Pub Date : 2023-07-01DOI: 10.1609/icaps.v33i1.27176
Mohammad Bakhshalipour, Mohamad Qadri, Dominic Guri, Seyed Borna Ehsani, M. Likhachev, Phillip B. Gibbons
A* suffers from limited parallelism. The maximum level of traditional parallelism in A* is the same as the degree of the search graph nodes, which is too small in many applications. As such, A* cannot fully leverage the multithreading capabilities of modern processors. In this paper, we go beyond traditional parallelism and introduce speculative parallelism for A*. We observe that A*'s node expansions exhibit predictable patterns in applications like path planning. Based on this observation, we propose Runahead A* (RA*). When a node is being expanded, RA* predicts future likely-to-be-expanded nodes, performs their corresponding computation on separate threads, and memoizes the computation results. Later when a predicted node is selected for expansion, rather than performing its computation, the memoized results are used, saving significant time in slow-expansion applications. We study five applications of A*. We show that when its prediction accuracy is high, RA* offers significant speedup over vanilla A* for slow-expansion applications. With 16 threads, RA*'s speedup for such applications ranges from 3.1x to 14.1x. We also study and provide insight into when, why, and to what extent node expansions are predictable. We provide an implementation of RA* at: https://github.com/cmu-roboarch/runahead-astar/
{"title":"Runahead A*: Speculative Parallelism for A* with Slow Expansions","authors":"Mohammad Bakhshalipour, Mohamad Qadri, Dominic Guri, Seyed Borna Ehsani, M. Likhachev, Phillip B. Gibbons","doi":"10.1609/icaps.v33i1.27176","DOIUrl":"https://doi.org/10.1609/icaps.v33i1.27176","url":null,"abstract":"A* suffers from limited parallelism. The maximum level of traditional parallelism in A* is the same as the degree of the search graph nodes, which is too small in many applications. As such, A* cannot fully leverage the multithreading capabilities of modern processors.\u0000In this paper, we go beyond traditional parallelism and introduce speculative parallelism for A*. We observe that A*'s node expansions exhibit predictable patterns in applications like path planning. Based on this observation, we propose Runahead A* (RA*). When a node is being expanded, RA* predicts future likely-to-be-expanded nodes, performs their corresponding computation on separate threads, and memoizes the computation results. Later when a predicted node is selected for expansion, rather than performing its computation, the memoized results are used, saving significant time in slow-expansion applications.\u0000We study five applications of A*. We show that when its prediction accuracy is high, RA* offers significant speedup over vanilla A* for slow-expansion applications. With 16 threads, RA*'s speedup for such applications ranges from 3.1x to 14.1x. We also study and provide insight into when, why, and to what extent node expansions are predictable. We provide an implementation of RA* at: https://github.com/cmu-roboarch/runahead-astar/","PeriodicalId":239898,"journal":{"name":"International Conference on Automated Planning and Scheduling","volume":"02 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":"130536816","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.v33i1.27172
Diego Aineto, Enrico Scala, E. Onaindía, I. Serina
This work explores the capabilities of current planning technologies to tackle the falsification of safety requirements for cyber-physical systems. Cyber-physical systems are systems where software and physical processes interact over time, and their requirements are commonly specified in temporal logic with time bounds. Roughly, falsification is the process of finding a trajectory of the cyber-physical system that violates the safety requirements, and it is a task typically tackled with black-box algorithms. We analyse the challenges posed by industry-driven falsification benchmarks taken from the ARCH-COMP competition, and propose a first attempt to deal with these problems through PDDL+ planning instead. Our experimental analysis on a selection of these problems provides empirical evidence on the feasibility and effectiveness of planning-based approaches, whilst also identifying the main areas of improvement.
