Symposium on Combinatorial Search最新文献

A Deterministic Search Approach for Solving Stochastic Drone Search and Rescue Planning Without Communications 解决无通信随机无人机搜救规划的确定性搜索方法

Symposium on Combinatorial Search Pub Date : 2024-06-01 DOI: 10.1609/socs.v17i1.31544

Evgeny Mishlyakov, Mikhail Gruntov, Alexander Shleyfman, E. Karpas

{"title":"A Deterministic Search Approach for Solving Stochastic Drone Search and Rescue Planning Without Communications","authors":"Evgeny Mishlyakov, Mikhail Gruntov, Alexander Shleyfman, E. Karpas","doi":"10.1609/socs.v17i1.31544","DOIUrl":"https://doi.org/10.1609/socs.v17i1.31544","url":null,"abstract":"In disaster relief efforts, delivering aid to areas with no communication poses a significant challenge. Unmanned aerial vehicles (UAVs) can be utilized to deliver aid kits to survivors in hard-to-reach areas; unfortunately, in some areas, lack of communication and infrastructure presents a key problem. \u0000In this paper, we address a stochastic planning problem of planning for a set of UAVs that deliver aid kits to areas that lack communications, where we do not know in advance the locations where aid kits need to be delivered, but rather have probabilistic information about the locations of aid targets.\u0000Our main insight is that, despite the stochastic nature of this problem, we can solve it through deterministic search by monitoring the expected reward for each partial solution. This insight enables the application of deterministic planning techniques, empirically demonstrating a notable improvement in efficiency and response speed. Our approach presents a promising solution to addressing the challenge of delivering aid in regions with limited radio infrastructure, as well as similar planning problems.","PeriodicalId":425645,"journal":{"name":"Symposium on Combinatorial Search","volume":"51 24","pages":"73-81"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141277175","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

Evaluating Distributional Predictions of Search Time: Put Up or Shut Up Games (Extended Abstract) 评估搜索时间的分布预测：闭嘴或闭嘴游戏（扩展摘要）

Symposium on Combinatorial Search Pub Date : 2024-06-01 DOI: 10.1609/socs.v17i1.31579

Sean Mariasin, Andrew Coles, E. Karpas, Wheeler Ruml, S. E. Shimony, Shahaf S. Shperberg

引用次数: 0

Neural Sequence Generation with Constraints via Beam Search with Cuts: A Case Study on VRP 通过带切口的光束搜索生成带约束条件的神经序列：VRP 案例研究

Symposium on Combinatorial Search Pub Date : 2024-06-01 DOI: 10.1609/socs.v17i1.31549

Pouya Shati, Eldan Cohen, Sheila A. McIlraith

{"title":"Neural Sequence Generation with Constraints via Beam Search with Cuts: A Case Study on VRP","authors":"Pouya Shati, Eldan Cohen, Sheila A. McIlraith","doi":"10.1609/socs.v17i1.31549","DOIUrl":"https://doi.org/10.1609/socs.v17i1.31549","url":null,"abstract":"In recent years, neural sequence models have been applied successfully to solve combinatorial optimization problems. Solutions, encoded as sequences, are typically generated from trained models via beam search, a search algorithm that generates sequences token-by-token while keeping a fixed number of promising partial solutions at each step. In this paper, we explore the problem of augmenting beam search generation with the enforcement of requirements---hard constraints that any generated solution must adhere to. We propose a hybrid approach, by encoding the requirements in the form of a constraint satisfaction problem (CSP) and iteratively solving the CSP to cut any partial solution within the beam search that is incapable of satisfying the requirements. We study this problem in the context of vehicle routing problems (VRP) further augmented with capacity-related or temporal requirements. We experimentally show that cuts often allow us to satisfy the requirements with negligible impact on solution quality. Without the use of cuts, beam search is shown to be exponentially less likely to satisfy the requirements as the length of the solution increases and/or the requirements are strengthened.","PeriodicalId":425645,"journal":{"name":"Symposium on Combinatorial Search","volume":"5 11","pages":"118-126"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141277714","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

Finding a Small, Diverse Subset of the Pareto Solution Set in Bi-Objective Search (Extended Abstract) 在双目标搜索中寻找帕累托解决方案集的小型多样化子集（扩展摘要）

Symposium on Combinatorial Search Pub Date : 2024-06-01 DOI: 10.1609/socs.v17i1.31568

Pablo Araneda, Carlos Hernández Ulloa, Nicolás Rivera, Jorge A. Baier

引用次数: 0

A-A*pex: Efficient Anytime Approximate Multi-Objective Search A-A*pex：高效的随时近似多目标搜索

Symposium on Combinatorial Search Pub Date : 2024-06-01 DOI: 10.1609/socs.v17i1.31556

