Pub Date : 2022-06-13DOI: 10.1609/icaps.v32i1.19823
Julian von Tschammer, Robert Mattmüller, David Speck
In top-k planning, the objective is to determine a set of k cheapest plans that provide several good alternatives to choose from. Such a solution set often contains plans that visit at least one state more than once. Depending on the application, plans with such loops are of little importance because they are dominated by a loopless representative and can prevent more meaningful plans from being found. In this paper, we motivate and introduce loopless top-k planning. We show how to enhance the state-of-the-art symbolic top-k planner, symK, to obtain an efficient, sound and complete algorithm for loopless top-k planning. An empirical evaluation shows that our proposed approach has a higher k-coverage than a generate-and-test approach that uses an ordinary top-k planner, which we show to be incomplete in the presence of zero-cost loops.
{"title":"Loopless Top-K Planning","authors":"Julian von Tschammer, Robert Mattmüller, David Speck","doi":"10.1609/icaps.v32i1.19823","DOIUrl":"https://doi.org/10.1609/icaps.v32i1.19823","url":null,"abstract":"In top-k planning, the objective is to determine a set of k cheapest plans that provide several good alternatives to choose from. Such a solution set often contains plans that visit at least one state more than once. Depending on the application, plans with such loops are of little importance because they are dominated by a loopless representative and can prevent more meaningful plans from being found.\u0000\u0000In this paper, we motivate and introduce loopless top-k planning. We show how to enhance the state-of-the-art symbolic top-k planner, symK, to obtain an efficient, sound and complete algorithm for loopless top-k planning. An empirical evaluation shows that our proposed approach has a higher k-coverage than a generate-and-test approach that uses an ordinary top-k planner, which we show to be incomplete in the presence of zero-cost loops.","PeriodicalId":239898,"journal":{"name":"International Conference on Automated Planning and Scheduling","volume":"170 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115962160","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 : 2022-06-13DOI: 10.1609/icaps.v32i1.19854
Alvaro Velasquez, Brett Bissey, Lior Barak, Daniel Melcer, Andre Beckus, Ismail R. Alkhouri, George K. Atia
Sparse rewards and their representation in multi-agent domains remains a challenge for the development of multi-agent planning systems. While techniques from formal methods can be adopted to represent the underlying planning objectives, their use in facilitating and accelerating learning has witnessed limited attention in multi-agent settings. Reward shaping methods that leverage such formal representations in single-agent settings are typically static in the sense that the artificial rewards remain the same throughout the entire learning process. In contrast, we investigate the use of such formal objective representations to define novel reward shaping functions that capture the learned experience of the agents. More specifically, we leverage the automaton representation of the underlying team objectives in mixed cooperative-competitive domains such that each automaton transition is assigned an expected value proportional to the frequency with which it was observed in successful trajectories of past behavior. This form of dynamic reward shaping is proposed within a multi-agent tree search architecture wherein agents can simultaneously reason about the future behavior of other agents as well as their own future behavior.
{"title":"Multi-Agent Tree Search with Dynamic Reward Shaping","authors":"Alvaro Velasquez, Brett Bissey, Lior Barak, Daniel Melcer, Andre Beckus, Ismail R. Alkhouri, George K. Atia","doi":"10.1609/icaps.v32i1.19854","DOIUrl":"https://doi.org/10.1609/icaps.v32i1.19854","url":null,"abstract":"Sparse rewards and their representation in multi-agent domains remains a challenge for the development of multi-agent planning systems. While techniques from formal methods can be adopted to represent the underlying planning objectives, their use in facilitating and accelerating learning has witnessed limited attention in multi-agent settings. Reward shaping methods that leverage such formal representations in single-agent settings are typically static in the sense that the artificial rewards remain the same throughout the entire learning process. In contrast, we investigate the use of such formal objective representations to define novel reward shaping functions that capture the learned experience of the agents. More specifically, we leverage the automaton representation of the underlying team objectives in mixed cooperative-competitive domains such that each automaton transition is assigned an expected value proportional to the frequency with which it was observed in successful trajectories of past behavior. This form of dynamic reward shaping is proposed within a multi-agent tree search architecture wherein agents can simultaneously reason about the future behavior of other agents as well as their own future behavior.","PeriodicalId":239898,"journal":{"name":"International Conference on Automated Planning and Scheduling","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122415854","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 : 2022-06-13DOI: 10.1609/icaps.v32i1.19804
Ryo Kuroiwa, J. Christopher Beck
Satisficing heuristic search such as greedy best-first search (GBFS) suffers from local minima, regions where heuristic values are inaccurate and a good node has a worse heuristic value than other nodes. Search algorithms that incorporate exploration mechanisms in GBFS empirically reduce the search effort to solve difficult problems. Although some of these methods entirely ignore the guidance of a heuristic during their exploration phase, intuitively, a good heuristic should have some bound on its inaccuracy, and exploration mechanisms should exploit this bound. In this paper, we theoretically analyze what a good node is for satisficing heuristic search algorithms and show that the heuristic value of a good node has an upper bound if a heuristic satisfies a certain property. Then, we propose biased exploration mechanisms which select lower heuristic values with higher probabilities. In the experiments using synthetic graph search problems and classical planning benchmarks, we show that the biased exploration mechanisms can be useful. In particular, one of our methods, Softmin-Type(h), significantly outperforms other GBFS variants in classical planning and improves the performance of Type-LAMA, a state-of-the-art classical planner.