{"title":"Falsification of Cyber-Physical Systems Using PDDL+ Planning","authors":"Diego Aineto, Enrico Scala, E. Onaindía, I. Serina","doi":"10.1609/icaps.v33i1.27172","DOIUrl":"https://doi.org/10.1609/icaps.v33i1.27172","url":null,"abstract":"This work explores the capabilities of current planning technologies to tackle the falsification of safety requirements for cyber-physical systems. Cyber-physical systems are systems where software and physical processes interact over time, and their requirements are commonly specified in temporal logic with time bounds. Roughly, falsification is the process of finding a trajectory of the cyber-physical system that violates the safety requirements, and it is a task typically tackled with black-box algorithms.\u0000We analyse the challenges posed by industry-driven falsification benchmarks taken from the ARCH-COMP competition, and propose a first attempt to deal with these problems through PDDL+ planning instead. Our experimental analysis on a selection of these problems provides empirical evidence on the feasibility and effectiveness of planning-based approaches, whilst also identifying the main areas of improvement.","PeriodicalId":239898,"journal":{"name":"International Conference on Automated Planning and Scheduling","volume":"14 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":"122222767","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.v33i1.27174
Dor Atzmon, S. Bernardini, F. Fagnani, D. Fairbairn
In tackling the multi-agent pathfinding problem (MAPF), we study a specific class of paths that are constructed by taking the agents’ shortest paths from the start to the goal locations and adding safe delays at the beginning of the paths, which guarantee that they are non-conflicting. Safe delays are calculated by exploiting a set of fundamental geometric constraints among the distances between all agents’ start and goal locations. Those constraints are simple, but the MAPF problem reformulated in terms of them remains computationally hard. Nonetheless, based on safe delays, we devise a new, fast and lightweight algorithm, called Delayed Shortest Path (DSP), to find solutions to the MAPF problem. Via an extensive experimental evaluation on standard benchmarks, we show that, in many cases, our technique runs several orders of magnitudes faster than related methods while addressing problems with thousands of agents and returning low-cost solutions.
{"title":"Exploiting Geometric Constraints in Multi-Agent Pathfinding","authors":"Dor Atzmon, S. Bernardini, F. Fagnani, D. Fairbairn","doi":"10.1609/icaps.v33i1.27174","DOIUrl":"https://doi.org/10.1609/icaps.v33i1.27174","url":null,"abstract":"In tackling the multi-agent pathfinding problem (MAPF), we study a specific class of paths that are constructed by taking the agents’ shortest paths from the start to the goal locations and adding safe delays at the beginning of the paths, which guarantee that they are non-conflicting. Safe delays are calculated by exploiting a set of fundamental geometric constraints among the distances between all agents’ start and goal locations. Those constraints are simple, but the MAPF problem reformulated in terms of them remains computationally hard. Nonetheless, based on safe delays, we devise a new, fast and lightweight algorithm, called Delayed Shortest Path (DSP), to find solutions to the MAPF problem. Via an extensive experimental evaluation on standard benchmarks, we show that, in many cases, our technique runs several orders of magnitudes faster than related methods while addressing problems with thousands of agents and returning low-cost solutions.","PeriodicalId":239898,"journal":{"name":"International Conference on Automated Planning and Scheduling","volume":"1107 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":"116056131","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.v33i1.27194
Taoan Huang, Vikas Shivashankar, Michael Caldara, Joseph W. Durham, Jiaoyang Li, B. Dilkina, Sven Koenig
The increasing demand for same-day delivery and the commitment of e-commerce companies to this service raise a number of challenges in logistics. One of these challenges for fulfillment centers is to coordinate hundreds of mobile robots in their automated warehouses efficiently to allow for the retrieval and packing of thousands of ordered items within the promised delivery deadlines. We formulate this challenge as the new problem of deadline-aware multi-agent tour planning, where the objective is to coordinate robots to visit multiple picking stations in congested warehouses to allow as many orders to be packed on time as possible. To solve it, we propose LaRge NeighbOrhood Search for DEadline-Aware MulTi-Agent Tour PlAnning (ROSETTA). We conduct extensive experiments to evaluate ROSETTA with up to 350 robots in simulated warehouses inspired by KIVA systems. We show that it increases the number of orders completed on time by up to 38% compared to several baseline algorithms and also significantly outperforms them in terms of throughput and station utilization.
