Pub Date : 2023-07-01DOI: 10.1609/icaps.v33i1.27247
Uwe Köckemann, D. Calisi, Guglielmo Gemignani, Jennifer Renoux, A. Saffiotti
Behavior Trees (BTs) are a formalism increasingly used to control the execution of robotic systems. The strength of BTs resides in their compact, hierarchical and transparent representation. However, when used in practical applications transparency is often hindered by the introduction of implicit run-time relations between nodes, e.g., because of data dependencies or hardware-related ordering constraints. Manually verifying the correctness of a BT with respect to these hidden relations is a tedious and error-prone task. This paper presents a modular planning-based approach for automatically testing BTs offline at design time, to identify possible executions that may violate given data and ordering constraints and to exhibit traces of these executions to help debugging. Our approach supports both basic and advanced BT node types, e.g., supporting parallel behaviors, and can be extended with other node types as needed. We evaluate our approach on BTs used in a commercially deployed robotics system and on a large set of randomly generated trees showing that our approach scales to realistic sizes of more than 3000 nodes.
{"title":"Planning for Automated Testing of Implicit Constraints in Behavior Trees","authors":"Uwe Köckemann, D. Calisi, Guglielmo Gemignani, Jennifer Renoux, A. Saffiotti","doi":"10.1609/icaps.v33i1.27247","DOIUrl":"https://doi.org/10.1609/icaps.v33i1.27247","url":null,"abstract":"Behavior Trees (BTs) are a formalism increasingly used to control the execution of robotic systems. The strength of BTs resides in their compact, hierarchical and transparent representation. However, when used in practical applications transparency is often hindered by the introduction of implicit run-time relations between nodes, e.g., because of data dependencies or hardware-related ordering constraints. Manually verifying the correctness of a BT with respect to these hidden relations is a tedious and error-prone task. This paper presents a modular planning-based approach for automatically testing BTs offline at design time, to identify possible executions that may violate given data and ordering constraints and to exhibit traces of these executions to help debugging. Our approach supports both basic and advanced BT node types, e.g., supporting parallel behaviors, and can be extended with other node types as needed. We evaluate our approach on BTs used in a commercially deployed robotics system and on a large set of randomly generated trees showing that our approach scales to realistic sizes of more than 3000 nodes.","PeriodicalId":239898,"journal":{"name":"International Conference on Automated Planning and Scheduling","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132614243","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-01DOI: 10.1609/icaps.v33i1.27244
F. Teichteil-Königsbuch, G. Povéda, Guillermo González de Garibay Barba, Tim Luchterhand, S. Thiébaux
Resource-Constrained Project Scheduling Problems (RCPSPs) are NP-complete, which makes it challenging to efficiently solve large instances and robustify solutions in the presence of uncertainty. To remedy this, we learn to efficiently mimic the solutions produced by Constraint Programming (CP) solver, using a Graph Neural Network (GNN) architecture designed to capture the structure of RCPSPs. Since the GNN solution may violate constraints, we ensure schedule feasibility at inference time by extracting the task ordering from the GNN schedule and post-processing it with the well-known Schedule Generation Scheme (SGS). We find that SIREN, the resulting algorithm, produces schedules that are of higher quality than those produced by the CP solver within the same computation time budget. The speed and solution quality of SIREN make it suitable as a component of an on-line scenario-based optimisation procedure for RCPSPs with stochastic durations. This leads to the SERENE system, which robustly selects, in real-time, the best next tasks to start in order to minimise the average makespan over the scenarios. Empirically, SERENE achieves better average makespan over different realisations of uncertainty than deterministic algorithms that continuously reschedule on the basis of either the worst, best or average task durations.