{"title":"Biased Exploration for Satisficing Heuristic Search","authors":"Ryo Kuroiwa, J. Christopher Beck","doi":"10.1609/icaps.v32i1.19804","DOIUrl":"https://doi.org/10.1609/icaps.v32i1.19804","url":null,"abstract":"Satisficing heuristic search such as greedy best-first search (GBFS) suffers from local minima, regions where heuristic values are inaccurate and a good node has a worse heuristic value than other nodes. Search algorithms that incorporate exploration mechanisms in GBFS empirically reduce the search effort to solve difficult problems. Although some of these methods entirely ignore the guidance of a heuristic during their exploration phase, intuitively, a good heuristic should have some bound on its inaccuracy, and exploration mechanisms should exploit this bound. In this paper, we theoretically analyze what a good node is for satisficing heuristic search algorithms and show that the heuristic value of a good node has an upper bound if a heuristic satisfies a certain property. Then, we propose biased exploration mechanisms which select lower heuristic values with higher probabilities. In the experiments using synthetic graph search problems and classical planning benchmarks, we show that the biased exploration mechanisms can be useful. In particular, one of our methods, Softmin-Type(h), significantly outperforms other GBFS variants in classical planning and improves the performance of Type-LAMA, a state-of-the-art classical planner.","PeriodicalId":239898,"journal":{"name":"International Conference on Automated Planning and Scheduling","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123816134","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}
Central to efficient ride-pooling are two challenges: (1) how to `price' customers' requests for rides, and (2) if the customer agrees to that price, how to best `match' these requests to drivers. While both of them are interdependent, each challenge's individual complexity has meant that, historically, they have been decoupled and studied individually. This paper creates a framework for batched pricing and matching in which pricing is seen as a meta-level optimisation over different possible matching decisions. Our key contributions are in developing a variant of the revenue-maximizing auction corresponding to the meta-level optimization problem, and then providing a scalable mechanism for computing posted prices. We test our algorithm on real-world data at city-scale and show that our algorithm reliably matches demand to supply across a range of parameters.
{"title":"Joint Pricing and Matching for City-Scale Ride-Pooling","authors":"Sanket Shah, Meghna Lowalekar, Pradeep Varakantham","doi":"10.1609/icaps.v32i1.19836","DOIUrl":"https://doi.org/10.1609/icaps.v32i1.19836","url":null,"abstract":"Central to efficient ride-pooling are two challenges: (1) how to `price' customers' requests for rides, and (2) if the customer agrees to that price, how to best `match' these requests to drivers. While both of them are interdependent, each challenge's individual complexity has meant that, historically, they have been decoupled and studied individually.\u0000\u0000This paper creates a framework for batched pricing and matching in which pricing is seen as a meta-level optimisation over different possible matching decisions. Our key contributions are in developing a variant of the revenue-maximizing auction corresponding to the meta-level optimization problem, and then providing a scalable mechanism for computing posted prices. We test our algorithm on real-world data at city-scale and show that our algorithm reliably matches demand to supply across a range of parameters.","PeriodicalId":239898,"journal":{"name":"International Conference on Automated Planning and Scheduling","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127745836","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 : 2022-06-13DOI: 10.1609/icaps.v32i1.19834
Geert L. J. Pingen, C. R. V. Ommeren, C. J. V. Leeuwen, Ruben Fransen, Tijmen Elfrink, Yorick C. de Vries, Janarthanan Karunakaran, Emir Demirovic, N. Yorke-Smith
Logistics planning is a complex optimization problem involving multiple decision makers. Automated scheduling systems offer support to human planners; however state-of-the-art approaches often employ a centralized control paradigm. While these approaches have shown great value, their application is hindered in dynamic settings with no central authority. Motivated by real-world scenarios, we present a decentralized approach to collaborative multi-agent scheduling by casting the problem as a Distributed Constraint Optimization Problem (DCOP). Our model-based heuristic approach uses message passing with a novel pruning technique to allow agents to cooperate on mutual agreement, leading to a near-optimal solution while offering low computational costs and flexibility in case of disruptions. Performance is evaluated in three real-world field trials with a logistics carrier and compared against a centralized model-free Deep Q-Network (DQN)-based Reinforcement Learning (RL) approach, a Mixed-Integer Linear Programming (MILP)-based solver, and both human and heuristic baselines. The results demonstrate that it is feasible to have virtual agents make autonomous decisions using our DCOP method, leading to an efficient distributed solution. To facilitate further research in Self-Organizing Logistics (SOL), we provide a novel real-life dataset.