Han Zhang, Oren Salzman, Ariel Felner, Carlos Hernández Ulloa, Sven Koenig

{"title":"A-A*pex: Efficient Anytime Approximate Multi-Objective Search","authors":"Han Zhang, Oren Salzman, Ariel Felner, Carlos Hernández Ulloa, Sven Koenig","doi":"10.1609/socs.v17i1.31556","DOIUrl":"https://doi.org/10.1609/socs.v17i1.31556","url":null,"abstract":"In the multi-objective search problem, a typical task is to compute the Pareto frontier, i.e., the set of all undominated solutions. However, computing the entire Pareto frontier can be very time-consuming, and in practice, we often have limited deliberation time. Therefore, this paper focuses on solving the multi-objective search problem with anytime algorithms, which compute an initial approximate frontier quickly and then work to find more solutions until eventually finding the entire Pareto frontier. Existing work has investigated such anytime algorithms for problem instances with only two objectives. In this paper, we propose Anytime A*pex (A-A*pex), which works with any number of objectives. In each iteration of A-A*pex, it runs A*pex, a state-of-the-art approximate multi-objective search algorithm, to compute more solutions. From one iteration to the next, A-A*pex can either reuse its previous search effort or restart from scratch. Our experimental results show that an A-A*pex variant that mixes\u0000reusing its search effort and restarting from scratch yields the best runtime performance. We also show that A-A*pex often computes solutions that collectively approximate the Pareto frontier much better than the solutions found by state-of-the-art multi-objective search algorithms for short deliberation times.","PeriodicalId":425645,"journal":{"name":"Symposium on Combinatorial Search","volume":"37 2","pages":"179-187"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141274141","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

Finiding All Optimal Solutions in Multi-Agent Path Finding (Extended Abstract) 在多代理路径查找中完成所有最优解（扩展摘要）

Symposium on Combinatorial Search Pub Date : 2024-06-01 DOI: 10.1609/socs.v17i1.31570

Shahar Bardugo, Dor Atzmon

引用次数: 0

Planning and Exection in Multi-Agent Path Finding: Models and Algorithms (Extended Abstract) 多代理路径查找中的规划与检测：模型与算法（扩展摘要）

Symposium on Combinatorial Search Pub Date : 2024-06-01 DOI: 10.1609/socs.v17i1.31592

Yue Zhang, Zhe Chen, Daniel D. Harabor, P. L. Bodic, P. Stuckey

引用次数: 0

Solving Facility Location Problems via FastMap and Locality Sensitive Hashing 通过 FastMap 和位置敏感哈希算法解决设施定位问题

Symposium on Combinatorial Search Pub Date : 2024-06-01 DOI: 10.1609/socs.v17i1.31541

Ang Li, P. Stuckey, Sven Koenig, T. K. S. Kumar

{"title":"Solving Facility Location Problems via FastMap and Locality Sensitive Hashing","authors":"Ang Li, P. Stuckey, Sven Koenig, T. K. S. Kumar","doi":"10.1609/socs.v17i1.31541","DOIUrl":"https://doi.org/10.1609/socs.v17i1.31541","url":null,"abstract":"Facility Location Problems (FLPs) arise while serving multiple customers in a shared environment, minimizing transportation and other costs. Hence, they involve the optimal placement of facilities. They are defined on graphs as well as in Euclidean spaces with or without obstacles; and they are typically NP-hard to solve optimally. There are many heuristic algorithms tailored to different kinds of FLPs. However, FLPs defined in Euclidean spaces without obstacles are the most amenable to efficient and effective heuristic algorithms. This motivates the idea of quickly reformulating FLPs on graphs and in Euclidean spaces with obstacles to FLPs in Euclidean spaces without obstacles. Towards this end, we propose a new approach that uses FastMap and Locality Sensitive Hashing. FastMap is a near-linear-time algorithm that embeds the vertices of a graph in a Euclidean space while approximately preserving graph-based distances as Euclidean distances for all pairs of vertices. Through extensive experiments, we show that our approach significantly outperforms other state-of-the-art competing algorithms on a variety of FLPs: the Multi-Agent Meeting, Vertex K-Median (VKM), Weighted VKM, and the Capacitated VKM problems.","PeriodicalId":425645,"journal":{"name":"Symposium on Combinatorial Search","volume":"13 1","pages":"46-54"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141279657","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

Parallelizing Multi-objective A* Search (Extended Abstract) 并行化多目标 A* 搜索（扩展摘要）

Symposium on Combinatorial Search Pub Date : 2024-06-01 DOI: 10.1609/socs.v17i1.31567

Saman Ahmadi

引用次数: 0

Hitting Set Heuristics for Overlapping Landmarks in Satisficing Planning 满足规划中重叠地标的命中集启发式算法

Symposium on Combinatorial Search Pub Date : 2024-06-01 DOI: 10.1609/socs.v17i1.31558

Clemens Büchner, Remo Christen, Salomé Eriksson, Thomas Keller

引用次数: 0