{"title":"Fast and Robust Resource-Constrained Scheduling with Graph Neural Networks","authors":"F. Teichteil-Königsbuch, G. Povéda, Guillermo González de Garibay Barba, Tim Luchterhand, S. Thiébaux","doi":"10.1609/icaps.v33i1.27244","DOIUrl":"https://doi.org/10.1609/icaps.v33i1.27244","url":null,"abstract":"Resource-Constrained Project Scheduling Problems (RCPSPs) are NP-complete, which makes it challenging to efficiently solve large instances and robustify solutions in the presence of uncertainty. To remedy this, we learn to efficiently mimic the solutions produced by Constraint Programming (CP) solver, using a Graph Neural Network (GNN) architecture designed to capture the structure of RCPSPs. Since the GNN solution may violate constraints, we ensure schedule feasibility at inference time by extracting the task ordering from the GNN schedule and post-processing it with the well-known Schedule Generation Scheme (SGS). We find that SIREN, the resulting algorithm, produces schedules that are of higher quality than those produced by the CP solver within the same computation time budget. The speed and solution quality of SIREN make it suitable as a component of an on-line scenario-based optimisation procedure for RCPSPs with stochastic durations. This leads to the SERENE system, which robustly selects, in real-time, the best next tasks to start in order to minimise the average makespan over the scenarios. Empirically, SERENE achieves better average makespan over different realisations of uncertainty than deterministic algorithms that continuously reschedule on the basis of either the worst, best or average task durations.","PeriodicalId":239898,"journal":{"name":"International Conference on Automated Planning and Scheduling","volume":"46 11","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113962191","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-01DOI: 10.1609/icaps.v33i1.27177
G. Behnke, David Speck, Michael Katz, Shirin Sohrabi
Since its introduction, partial satisfaction planning (PSP), including both oversubscription (OSP) and net-benefit, has received significant attention in the classical planning community. However, hierarchical aspects have been mostly ignored in this context, although several problem domains that form the main motivation for PSP, such as the rover domain, have an inherent hierarchical structure. In this paper, we are taking the necessary steps for facilitating this research direction. First, we formally define hierarchical partial satisfaction planning problems and discuss the usefulness and necessity of this formalism. Second, we present a carefully structured set of benchmarks consisting of OSP and net-benefit problems with hierarchical structure. We describe and analyze the different domains of the benchmark set and the desiderata that are met to provide an interesting and challenging starting point for upcoming research. Third, we introduce various planning techniques that can solve hierarchical OSP problems and investigate their empirical behaviour on our proposed benchmark.
{"title":"On Partial Satisfaction Planning with Total-Order HTNs","authors":"G. Behnke, David Speck, Michael Katz, Shirin Sohrabi","doi":"10.1609/icaps.v33i1.27177","DOIUrl":"https://doi.org/10.1609/icaps.v33i1.27177","url":null,"abstract":"Since its introduction, partial satisfaction planning (PSP), including both oversubscription (OSP) and net-benefit, has received significant attention in the classical planning community. \u0000However, hierarchical aspects have been mostly ignored in this context, although several problem domains that form the main motivation for PSP, such as the rover domain, have an inherent hierarchical structure.\u0000In this paper, we are taking the necessary steps for facilitating this research direction.\u0000First, we formally define hierarchical partial satisfaction planning problems and discuss the usefulness and necessity of this formalism. \u0000Second, we present a carefully structured set of benchmarks consisting of OSP and net-benefit problems with hierarchical structure.\u0000We describe and analyze the different domains of the benchmark set and the desiderata that are met to provide an interesting and challenging starting point for upcoming research.\u0000Third, we introduce various planning techniques that can solve hierarchical OSP problems and investigate their empirical behaviour on our proposed benchmark.","PeriodicalId":239898,"journal":{"name":"International Conference on Automated Planning and Scheduling","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132097902","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-01DOI: 10.1609/icaps.v33i1.27186
Daniel Fiser
A lifted mutex group is a schematic first-order description of sets of facts such that each set contains facts out of which at most one can hold in any reachable state. It was previously shown that lifted mutex groups can be used for pruning of operators during grounding of PDDL tasks, i.e., it is possible to prune unreachable and dead-end operators even before the grounded representation is known. Here, we show that applying such a pruning technique does not require a modification of the grounding procedure. Instead, it is possible to compile the conditions under which we can use lifted mutex groups to prune operators directly into the preconditions of lifted actions on the PDDL level. In fact, we show that such compilation captures the pruning power of lifted mutex groups perfectly.
{"title":"Operator Pruning Using Lifted Mutex Groups via Compilation on Lifted Level","authors":"Daniel Fiser","doi":"10.1609/icaps.v33i1.27186","DOIUrl":"https://doi.org/10.1609/icaps.v33i1.27186","url":null,"abstract":"A lifted mutex group is a schematic first-order description of sets of facts such that each set contains facts out of which at most one can hold in any reachable state. It was previously shown that lifted mutex groups can be used for pruning of operators during grounding of PDDL tasks, i.e., it is possible to prune unreachable and dead-end operators even before the grounded representation is known. Here, we show that applying such a pruning technique does not require a modification of the grounding procedure. Instead, it is possible to compile the conditions under which we can use lifted mutex groups to prune operators directly into the preconditions of lifted actions on the PDDL level. In fact, we show that such compilation captures the pruning power of lifted mutex groups perfectly.","PeriodicalId":239898,"journal":{"name":"International Conference on Automated Planning and Scheduling","volume":"87 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131176254","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-01DOI: 10.1609/icaps.v33i1.27211
Loc Pham, Tran Cao Son, Enrico Pontelli
Earlier epistemic planning systems for multi-agent domains generate plans that contain various types of actions such as ontic, sensing, or announcement actions. However, none of these systems consider untruthful announcements, i.e., none can generate plans that contain a lying or a misleading announcement. In this paper, we present a novel epistemic planner, called EFP3.0, for multi-agent domains with untruthful announcements. The planner is similar to the systems EFP or EFP2.0 in that it is a forward-search planner and can deal with unlimited nested beliefs and common knowledge by employing a Kripke based state representation and implementing an update model based transition function. Different from EFP, EFP3.0 employs a specification language that uses edge-conditioned update models for reasoning about effects of actions in multi-agent domains. We describe the basics of EFP3.0 and conduct experimental evaluations of the system against state-of-the-art epistemic planners. We discuss potential improvements that could be useful for scalability and efficiency of the system.
