https://ojs.aaai.org/index.php/SOCS/issue/feed 2023-07-02T00:00:00-07:00 Open Journal Systems <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> https://ojs.aaai.org/index.php/SOCS/article/view/27277 2023-07-01T16:04:42-07:00 Adi Botea abotea@yahoo.com Vadim Bulitko bulitko@ualberta.ca

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.

2023-07-02T00:00:00-07:00 Copyright (c) 2023 Association for the Advancement of Artificial Intelligence https://ojs.aaai.org/index.php/SOCS/article/view/27278 2023-07-01T16:04:43-07:00 Shao-Hung Chan shaohung@usc.edu Roni Stern sternron@post.bgu.ac.il Ariel Felner felner@bgu.ac.il Sven Koenig skoenig@usc.edu

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.

2023-07-02T00:00:00-07:00 Copyright (c) 2023 Association for the Advancement of Artificial Intelligence https://ojs.aaai.org/index.php/SOCS/article/view/27279 2023-07-01T16:04:44-07:00 Tzvika Geft zvigreg@mail.tau.ac.il

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.

2023-07-02T00:00:00-07:00 Copyright (c) 2023 Association for the Advancement of Artificial Intelligence https://ojs.aaai.org/index.php/SOCS/article/view/27280 2023-07-01T16:04:45-07:00 Michael Katz ctpelok@gmail.com Junkyu Lee junkyu.lee@ibm.com Shirin Sohrabi ssohrab@us.ibm.com

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.

2023-07-02T00:00:00-07:00 Copyright (c) 2023 Association for the Advancement of Artificial Intelligence https://ojs.aaai.org/index.php/SOCS/article/view/27281 2023-07-01T16:04:46-07:00 Junkyu Lee junkyu.lee@ibm.com Michael Katz ctpelok@gmail.com Shirin Sohrabi ssohrab@us.ibm.com

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.

2023-07-02T00:00:00-07:00 Copyright (c) 2023 Association for the Advancement of Artificial Intelligence https://ojs.aaai.org/index.php/SOCS/article/view/27282 2023-07-01T16:04:47-07:00 Jonathan Morag moragj@post.bgu.ac.il Roni Stern roni.stern@gmail.com Ariel Felner felner@bgu.ac.il

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.

2023-07-02T00:00:00-07:00 Copyright (c) 2023 Association for the Advancement of Artificial Intelligence https://ojs.aaai.org/index.php/SOCS/article/view/27283 2023-07-01T16:04:49-07:00 Thomas K. Nobes thomas.nobes1@monash.edu Daniel Harabor daniel.harabor@monash.edu Michael Wybrow michael.wybrow@monash.edu Stuart D.C. Walsh stuart.walsh@monash.edu

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.

2023-07-02T00:00:00-07:00 Copyright (c) 2023 Association for the Advancement of Artificial Intelligence https://ojs.aaai.org/index.php/SOCS/article/view/27284 2023-07-01T16:04:50-07:00 Conny Olz conny.olz@uni-ulm.de Pascal Bercher pascal.bercher@anu.edu.au

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.

2023-07-02T00:00:00-07:00 Copyright (c) 2023 Association for the Advancement of Artificial Intelligence https://ojs.aaai.org/index.php/SOCS/article/view/27285 2023-07-01T16:04:51-07:00 Takumi Shimoda takumi35shimoda@yahoo.co.jp Alex Fukunaga fukunaga@idea.c.u-tokyo.ac.jp

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.

2023-07-02T00:00:00-07:00 Copyright (c) 2023 Association for the Advancement of Artificial Intelligence https://ojs.aaai.org/index.php/SOCS/article/view/27286 2023-07-01T16:04:53-07:00 Václav Sobotka sobotka@mail.muni.cz Hana Rudová hanka@fi.muni.cz

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.

2023-07-02T00:00:00-07:00 Copyright (c) 2023 Association for the Advancement of Artificial Intelligence https://ojs.aaai.org/index.php/SOCS/article/view/27287 2023-07-01T16:04:56-07:00 David Vainshtein dudiwa@campus.technion.ac.il Yaakov Sherma yaakovsherma@campus.technion.ac.il Kiril Solovey kirilsol@technion.ac.il Oren Salzman osalzman@cs.technion.ac.il

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.