{"title":"Talking Trucks: Decentralized Collaborative Multi-Agent Order Scheduling for Self-Organizing Logistics","authors":"Geert L. J. Pingen, C. R. V. Ommeren, C. J. V. Leeuwen, Ruben Fransen, Tijmen Elfrink, Yorick C. de Vries, Janarthanan Karunakaran, Emir Demirovic, N. Yorke-Smith","doi":"10.1609/icaps.v32i1.19834","DOIUrl":"https://doi.org/10.1609/icaps.v32i1.19834","url":null,"abstract":"Logistics planning is a complex optimization problem involving multiple decision makers. Automated scheduling systems offer support to human planners; however state-of-the-art approaches often employ a centralized control paradigm. While these approaches have shown great value, their application is hindered in dynamic settings with no central authority. Motivated by real-world scenarios, we present a decentralized approach to collaborative multi-agent scheduling by casting the problem as a Distributed Constraint Optimization Problem (DCOP). Our model-based heuristic approach uses message passing with a novel pruning technique to allow agents to cooperate on mutual agreement, leading to a near-optimal solution while offering low computational costs and flexibility in case of disruptions. Performance is evaluated in three real-world field trials with a logistics carrier and compared against a centralized model-free Deep Q-Network (DQN)-based Reinforcement Learning (RL) approach, a Mixed-Integer Linear Programming (MILP)-based solver, and both human and heuristic baselines. The results demonstrate that it is feasible to have virtual agents make autonomous decisions using our DCOP method, leading to an efficient distributed solution. To facilitate further research in Self-Organizing Logistics (SOL), we provide a novel real-life dataset.","PeriodicalId":239898,"journal":{"name":"International Conference on Automated Planning and Scheduling","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133539902","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 : 2022-06-13DOI: 10.1609/icaps.v32i1.19793
M. Helmert, Silvan Sievers, Alexander Rovner, Augusto B. Corrêa
Potential functions are a general class of heuristics for classical planning. For satisficing planning, previous work suggested the use of descending and dead-end avoiding (DDA) potential heuristics, which solve planning tasks by backtrack-free search. In this work we study the complexity of devising DDA potential heuristics for classical planning tasks. We show that verifying or synthesizing DDA potential heuristics is PSPACE-complete, but suitable modifications of the DDA properties reduce the complexity of these problems to the first and second level of the polynomial hierarchy. We also discuss the implications of our results for other forms of heuristic synthesis in classical planning.
{"title":"On the Complexity of Heuristic Synthesis for Satisficing Classical Planning: Potential Heuristics and Beyond","authors":"M. Helmert, Silvan Sievers, Alexander Rovner, Augusto B. Corrêa","doi":"10.1609/icaps.v32i1.19793","DOIUrl":"https://doi.org/10.1609/icaps.v32i1.19793","url":null,"abstract":"Potential functions are a general class of heuristics for classical planning. For satisficing planning, previous work suggested the use of descending and dead-end avoiding (DDA) potential heuristics, which solve planning tasks by backtrack-free search. In this work we study the complexity of devising DDA potential heuristics for classical planning tasks. We show that verifying or synthesizing DDA potential heuristics is PSPACE-complete, but suitable modifications of the DDA properties reduce the complexity of these problems to the first and second level of the polynomial hierarchy. We also discuss the implications of our results for other forms of heuristic synthesis in classical planning.","PeriodicalId":239898,"journal":{"name":"International Conference on Automated Planning and Scheduling","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130992840","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 : 2022-06-13DOI: 10.1609/icaps.v32i1.19839
Zheyuan Wang, M. Gombolay
Resource optimization for predictive maintenance is a challenging computational problem that requires inferring and reasoning over stochastic failure models and dynamically allocating repair resources. Predictive maintenance scheduling is typically performed with a combination of ad hoc, hand-crafted heuristics with manual scheduling corrections by human domain experts, which is a labor-intensive process that is hard to scale. In this paper, we develop an innovative heterogeneous graph neural network to automatically learn an end-to-end resource scheduling policy. Our approach is fully graph-based with the addition of state summary and decision value nodes that provides a computationally lightweight and nonparametric means to perform dynamic scheduling. We augment our policy optimization procedure to enable robust learning in highly stochastic environments for which typical actor-critic reinforcement learning methods are ill-suited. In consultation with aerospace industry partners, we develop a virtual predictive-maintenance environment for a heterogeneous fleet of aircraft, called AirME. Our approach sets a new state-of-the-art by outperforming conventional, hand-crafted heuristics and baseline learning methods across problem sizes and various objective functions.