{"title":"Planning in Multi-Agent Domains with Untruthful Announcements","authors":"Loc Pham, Tran Cao Son, Enrico Pontelli","doi":"10.1609/icaps.v33i1.27211","DOIUrl":"https://doi.org/10.1609/icaps.v33i1.27211","url":null,"abstract":"Earlier epistemic planning systems for multi-agent domains generate plans that contain various types of actions such as ontic, sensing, or announcement actions. However, none of these systems consider untruthful announcements, i.e., none can generate plans that contain a lying or a misleading announcement. In this paper, we present a novel epistemic planner, called EFP3.0, for multi-agent domains with untruthful announcements. The planner is similar to the systems EFP or EFP2.0 in that it is a forward-search planner and can deal with unlimited nested beliefs and common knowledge by employing a Kripke based state representation and implementing an update model based transition function. Different from EFP, EFP3.0 employs a specification language that uses edge-conditioned update models for reasoning about effects of actions in multi-agent domains. We describe the basics of EFP3.0 and conduct experimental evaluations of the system against state-of-the-art epistemic planners. We discuss potential improvements that could be useful for scalability and efficiency of the system.","PeriodicalId":239898,"journal":{"name":"International Conference on Automated Planning and Scheduling","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123835021","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-01DOI: 10.1609/icaps.v33i1.27201
Ryo Kuroiwa, J. Christopher Beck
Domain-independent dynamic programming (DIDP) is a recently proposed model-based paradigm for combinatorial optimization where a problem is formulated as dynamic programming (DP) and solved by a generic solver. In this paper, we develop anytime heuristic search solvers for DIDP, which quickly find a feasible solution and continuously improve it to prove optimality. We implement six anytime heuristic search algorithms previously used as problem-specific methods and evaluate them on nine different problem classes. Our experimental results show that most of the anytime DIDP solvers outperform an existing A*-based solver, mixed-integer programming, and constraint programming in proving optimality, solution quality, and primal integral across multiple problem classes. In particular, complete anytime beam search (CABS) performs the best, improving on the best-known solution for one instance of traveling salesman problem with time windows and closing five instances of one-to-one multi-commodity pick-and-delivery traveling salesman problems.
{"title":"Solving Domain-Independent Dynamic Programming Problems with Anytime Heuristic Search","authors":"Ryo Kuroiwa, J. Christopher Beck","doi":"10.1609/icaps.v33i1.27201","DOIUrl":"https://doi.org/10.1609/icaps.v33i1.27201","url":null,"abstract":"Domain-independent dynamic programming (DIDP) is a recently proposed model-based paradigm for combinatorial optimization where a problem is formulated as dynamic programming (DP) and solved by a generic solver. In this paper, we develop anytime heuristic search solvers for DIDP, which quickly find a feasible solution and continuously improve it to prove optimality. We implement six anytime heuristic search algorithms previously used as problem-specific methods and evaluate them on nine different problem classes. Our experimental results show that most of the anytime DIDP solvers outperform an existing A*-based solver, mixed-integer programming, and constraint programming in proving optimality, solution quality, and primal integral across multiple problem classes. In particular, complete anytime beam search (CABS) performs the best, improving on the best-known solution for one instance of traveling salesman problem with time windows and closing five instances of one-to-one multi-commodity pick-and-delivery traveling salesman problems.","PeriodicalId":239898,"journal":{"name":"International Conference on Automated Planning and Scheduling","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126730302","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-01DOI: 10.1609/icaps.v33i1.27202
Edward Lam, Daniel D. Harabor, P. J. Stuckey, Jiaoyang Li
Given a set of agents on a grid, the multi-agent path finding problem aims to find a path that moves each agent from its given start location to its target location such that they do not collide and that the sum of arrival times is minimized. LNS2 is a state-of-the-art algorithm for anytime, suboptimal solving. It is an upper-bounding algorithm that repeatedly adjusts an existing solution and, being a local search, is oblivious to optimality. BCP is a state-of-the-art algorithm for exact solving. It is a lower-bounding tree search that attempts to tighten the lower bound until a solution appears. As BCP operates on the lower bound, the first solution it finds is optimal or nearly optimal, and therefore has poor anytime behavior. This paper proposes to tightly couple LNS2 and BCP to achieve better anytime, suboptimal solving while retaining the optimality guarantee of BCP. Experiments indicate that the combination achieves better anytime behavior than BCP in general and better suboptimal performance than LNS2 on congested maps.