2023-07-02T00:00:00-07:00 Copyright (c) 2023 Association for the Advancement of Artificial Intelligence https://ojs.aaai.org/index.php/SOCS/article/view/27288 2023-07-01T16:04:58-07:00 Han Zhang zhan645@usc.edu Oren Salzman osalzman@cs.technion.ac.il Ariel Felner felner@bgu.ac.il T. K. Satish Kumar tkskwork@gmail.com Shawn Skyler shawn@post.bgu.ac.il Carlos Hernández Ulloa carlos.hernandez@uss.cl Sven Koenig skoenig@usc.edu

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.

2023-07-02T00:00:00-07:00 Copyright (c) 2023 Association for the Advancement of Artificial Intelligence https://ojs.aaai.org/index.php/SOCS/article/view/27289 2023-07-01T16:04:59-07:00 Xiaodi Zhang xiaodi.zhang@anu.edu.au Alban Grastien alban.grastien@anu.edu.au

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.

2023-07-02T00:00:00-07:00 Copyright (c) 2023 Association for the Advancement of Artificial Intelligence https://ojs.aaai.org/index.php/SOCS/article/view/27290 2023-07-01T16:05:00-07:00 Yue Zhang yue.zhang@monash.edu Daniel Harabor daniel.harabor@monash.edu Pierre Le Bodic pierre.lebodic@monash.edu Peter J. Stuckey pstuckey@unimelb.edu.au

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.

2023-07-02T00:00:00-07:00 Copyright (c) 2023 Association for the Advancement of Artificial Intelligence https://ojs.aaai.org/index.php/SOCS/article/view/27291 2023-07-01T16:05:01-07:00 Shizhe Zhao eggeek.me@gmail.com Daniel Harabor daniel.harabor@monash.edu Peter J. Stuckey pstuckey@unimelb.edu.au

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.

2023-07-02T00:00:00-07:00 Copyright (c) 2023 Association for the Advancement of Artificial Intelligence https://ojs.aaai.org/index.php/SOCS/article/view/27292 2023-07-01T16:05:05-07:00 Marco Dalla m.dalla@cs.ucc.ie Benjamin Provan-Bessell b.provanbessell@cs.ucc.ie Andrea Visentin andrea.visentin@ucc.ie Barry O'Sullivan b.osullivan@cs.ucc.ie

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.

2023-07-02T00:00:00-07:00 Copyright (c) 2023 Association for the Advancement of Artificial Intelligence https://ojs.aaai.org/index.php/SOCS/article/view/27293 2023-07-01T16:05:06-07:00 Michael Katz ctpelok@gmail.com Junkyu Lee junkyu.lee@ibm.com

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.

2023-07-02T00:00:00-07:00 Copyright (c) 2023 Association for the Advancement of Artificial Intelligence https://ojs.aaai.org/index.php/SOCS/article/view/27294 2023-07-01T16:05:07-07:00 Chao Lei chao68681@gmail.com Nir Lipovetzky nir.lipovetzky@unimelb.edu.au Krista A. Ehinger kehinger@unimelb.edu.au

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.

2023-07-02T00:00:00-07:00 Copyright (c) 2023 Association for the Advancement of Artificial Intelligence https://ojs.aaai.org/index.php/SOCS/article/view/27295 2023-07-01T16:05:08-07:00 Shohin Mukherjee shohin.93@gmail.com Maxim Likhachev maxim@cs.cmu.edu

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

2023-07-02T00:00:00-07:00 Copyright (c) 2023 Association for the Advancement of Artificial Intelligence https://ojs.aaai.org/index.php/SOCS/article/view/27296 2023-07-01T16:05:09-07:00 Lior Siag siagl@post.bgu.ac.il Shahaf Shperberg s.shperberg@gmail.com Ariel Felner felner@bgu.ac.il Nathan R. Sturtevant nathanst@ualberta.ca

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.