{"title":"Deadline-Aware Multi-Agent Tour Planning","authors":"Taoan Huang, Vikas Shivashankar, Michael Caldara, Joseph W. Durham, Jiaoyang Li, B. Dilkina, Sven Koenig","doi":"10.1609/icaps.v33i1.27194","DOIUrl":"https://doi.org/10.1609/icaps.v33i1.27194","url":null,"abstract":"The increasing demand for same-day delivery and the commitment of e-commerce companies to this service raise a number of challenges in logistics. One of these challenges for fulfillment centers is to coordinate hundreds of mobile robots in their automated warehouses efficiently to allow for the retrieval and packing of thousands of ordered items within the promised delivery deadlines. We formulate this challenge as the new problem of deadline-aware multi-agent tour planning, where the objective is to coordinate robots to visit multiple picking stations in congested warehouses to allow as many orders to be packed on time as possible. To solve it, we propose LaRge NeighbOrhood Search for DEadline-Aware MulTi-Agent Tour PlAnning (ROSETTA). We conduct extensive experiments to evaluate ROSETTA with up to 350 robots in simulated warehouses inspired by KIVA systems. We show that it increases the number of orders completed on time by up to 38% compared to several baseline algorithms and also significantly outperforms them in terms of throughput and station utilization.","PeriodicalId":239898,"journal":{"name":"International Conference on Automated Planning and Scheduling","volume":"37 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":"127607314","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.v33i1.27218
Alexander Shleyfman, Ryo Kuroiwa, J. Christopher Beck
One of the main challenges of domain-independent numeric planning is the complexity of the search problem. The exploitation of structural symmetries in a search problem can constitute an effective method of pruning search branches that may lead to exponential improvements in performance. For over a decade, symmetry breaking techniques have been successfully used within both optimal and satisficing classical planning. In this work, we show that symmetry detection methods applied in classical planning with some effort can be modified to detect symmetries in linear numeric planning. The detected symmetry group, thereafter, can be used almost directly in the A*-based symmetry breaking algorithms such as DKS and Orbit Space Search. We empirically validate that symmetry pruning can yield a substantial reduction in the search effort, even if algorithms are equipped with a strong heuristic, such as LM-cut.
{"title":"Symmetry Detection and Breaking in Linear Cost-Optimal Numeric Planning","authors":"Alexander Shleyfman, Ryo Kuroiwa, J. Christopher Beck","doi":"10.1609/icaps.v33i1.27218","DOIUrl":"https://doi.org/10.1609/icaps.v33i1.27218","url":null,"abstract":"One of the main challenges of domain-independent numeric planning is the complexity of the search problem. The exploitation of structural symmetries in a search problem can constitute an effective method of pruning search branches that may lead to exponential improvements in performance. For over a decade, symmetry breaking techniques have been successfully used within both optimal and satisficing classical planning. In this work, we show that symmetry detection methods applied in classical planning with some effort can be modified to detect symmetries in linear numeric planning. The detected symmetry group, thereafter, can be used almost directly in the A*-based symmetry breaking algorithms such as DKS and Orbit Space Search. We empirically validate that symmetry pruning can yield a substantial reduction in the search effort, even if algorithms are equipped with a strong heuristic, such as LM-cut.","PeriodicalId":239898,"journal":{"name":"International Conference on Automated Planning and Scheduling","volume":"70 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":"115538402","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.v33i1.27213
S. Polyakovskiy, Peter J. Stuckey
The guillotine rectangular cutting problem deals with a single rectangular plate of raw material and a collection of rectangular items to be cut from the plate. Each item is associated with a profit and a demand. The problem searches for a feasible layout of a subset of items on the plate so as to maximize the total profit of selected items. The guillotine constraint restricts feasible layouts to those that can be obtained via guillotine edge-to-edge cuts that run parallel to an edge of the plate. We propose a novel constraint programming model that is suitable for guillotine cutting with an arbitrary number of stages of alternating horizontal and vertical guillotine cuts. This is an assignment-based model that models guillotine cuts using a constant number of rectangular regions, with some regions allocated to items. It treats the entire plate as a primary region and decides on the guillotine cuts required to split the regions recursively till they produce space for the items. To speed the search, the model explores the strength of cumulative scheduling relaxations of the cutting problem. Our model is a successful alternative to more traditional mixed-integer linear programming (MILP) models. It outperforms a number of state-of-the-art MILPs on a set of small and moderate size benchmark instances and proves optimality for several instances that remain challenging for these MILPs.