{"title":"Exact Anytime Multi-Agent Path Finding Using Branch-and-Cut-and-Price and Large Neighborhood Search","authors":"Edward Lam, Daniel D. Harabor, P. J. Stuckey, Jiaoyang Li","doi":"10.1609/icaps.v33i1.27202","DOIUrl":"https://doi.org/10.1609/icaps.v33i1.27202","url":null,"abstract":"Given a set of agents on a grid, the multi-agent path finding problem aims to find a path that moves each agent from its given start location to its target location such that they do not collide and that the sum of arrival times is minimized. LNS2 is a state-of-the-art algorithm for anytime, suboptimal solving. It is an upper-bounding algorithm that repeatedly adjusts an existing solution and, being a local search, is oblivious to optimality. BCP is a state-of-the-art algorithm for exact solving. It is a lower-bounding tree search that attempts to tighten the lower bound until a solution appears. As BCP operates on the lower bound, the first solution it finds is optimal or nearly optimal, and therefore has poor anytime behavior. This paper proposes to tightly couple LNS2 and BCP to achieve better anytime, suboptimal solving while retaining the optimality guarantee of BCP. Experiments indicate that the combination achieves better anytime behavior than BCP in general and better suboptimal performance than LNS2 on congested maps.","PeriodicalId":239898,"journal":{"name":"International Conference on Automated Planning and Scheduling","volume":"94 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115032521","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-01DOI: 10.1609/icaps.v33i1.27214
Z. Ren, Jiaoyang Li, Han Zhang, Sven Koenig, S. Rathinam, H. Choset
This paper considers a multi-agent multi-objective path-finding problem that requires not only finding collision-free paths for multiple agents from their respective start locations to their respective goal locations but also optimizing multiple objectives simultaneously. In general, there is no single solution that optimizes all the objectives simultaneously, and the problem is thus to find the so-called Pareto-optimal frontier. To solve this problem, an algorithm called Multi-Objective Conflict-Based Search (MO-CBS) was recently developed and is guaranteed to find the exact Pareto-optimal frontier. However, MO-CBS does not scale well with the number of agents due to the large branching factor of the search, which leads to a lot of duplicated effort in agent-agent collision resolution. This paper therefore develops a new algorithm called Binary Branching MO-CBS (BB-MO-CBS) that reduces the branching factor as well as the duplicated collision resolution during the search, which expedites the search as a result. Our experimental results show that BB-MO-CBS reduces the number of conflicts by up to two orders of magnitude and often doubles or triples the success rates of MO-CBS on various maps given a runtime limit.
{"title":"Binary Branching Multi-Objective Conflict-Based Search for Multi-Agent Path Finding","authors":"Z. Ren, Jiaoyang Li, Han Zhang, Sven Koenig, S. Rathinam, H. Choset","doi":"10.1609/icaps.v33i1.27214","DOIUrl":"https://doi.org/10.1609/icaps.v33i1.27214","url":null,"abstract":"This paper considers a multi-agent multi-objective path-finding problem that requires not only finding collision-free paths for multiple agents from their respective start locations to their respective goal locations but also optimizing multiple objectives simultaneously. In general, there is no single solution that optimizes all the objectives simultaneously, and the problem is thus to find the so-called Pareto-optimal frontier. To solve this problem, an algorithm called Multi-Objective Conflict-Based Search (MO-CBS) was recently developed and is guaranteed to find the exact Pareto-optimal frontier. However, MO-CBS does not scale well with the number of agents due to the large branching factor of the search, which leads to a lot of duplicated effort in agent-agent collision resolution. This paper therefore develops a new algorithm called Binary Branching MO-CBS (BB-MO-CBS) that reduces the branching factor as well as the duplicated collision resolution during the search, which expedites the search as a result. Our experimental results show that BB-MO-CBS reduces the number of conflicts by up to two orders of magnitude and often doubles or triples the success rates of MO-CBS on various maps given a runtime limit.","PeriodicalId":239898,"journal":{"name":"International Conference on Automated Planning and Scheduling","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132950209","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-01DOI: 10.1609/icaps.v33i1.27206
Esther Mugdan, Remo Christen, Salomé Eriksson
Algorithms are usually shown to be correct on paper, but bugs in their implementations can still lead to incorrect results. In the case of classical planning, it is fortunately straightforward to check whether a computed plan is correct. For optimal planning however, plans are additionally required to have minimal cost, which is significantly more difficult to verify. While some domain-specific approaches exists, we lack a general tool to verify optimality for arbitrary problems. We bridge this gap and introduce two approaches based on the principle of certifying algorithms, which provide a computer-verifiable certificate of correctness alongside their answer. We show that both approaches are sound and complete, analyze whether they can be generated and verified efficiently, and show how to apply them to concrete planning algorithms. The experimental evaluation shows that verifying optimality comes with a cost but is still practically feasible. Furthermore it confirms that the tested planner configurations provide optimal plans on the given instances, as all certificates were verified successfully.