2023-07-02T00:00:00-07:00 Copyright (c) 2023 Association for the Advancement of Artificial Intelligence https://ojs.aaai.org/index.php/SOCS/article/view/27297 2023-07-01T16:05:10-07:00 Hanlan Yang hanlany@andrew.cmu.edu Shohin Mukherjee shohin.93@gmail.com Maxim Likhachev maxim@cs.cmu.edu

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

2023-07-02T00:00:00-07:00 Copyright (c) 2023 Association for the Advancement of Artificial Intelligence https://ojs.aaai.org/index.php/SOCS/article/view/27298 2023-07-01T16:05:14-07:00 Ruth Helen Bergin 119401946@umail.ucc.ie Marco Dalla m.dalla@cs.ucc.ie Andrea Visentin andrea.visentin@ucc.ie Barry O'Sullivan b.osullivan@cs.ucc.ie Gregory Provan g.provan@cs.ucc.ie

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.

2023-07-02T00:00:00-07:00 Copyright (c) 2023 Association for the Advancement of Artificial Intelligence https://ojs.aaai.org/index.php/SOCS/article/view/27299 2023-07-01T16:05:15-07:00 Marc-Emmanuel Coupvent des Graviers mecdg0@gmail.com Kevin Osanlou k.osanlou@gmail.com Christophe Guettier guettier_c@yahoo.fr Tristan Cazenave cazenave@lamsade.dauphine.fr

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.

2023-07-02T00:00:00-07:00 Copyright (c) 2023 Association for the Advancement of Artificial Intelligence https://ojs.aaai.org/index.php/SOCS/article/view/27300 2023-07-01T16:05:16-07:00 Andrii Nyporko nyporand@fel.cvut.cz Lukáš Chrpa chrpaluk@cvut.cz

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.

2023-07-02T00:00:00-07:00 Copyright (c) 2023 Association for the Advancement of Artificial Intelligence https://ojs.aaai.org/index.php/SOCS/article/view/27301 2023-07-01T16:05:17-07:00 Adittyo Paul adittyop@andrew.cmu.edu Ying Feng yingfeng@andrew.cmu.edu Jiaoyang Li jiaoyangli@cmu.edu

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.

2023-07-02T00:00:00-07:00 Copyright (c) 2023 Association for the Advancement of Artificial Intelligence https://ojs.aaai.org/index.php/SOCS/article/view/27302 2023-07-01T16:05:19-07:00 Francesco Percassi f.percassi@hud.ac.uk Enrico Scala enricos83@gmail.com Mauro Vallati m.vallati@hud.ac.uk

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.

2023-07-02T00:00:00-07:00 Copyright (c) 2023 Association for the Advancement of Artificial Intelligence https://ojs.aaai.org/index.php/SOCS/article/view/27303 2023-07-01T16:05:20-07:00 Wiktor Piotrowski wiktorpi@parc.com Yoni Sher yoni.sandsher@gmail.com Sachin Grover sachin.grover@asu.edu Roni Stern roni.stern@gmail.com Shiwali Mohan mohan@parc.com

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.

2023-07-02T00:00:00-07:00 Copyright (c) 2023 Association for the Advancement of Artificial Intelligence https://ojs.aaai.org/index.php/SOCS/article/view/27304 2023-07-01T16:05:21-07:00 Zhongqiang Ren ren.zhongqiang@outlook.com Sivakumar Rathinam srathinam@tamu.edu Howie Choset choset@andrew.cmu.edu

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.

2023-07-02T00:00:00-07:00 Copyright (c) 2023 Association for the Advancement of Artificial Intelligence https://ojs.aaai.org/index.php/SOCS/article/view/27305 2023-07-01T16:05:22-07:00 Shawn Skyler shawn@post.bgu.ac.il Shahaf Shperberg s.shperberg@gmail.com Dor Atzmon dorat@post.bgu.ac.il Ariel Felner felner@bgu.ac.il Oren Salzman osalzman@cs.technion.ac.il Shao-Hung Chan shaohung@usc.edu Han Zhang zhan645@usc.edu Sven Koenig skoenig@usc.edu William Yeoh wyeoh@wustl.edu Carlos Hernández Ulloa carlos.hernandez@uss.cl

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.

