical A* that can overcome the structural bias introduced by first-in-first-out duplicate detection, which we observed under the condition of variable heuristic error. These results inspire a model of greedy-best-first-search for this problem in the satisficing setting. We hope to apply our approach in a novel planning application--activity selection for agent-based modeling for epidemiology--where planning technology should avoid introducing structural bias if possible. Alison Paredes Copyright (c) 2023 Association for the Advancement of Artificial Intelligence 2023-07-02 2023-07-02 16 1 196 197 10.1609/socs.v16i1.27311 https://ojs.aaai.org/index.php/SOCS/article/view/27312 Automation in industries is becoming an ever-increasing necessity, especially in the sector of logistics. In many cases, this means having many different automated guided vehicles (AGVs) moving at the same time, hence needing coordination to avoid conflicts between different agents. The problem of organizing a fleet of autonomous robots is known as the Multi-Agent Path Finding (MAPF) problem in the literature for which several optimal and sub-optimal algorithms have been proposed. When faced with real-life scenarios, these algorithms must provide the best feasible solution in the shortest time possible, therefore they must scale for large scenarios and be efficient. In this work, we briefly describe our open-source framework we are working on and we lay down the research paths we are going to focus on. The goal is to develop a holistic system that allows to control different aspects of the MAPF problem, from graph topology to goal scheduling. Enrico Saccon Copyright (c) 2023 Association for the Advancement of Artificial Intelligence 2023-07-02 2023-07-02 16 1 198 199 10.1609/socs.v16i1.27312 https://ojs.aaai.org/index.php/SOCS/article/view/27313 In this work, we introduce a set of methodological and visualisation tools to track progress and state-of-the-art performance in the area of Multi-Agent Path Finding (MAPF). Our objectives are to lower the barriers of entry for new researchers and to further promote the study of MAPF. Bojie Shen Copyright (c) 2023 Association for the Advancement of Artificial Intelligence 2023-07-02 2023-07-02 16 1 200 201 10.1609/socs.v16i1.27313 https://ojs.aaai.org/index.php/SOCS/article/view/27314 Interest in vehicle routing problems (VRP) with stochastic and dynamic elements has grown in the past decade. Despite numerous contributions in this area, the handling of uncertainties and dynamic changes in complex VRPs received little attention. Based on our experience from industrial practice, we discuss why accounting for uncertainties and dynamic changes is crucial for the applicability of the produced routing plans. Then, we first identify and justify the best-suited direction to address dynamicity and uncertainties in real-world VRPs. Second, we outline the key concepts and ideas of our approach to finally demonstrate that it is realistic to implement them efficiently. Václav Sobotka Copyright (c) 2023 Association for the Advancement of Artificial Intelligence 2023-07-02 2023-07-02 16 1 202 203 10.1609/socs.v16i1.27314 https://ojs.aaai.org/index.php/SOCS/article/view/27315 Traditionally when planning we assume that we receive the problem instance as input, then formulate a plan, then the clock begins and the agent executes the plan. Sometimes we are forced to consider time passing as we plan, which is known as situated planning. In my dissertation I explore situated planning in three domains: grid based path planning, the orienteering problem and opportunistic science. Devin Wild Thomas Copyright (c) 2023 Association for the Advancement of Artificial Intelligence 2023-07-02 2023-07-02 16 1 204 205 10.1609/socs.v16i1.27315 https://ojs.aaai.org/index.php/SOCS/article/view/27316 The shortest path problem in graphs is a cornerstone of AI theory and applications. Existing algorithms generally ignore edge weight computation time. In this paper we present a generalized framework for weighted directed graphs, where edge weight can be computed (estimated) multiple times, at increasing accuracy and run-time expense. This raises a generalized shortest path problem that optimizes different aspects of path cost and its uncertainty. We describe in high-level a complete anytime algorithm for the generalized problem and discuss possible future extensions. Eyal Weiss Copyright (c) 2023 Association for the Advancement of Artificial Intelligence 2023-07-02 2023-07-02 16 1 206 207 10.1609/socs.v16i1.27316 https://ojs.aaai.org/index.php/SOCS/article/view/27317 Distributional heuristic search uses distributions rather than point values for its heuristic estimates of the cost to goal from any given state. Distributional heuristics are desirable as they provide search algorithms not only with a way to evaluate nodes, but also with a basis for rational decision making tailored to specific search settings. Bounded suboptimal, anytime, and contract searches have differing but related objectives that each lend themselves to probabilistic reasoning supported by distributional heuristics. In many applications, speed of planning can be more important than solution quality. Whether due to certain domains' inherent difficulty, where anything but a satisficing approach is infeasible due to time or memory constraints, or due to the limited planning time available in real-time robotics and other time-sensitive planning settings, important open questions are how best to find solutions as quickly as possible and how to find the best solution possible while subject to an explicit limit on planning time. Successful algorithms must reason not only about solution cost, possibly in relation to a suboptimality bound, but also about the relative likelihood of finding a solution under one node vs. under another, of finding a solution of a particular cost (such as in relation to that of an incumbent solution), or about the expected amount of search effort to find a goal under a given node. This dissertation takes up these issues in four parts. I (1) examine different methods for generating distributional heuristics in bounded cost heuristic search and classical planning; (2) study the contract search setting, which involves online estimation of several unknown values; (3) consider the bounded suboptimal setting; and (4) address the anytime setting. Stephen Wissow Copyright (c) 2023 Association for the Advancement of Artificial Intelligence 2023-07-02 2023-07-02 16 1 208 209 10.1609/socs.v16i1.27317 https://ojs.aaai.org/index.php/SOCS/article/view/27318 The Sixteenth International Symposium on Combinatorial Search (SoCS) was held at the Aquapalace Hotel Prague, Czech Republic, at July 14–16, 2023, just after the International Conference on Automated Planning and Scheduling (ICAPS). The symposium co-chairs were Roman Barták, Wheeler Ruml, and Oren Salzman. Roman Barták Wheeler Ruml Oren Salzman Copyright (c) 2023 Association for the Advancement of Artificial Intelligence 2023-07-02 2023-07-02 16 1 10.1609/socs.v16i1.27318