{"title":"Optimality Certificates for Classical Planning","authors":"Esther Mugdan, Remo Christen, Salomé Eriksson","doi":"10.1609/icaps.v33i1.27206","DOIUrl":"https://doi.org/10.1609/icaps.v33i1.27206","url":null,"abstract":"Algorithms are usually shown to be correct on paper, but bugs in their implementations can still lead to incorrect results. In the case of classical planning, it is fortunately straightforward to check whether a computed plan is correct. For optimal planning however, plans are additionally required to have minimal cost, which is significantly more difficult to verify. While some domain-specific approaches exists, we lack a general tool to verify optimality for arbitrary problems. We bridge this gap and introduce two approaches based on the principle of certifying algorithms, which provide a computer-verifiable certificate of correctness alongside their answer. We show that both approaches are sound and complete, analyze whether they can be generated and verified efficiently, and show how to apply them to concrete planning algorithms. The experimental evaluation shows that verifying optimality comes with a cost but is still practically feasible. Furthermore it confirms that the tested planner configurations provide optimal plans on the given instances, as all certificates were verified successfully.","PeriodicalId":239898,"journal":{"name":"International Conference on Automated Planning and Scheduling","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122416165","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-01DOI: 10.1609/icaps.v33i1.27188
Daniel Gnad, Silvan Sievers, Á. Torralba
Abstraction heuristics are a state-of-the-art technique to solve classical planning problems optimally. A common approach is to precompute many small abstractions and combine them admissibly using cost partitioning. Recent work has shown that this approach does not work out well when using such heuristics for decoupled state space search, where search nodes represent potentially large sets of states. This is due to the fact that admissibly combining the estimates of several heuristics without sacrificing accuracy is NP-hard for decoupled states. In this work we propose to use a single large abstraction instead. We focus on merge-and-shrink and symbolic pattern database heuristics, which are designed to produce such abstractions. For these heuristics, we prove that the evaluation of decoupled states is NP-hard in general, but we also identify conditions under which it is polynomial. We introduce algorithms for both the general and the polynomial case. Our experimental evaluation shows that single large abstraction heuristics lead to strong performance when the heuristic evaluation is polynomial.
{"title":"Efficient Evaluation of Large Abstractions for Decoupled Search: Merge-and-Shrink and Symbolic Pattern Databases","authors":"Daniel Gnad, Silvan Sievers, Á. Torralba","doi":"10.1609/icaps.v33i1.27188","DOIUrl":"https://doi.org/10.1609/icaps.v33i1.27188","url":null,"abstract":"Abstraction heuristics are a state-of-the-art technique to solve classical planning problems optimally. A common approach is to precompute many small abstractions and combine them admissibly using cost partitioning. Recent work has shown that this approach does not work out well when using such heuristics for decoupled state space search, where search nodes represent potentially large sets of states. This is due to the fact that admissibly combining the estimates of several heuristics without sacrificing accuracy is NP-hard for decoupled states. In this work we propose to use a single large abstraction instead. We focus on merge-and-shrink and symbolic pattern database heuristics, which are designed to produce such abstractions. For these heuristics, we prove that the evaluation of decoupled states is NP-hard in general, but we also identify conditions under which it is polynomial. We introduce algorithms for both the general and the polynomial case. Our experimental evaluation shows that single large abstraction heuristics lead to strong performance when the heuristic evaluation is polynomial.","PeriodicalId":239898,"journal":{"name":"International Conference on Automated Planning and Scheduling","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132288796","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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