2023-07-02T00:00:00-07:00 Copyright (c) 2023 Association for the Advancement of Artificial Intelligence https://ojs.aaai.org/index.php/SOCS/article/view/27306 2023-07-01T16:05:24-07:00 Jiří Švancara svancara@ktiml.mff.cuni.cz Etienne Tignon tignon@uni-potsdam.de Roman Barták bartak@ktiml.mff.cuni.cz Torsten Schaub torsten@cs.uni-potsdam.de Philipp Wanko wanko@uni-potsdam.de Roland Kaminski kaminski@cs.uni-potsdam.de

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.

2023-07-02T00:00:00-07:00 Copyright (c) 2023 Association for the Advancement of Artificial Intelligence https://ojs.aaai.org/index.php/SOCS/article/view/27307 2023-07-01T16:05:25-07:00 Oleg Tarakanov light_ln2@hotmail.com

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.

2023-07-02T00:00:00-07:00 Copyright (c) 2023 Association for the Advancement of Artificial Intelligence https://ojs.aaai.org/index.php/SOCS/article/view/27308 2023-07-01T16:05:28-07:00 David Fairbairn david.fairbairn@tharsus.co.uk

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.

2023-07-02T00:00:00-07:00 Copyright (c) 2023 Association for the Advancement of Artificial Intelligence https://ojs.aaai.org/index.php/SOCS/article/view/27309 2023-07-01T16:05:29-07:00 Ryo Kuroiwa ryo.kuroiwa@mail.utoronto.ca

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.

2023-07-02T00:00:00-07:00 Copyright (c) 2023 Association for the Advancement of Artificial Intelligence https://ojs.aaai.org/index.php/SOCS/article/view/27310 2023-07-01T16:05:30-07:00 Thomas K. Nobes thomas.nobes1@monash.edu

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.

2023-07-02T00:00:00-07:00 Copyright (c) 2023 Association for the Advancement of Artificial Intelligence https://ojs.aaai.org/index.php/SOCS/article/view/27311 2023-07-01T16:05:31-07:00 Alison Paredes 20asp3@queensu.ca

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.

2023-07-02T00:00:00-07:00 Copyright (c) 2023 Association for the Advancement of Artificial Intelligence https://ojs.aaai.org/index.php/SOCS/article/view/27312 2023-07-01T16:05:32-07:00 Enrico Saccon enrico.saccon@unitn.it

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.

2023-07-02T00:00:00-07:00 Copyright (c) 2023 Association for the Advancement of Artificial Intelligence https://ojs.aaai.org/index.php/SOCS/article/view/27313 2023-07-01T16:05:33-07:00 Bojie Shen bojie.shen@monash.edu

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.

2023-07-02T00:00:00-07:00 Copyright (c) 2023 Association for the Advancement of Artificial Intelligence https://ojs.aaai.org/index.php/SOCS/article/view/27314 2023-07-01T16:05:34-07:00 Václav Sobotka sobotka@mail.muni.cz

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.

2023-07-02T00:00:00-07:00 Copyright (c) 2023 Association for the Advancement of Artificial Intelligence https://ojs.aaai.org/index.php/SOCS/article/view/27315 2023-07-01T16:05:35-07:00 Devin Wild Thomas devin.thomas@unh.edu

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.

2023-07-02T00:00:00-07:00 Copyright (c) 2023 Association for the Advancement of Artificial Intelligence https://ojs.aaai.org/index.php/SOCS/article/view/27316 2023-07-01T16:05:36-07:00 Eyal Weiss eyal.weiss@biu.ac.il

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.

2023-07-02T00:00:00-07:00 Copyright (c) 2023 Association for the Advancement of Artificial Intelligence https://ojs.aaai.org/index.php/SOCS/article/view/27317 2023-07-01T16:05:37-07:00 Stephen Wissow sjw@cs.unh.edu

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.

2023-07-02T00:00:00-07:00 Copyright (c) 2023 Association for the Advancement of Artificial Intelligence https://ojs.aaai.org/index.php/SOCS/article/view/27318 2023-07-01T16:05:40-07:00 Roman Barták bartak@ktiml.mff.cuni.cz Wheeler Ruml ruml@cs.unh.edu Oren Salzman osalzman@cs.technion.ac.il

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.

2023-07-02T00:00:00-07:00 Copyright (c) 2023 Association for the Advancement of Artificial Intelligence