{"title":"Stochastic Resource Optimization over Heterogeneous Graph Neural Networks for Failure-Predictive Maintenance Scheduling","authors":"Zheyuan Wang, M. Gombolay","doi":"10.1609/icaps.v32i1.19839","DOIUrl":"https://doi.org/10.1609/icaps.v32i1.19839","url":null,"abstract":"Resource optimization for predictive maintenance is a challenging computational problem that requires inferring and reasoning over stochastic failure models and dynamically allocating repair resources. Predictive maintenance scheduling is typically performed with a combination of ad hoc, hand-crafted heuristics with manual scheduling corrections by human domain experts, which is a labor-intensive process that is hard to scale. In this paper, we develop an innovative heterogeneous graph neural network to automatically learn an end-to-end resource scheduling policy. Our approach is fully graph-based with the addition of state summary and decision value nodes that provides a computationally lightweight and nonparametric means to perform dynamic scheduling. We augment our policy optimization procedure to enable robust learning in highly stochastic environments for which typical actor-critic reinforcement learning methods are ill-suited. In consultation with aerospace industry partners, we develop a virtual predictive-maintenance environment for a heterogeneous fleet of aircraft, called AirME. Our approach sets a new state-of-the-art by outperforming conventional, hand-crafted heuristics and baseline learning methods across problem sizes and various objective functions.","PeriodicalId":239898,"journal":{"name":"International Conference on Automated Planning and Scheduling","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128383292","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 : 2022-06-13DOI: 10.1609/icaps.v32i1.19802
Thorsten Klößner, F. Pommerening, Thomas Keller, G. Röger
In classical planning, cost partitioning is a powerful method which allows to combine multiple admissible heuristics while retaining an admissible bound. In this paper, we extend the theory of cost partitioning to probabilistic planning by generalizing from deterministic transition systems to stochastic shortest path problems (SSPs). We show that fundamental results related to cost partitioning still hold in our extended theory. We also investigate how to optimally partition costs for a large class of abstraction heuristics for SSPs. Lastly, we analyze occupation measure heuristics for SSPs as well as the theory of approximate linear programming for reward-oriented Markov decision processes. All of these fit our framework and can be seen as cost-partitioned heuristics.
{"title":"Cost Partitioning Heuristics for Stochastic Shortest Path Problems","authors":"Thorsten Klößner, F. Pommerening, Thomas Keller, G. Röger","doi":"10.1609/icaps.v32i1.19802","DOIUrl":"https://doi.org/10.1609/icaps.v32i1.19802","url":null,"abstract":"In classical planning, cost partitioning is a powerful method which allows to combine multiple admissible heuristics while retaining an admissible bound. In this paper, we extend the theory of cost partitioning to probabilistic planning by generalizing from deterministic transition systems to stochastic shortest path problems (SSPs). We show that fundamental results related to cost partitioning still hold in our extended theory. We also investigate how to optimally partition costs for a large class of abstraction heuristics for SSPs. Lastly, we analyze occupation measure heuristics for SSPs as well as the theory of approximate linear programming for reward-oriented Markov decision processes. All of these fit our framework and can be seen as cost-partitioned heuristics.","PeriodicalId":239898,"journal":{"name":"International Conference on Automated Planning and Scheduling","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114976372","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 : 2022-06-13DOI: 10.1609/icaps.v32i1.19830
Jonathan Chase, Siong Thye Goh, T. Phong, H. Lau
In the quest to achieve better response times in dense urban environments, law enforcement agencies are seeking AI-driven planning systems to inform their patrol strategies. In this paper, we present a framework, OFFICERS, for deployment planning that learns from historical data to generate deployment schedules on a daily basis. We accurately predict incidents using ST-ResNet, a deep learning technique that captures wide-ranging spatio-temporal dependencies, and solve a large-scale optimization problem to schedule deployment, significantly improving its scalability through a simulated annealing solver. Methodologically, our approach outperforms our previous works where prediction was done using Generative Adversarial Networks, and optimization was performed with the CPLEX solver. Furthermore, we show that our proposed framework is designed to be readily transferable between use cases, handling a wide range of both criminal and non-criminal incidents, with the use of deep learning and a general-purpose efficient solver, reducing dependence on context-specific details. We demonstrate the value of our approach on a police patrol case study, and discuss both the ethical considerations, and operational requirements, for deployment of a lightweight and responsive planning system.