{"title":"A Constraint Programming Solution to the Guillotine Rectangular Cutting Problem","authors":"S. Polyakovskiy, Peter J. Stuckey","doi":"10.1609/icaps.v33i1.27213","DOIUrl":"https://doi.org/10.1609/icaps.v33i1.27213","url":null,"abstract":"The guillotine rectangular cutting problem deals with a single rectangular plate of raw material and a collection of rectangular items to be cut from the plate. Each item is associated with a profit and a demand. The problem searches for a feasible layout of a subset of items on the plate so as to maximize the total profit of selected items. The guillotine constraint restricts feasible layouts to those that can be obtained via guillotine edge-to-edge cuts that run parallel to an edge of the plate. We propose a novel constraint programming model that is suitable for guillotine cutting with an arbitrary number of stages of alternating horizontal and vertical guillotine cuts. This is an assignment-based model that models guillotine cuts using a constant number of rectangular regions, with some regions allocated to items. It treats the entire plate as a primary region and decides on the guillotine cuts required to split the regions recursively till they produce space for the items. To speed the search, the model explores the strength of cumulative scheduling relaxations of the cutting problem. Our model is a successful alternative to more traditional mixed-integer linear programming (MILP) models. It outperforms a number of state-of-the-art MILPs on a set of small and moderate size benchmark instances and proves optimality for several instances that remain challenging for these MILPs.","PeriodicalId":239898,"journal":{"name":"International Conference on Automated Planning and Scheduling","volume":"10 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":"115389331","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.v33i1.27234
A. Umbrico, Marco Benadduci, R. Bevilacqua, A. Cesta, Francesca Fracasso, E. Maranesi, Andrea Orlandini, Gabriella Cortellessa
This work investigate temporal planning to synthesize personalized physical rehabilitation programs. The first contribution of the work concerns the representation of (heterogeneous) clinical and spatial constraints into a planning framework. The second contribution is the integration of numerical and symbolic reasoning to synthesize technically valid and coherent plans with respect to different clinical objectives. The experimental section discusses the developed planner from a technical view, assessing solving and personalization capabilities, and from a clinical view, assessing the efficacy of plans on the involved patients.
{"title":"Combining Clinical and Spatial Constraints into Temporal Planning to Personalize Physical Rehabilitation","authors":"A. Umbrico, Marco Benadduci, R. Bevilacqua, A. Cesta, Francesca Fracasso, E. Maranesi, Andrea Orlandini, Gabriella Cortellessa","doi":"10.1609/icaps.v33i1.27234","DOIUrl":"https://doi.org/10.1609/icaps.v33i1.27234","url":null,"abstract":"This work investigate temporal planning to synthesize personalized physical rehabilitation programs. The first contribution of the work concerns the representation of (heterogeneous) clinical and spatial constraints into a planning framework. The second contribution is the integration of numerical and symbolic reasoning to synthesize technically valid and coherent plans with respect to different clinical objectives. The experimental section discusses the developed planner from a technical view, assessing solving and personalization capabilities, and from a clinical view, assessing the efficacy of plans on the involved patients.","PeriodicalId":239898,"journal":{"name":"International Conference on Automated Planning and Scheduling","volume":"35 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":"125036836","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.v33i1.27217
Bojie Shen, Zhe Chen, Jiaoyang Li, M. A. Cheema, Daniel D. Harabor, P. J. Stuckey
In Multi-Agent Path Finding (MAPF), we are asked to plan collision-free paths for teams of moving agents. Among the leading methods for optimal MAPF is Conflict-Based Search (CBS), an algorithmic family which has received intense attention in recent years and for which large advancements in efficiency and effectiveness have been reported. Yet all of the recent CBS gains come from reasoning over pairs of agents only. In this paper, we show how to further improve CBS by reasoning about more than two agents at the same time. Our new cluster reasoning techniques allow us to generate stronger bounds for CBS and to identify more bypasses (alternative cost-equivalent paths), which reduce the number of nodes in the CBS conflict tree.
