International Conference on Automated Planning and Scheduling最新文献_第9页

International Conference on Automated Planning and Scheduling Pub Date : 2022-06-13 DOI: 10.1609/icaps.v32i1.19823

Julian von Tschammer, Robert Mattmüller, David Speck

引用次数: 5

Multi-Agent Tree Search with Dynamic Reward Shaping 动态奖励形成的多智能体树搜索

International Conference on Automated Planning and Scheduling Pub Date : 2022-06-13 DOI: 10.1609/icaps.v32i1.19854

Alvaro Velasquez, Brett Bissey, Lior Barak, Daniel Melcer, Andre Beckus, Ismail R. Alkhouri, George K. Atia

{"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}

引用次数: 1

Biased Exploration for Satisficing Heuristic Search 满足启发式搜索的偏差探索

International Conference on Automated Planning and Scheduling Pub Date : 2022-06-13 DOI: 10.1609/icaps.v32i1.19804

Ryo Kuroiwa, J. Christopher Beck

{"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}

引用次数: 3

Joint Pricing and Matching for City-Scale Ride-Pooling 城市规模拼车的联合定价与匹配

International Conference on Automated Planning and Scheduling Pub Date : 2022-06-13 DOI: 10.1609/icaps.v32i1.19836

Sanket Shah, Meghna Lowalekar, Pradeep Varakantham

引用次数: 1

Talking Trucks: Decentralized Collaborative Multi-Agent Order Scheduling for Self-Organizing Logistics 会说话的卡车:自组织物流的分散协作多智能体订单调度

International Conference on Automated Planning and Scheduling Pub Date : 2022-06-13 DOI: 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

{"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}

引用次数: 0

On the Complexity of Heuristic Synthesis for Satisficing Classical Planning: Potential Heuristics and Beyond 论满足经典规划的启发式综合的复杂性:潜在启发式及超越

International Conference on Automated Planning and Scheduling Pub Date : 2022-06-13 DOI: 10.1609/icaps.v32i1.19793

M. Helmert, Silvan Sievers, Alexander Rovner, Augusto B. Corrêa

引用次数: 1

Stochastic Resource Optimization over Heterogeneous Graph Neural Networks for Failure-Predictive Maintenance Scheduling 基于异构图神经网络的故障预测维修调度随机资源优化

International Conference on Automated Planning and Scheduling Pub Date : 2022-06-13 DOI: 10.1609/icaps.v32i1.19839

Zheyuan Wang, M. Gombolay

{"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}

引用次数: 1

Cost Partitioning Heuristics for Stochastic Shortest Path Problems 随机最短路径问题的代价划分启发式算法

International Conference on Automated Planning and Scheduling Pub Date : 2022-06-13 DOI: 10.1609/icaps.v32i1.19802

Thorsten Klößner, F. Pommerening, Thomas Keller, G. Röger

引用次数: 1

OFFICERS: Operational Framework for Intelligent Crime-and-Emergency Response Scheduling 官员:智能犯罪和应急响应调度的操作框架

International Conference on Automated Planning and Scheduling Pub Date : 2022-06-13 DOI: 10.1609/icaps.v32i1.19830

Jonathan Chase, Siong Thye Goh, T. Phong, H. Lau

{"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}

引用次数: 0

Who Needs These Operators Anyway: Top Quality Planning with Operator Subset Criteria 谁需要这些算子:算子子集标准的高质量规划

International Conference on Automated Planning and Scheduling Pub Date : 2022-06-13 DOI: 10.1609/icaps.v32i1.19800

Michael Katz, Shirin Sohrabi

{"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}

引用次数: 4