{"title":"OFFICERS: Operational Framework for Intelligent Crime-and-Emergency Response Scheduling","authors":"Jonathan Chase, Siong Thye Goh, T. Phong, H. Lau","doi":"10.1609/icaps.v32i1.19830","DOIUrl":"https://doi.org/10.1609/icaps.v32i1.19830","url":null,"abstract":"In the quest to achieve better response times in dense urban environments, law enforcement agencies are seeking AI-driven planning systems to inform their patrol strategies. In this paper, we present a framework, OFFICERS, for deployment planning that learns from historical data to generate deployment schedules on a daily basis. We accurately predict incidents using ST-ResNet, a deep learning technique that captures wide-ranging spatio-temporal dependencies, and solve a large-scale optimization problem to schedule deployment, significantly improving its scalability through a simulated annealing solver. Methodologically, our approach outperforms our previous works where prediction was done using Generative Adversarial Networks, and optimization was performed with the CPLEX solver. Furthermore, we show that our proposed framework is designed to be readily transferable between use cases, handling a wide range of both criminal and non-criminal incidents, with the use of deep learning and a general-purpose efficient solver, reducing dependence on context-specific details. We demonstrate the value of our approach on a police patrol case study, and discuss both the ethical considerations, and operational requirements, for deployment of a lightweight and responsive planning system.","PeriodicalId":239898,"journal":{"name":"International Conference on Automated Planning and Scheduling","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129731468","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 : 2022-06-13DOI: 10.1609/icaps.v32i1.19800
Michael Katz, Shirin Sohrabi
Top-quality planning in general and quotient top-quality planning in particular deal with producing multiple high-quality plans while allowing for their efficient generation, skipping equivalent ones. Prior work has explored one equivalence relation, considering two plans to be equivalent if their operator multi-sets are equal. This allowed omitting plans that are reorderings of previously found ones. However, the resulting sets of plans were still large, in some domains even infinite. In this paper, we consider a different relation: two plans are related if one's operator multiset is a subset of the other's. We propose novel reformulations that forbid plans that are related to the given ones. While the new relation is not transitive and thus not an equivalence relation, we can define a new subset top-quality planning problem, with finite size solution sets. We formally prove that these solutions can be obtained by exploiting the proposed reformulations. Our empirical evaluation shows that solutions to the new problem can be found for more tasks than unordered top-quality planning solutions. Further, the results shows that the solution sizes significantly decrease, making the new approach more practical, particularly in domains with redundant operators.
{"title":"Who Needs These Operators Anyway: Top Quality Planning with Operator Subset Criteria","authors":"Michael Katz, Shirin Sohrabi","doi":"10.1609/icaps.v32i1.19800","DOIUrl":"https://doi.org/10.1609/icaps.v32i1.19800","url":null,"abstract":"Top-quality planning in general and quotient top-quality planning in particular deal with producing multiple high-quality plans while allowing for their efficient generation, skipping equivalent ones. Prior work has explored one equivalence relation, considering two plans to be equivalent if their operator multi-sets are equal. This allowed omitting plans that are reorderings of previously found ones. However, the resulting sets of plans were still large, in some domains even infinite.\u0000In this paper, we consider a different relation: two plans are related if one's operator multiset is a subset of the other's. We propose novel reformulations that forbid plans that are related to the given ones. While the new relation is not transitive and thus not an equivalence relation, we can define a new subset top-quality planning problem, with finite size solution sets. We formally prove that these solutions can be obtained by exploiting the proposed reformulations. Our empirical evaluation shows that solutions to the new problem can be found for more tasks than unordered top-quality planning solutions. Further, the results shows that the solution sizes significantly decrease, making the new approach more practical, particularly in domains with redundant operators.","PeriodicalId":239898,"journal":{"name":"International Conference on Automated Planning and Scheduling","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131161197","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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