{"title":"Beyond Pairwise Reasoning in Multi-Agent Path Finding","authors":"Bojie Shen, Zhe Chen, Jiaoyang Li, M. A. Cheema, Daniel D. Harabor, P. J. Stuckey","doi":"10.1609/icaps.v33i1.27217","DOIUrl":"https://doi.org/10.1609/icaps.v33i1.27217","url":null,"abstract":"In Multi-Agent Path Finding (MAPF), we are asked to plan collision-free paths for teams of moving agents. Among the leading methods for optimal MAPF is Conflict-Based Search (CBS), an algorithmic family which has received intense attention in recent years and for which large advancements in efficiency and effectiveness have been reported. Yet all of the recent CBS gains come from reasoning over pairs of agents only. In this paper, we show how to further improve CBS by reasoning about more than two agents at the same time. Our new cluster reasoning techniques allow us to generate stronger bounds for CBS and to identify more bypasses (alternative cost-equivalent paths), which reduce the number of nodes in the CBS conflict tree.","PeriodicalId":239898,"journal":{"name":"International Conference on Automated Planning and Scheduling","volume":"12 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":"122075150","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.v33i1.27184
Augusto B. Corrêa, Markus Hecher, M. Helmert, David M Longo, F. Pommerening, S. Woltran
Classical planning tasks are commonly described in a first-order language. However, most classical planners translate tasks by grounding them and then rewriting them into a propositional language. In recent years, the grounding step has become a larger bottleneck. In this work, we study how to improve it. We build on top of the most common grounder for planning tasks which uses Datalog to find all reachable atoms and actions. Inspired by recent progress in lifted planning, database theory, and algorithmics, we develop a new method to ground these Datalog programs. Our algorithm can ground more instances than the baseline, and most tasks it cannot ground are out of reach from any ground planner.
{"title":"Grounding Planning Tasks Using Tree Decompositions and Iterated Solving","authors":"Augusto B. Corrêa, Markus Hecher, M. Helmert, David M Longo, F. Pommerening, S. Woltran","doi":"10.1609/icaps.v33i1.27184","DOIUrl":"https://doi.org/10.1609/icaps.v33i1.27184","url":null,"abstract":"Classical planning tasks are commonly described in a first-order language. However, most classical planners translate tasks by grounding them and then rewriting them into a propositional language. In recent years, the grounding step has become a larger bottleneck. In this work, we study how to improve it. We build on top of the most common grounder for planning tasks which uses Datalog to find all reachable atoms and actions. Inspired by recent progress in lifted planning, database theory, and algorithmics, we develop a new method to ground these Datalog programs. Our algorithm can ground more instances than the baseline, and most tasks it cannot ground are out of reach from any ground planner.","PeriodicalId":239898,"journal":{"name":"International Conference on Automated Planning and Scheduling","volume":"37 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":"122155104","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.v33i1.27222
Eyal Weiss, G. Kaminka
AI Planning require computing the costs of ground actions. While often assumed to be negligible, the run-time of this computation can become a major component in the overall planning run-time. To address this, we introduce planning with multiple action cost estimates, a generalization of classical planning, where action cost can be incrementally determined using multiple estimation procedures, which trade computational effort for increasingly tightening bounds on the exact cost. We then present ACE, a generalized A*, to solve such problems. We provide theoretical guarantees, and extensive experiments that show considerable run-time savings compared to alternatives.
{"title":"Planning with Multiple Action-Cost Estimates","authors":"Eyal Weiss, G. Kaminka","doi":"10.1609/icaps.v33i1.27222","DOIUrl":"https://doi.org/10.1609/icaps.v33i1.27222","url":null,"abstract":"AI Planning require computing the costs of ground actions. While often assumed to be negligible, the run-time of this computation can become a major component in the overall planning run-time. To address this, we introduce planning with multiple action cost estimates, a generalization of classical planning, where action cost can be incrementally determined using multiple estimation procedures, which trade computational effort for increasingly tightening bounds on the exact cost. We then present ACE, a generalized A*, to solve such problems. We provide theoretical guarantees, and extensive experiments that show considerable run-time savings compared to alternatives.","PeriodicalId":239898,"journal":{"name":"International Conference on Automated Planning and Scheduling","volume":"56 80 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":"132720367","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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