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Multi-Train Path Finding Revisited 多列列车寻径重访
Symposium on Combinatorial Search Pub Date : 2022-07-17 DOI: 10.1609/socs.v15i1.21750
Zhe Chen, Jiaoyang Li, Daniel D. Harabor, P. J. Stuckey, Sven Koenig
{"title":"Multi-Train Path Finding Revisited","authors":"Zhe Chen, Jiaoyang Li, Daniel D. Harabor, P. J. Stuckey, Sven Koenig","doi":"10.1609/socs.v15i1.21750","DOIUrl":"https://doi.org/10.1609/socs.v15i1.21750","url":null,"abstract":"Multi-Train Path Finding (MTPF) is a coordination problem that asks us to plan collision-free paths for a team of moving agents, where each agent occupies a sequence of locations at any given time. MTPF is useful for planning a range of real-world vehicles, including rail trains and road convoys. MTPF is closely related to another coordination problem known as k-Robust Multi-Agent Path Finding (kR-MAPF). Although similar in principle, the performance of optimal MTPF algorithms in practice lags far behind that of optimal kR-MAPF algorithms. In this work, we revisit the connection between them and reduce the performance gap. First, we show that, in\u0000many cases, a valid kR-MAPF plan is also a valid MTPF plan, which leads to a new and faster approach for collision resolution. We also show that many recently introduced improvements for kR-MAPF, such as lower-bounding heuristics and symmetry reasoning, can be extended to MTPF. Finally, we explore a new type of pairwise symmetry specific to MTPF. Our experiments show that these improvements yield large efficiency gains for optimal MTPF.","PeriodicalId":425645,"journal":{"name":"Symposium on Combinatorial Search","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116070096","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
On Bidirectional Heuristic Search in Classical Planning: An Analysis of BAE 经典规划中的双向启发式搜索:BAE分析
Symposium on Combinatorial Search Pub Date : 2022-07-17 DOI: 10.1609/socs.v15i1.21756
Kilian Hu, David Speck
{"title":"On Bidirectional Heuristic Search in Classical Planning: An Analysis of BAE","authors":"Kilian Hu, David Speck","doi":"10.1609/socs.v15i1.21756","DOIUrl":"https://doi.org/10.1609/socs.v15i1.21756","url":null,"abstract":"Heuristic search is a successful approach to cost-optimal planning. Bidirectional heuristic search algorithms have been around for a long time, but only recent advances have led to algorithms like BAE* that have the potential to outperform unidirectional heuristic search algorithms like A* in practice. In this work, we analyze BAE* for classical planning and the challenges associated with the underlying assumption of an explicit state representation. We show that it is crucial to use mutexes and reachability analysis to reduce the potentially exponential number of goal states, which makes it possible to create an explicit representation of a reversed planning task that can be used for the backward search of BAE*. Our empirical evaluation shows that BAE* solves more instances than A* in multiple domains with significantly fewer node expansions, demonstrating the usefulness of BAE* in planning.","PeriodicalId":425645,"journal":{"name":"Symposium on Combinatorial Search","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128240932","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
Weight Constrained Path Finding with Bidirectional A 双向A的权约束寻径
Symposium on Combinatorial Search Pub Date : 2022-07-17 DOI: 10.1609/socs.v15i1.21746
Saman Ahmadi, Guido Tack, Daniel D. Harabor, P. Kilby
{"title":"Weight Constrained Path Finding with Bidirectional A","authors":"Saman Ahmadi, Guido Tack, Daniel D. Harabor, P. Kilby","doi":"10.1609/socs.v15i1.21746","DOIUrl":"https://doi.org/10.1609/socs.v15i1.21746","url":null,"abstract":"Weight constrained path finding, known as a challenging variant of the classic shortest path problem, aims to plan cost optimum paths whose weight/resource usage is limited by a side constraint. Given the bi-criteria nature of the problem (i.e., the presence of cost and weight), solutions to the Weight Constrained Shortest Path Problem (WCSPP) have some properties in common with bi-objective search. This paper leverages the state-of-the-art bi-objective search algorithm BOBA* and presents WC-BA*, an exact A*-based WCSPP method that explores the search space in different objective orderings bidirectionally. We also enrich WC-BA* with two novel heuristic tuning approaches that can significantly reduce the number of node expansions in the exhaustive search of A*. The results of our experiments on a large set of realistic problem instances show that our new algorithm solves all instances and outperforms the state-of-the-art WCSPP algorithms in various scenarios.","PeriodicalId":425645,"journal":{"name":"Symposium on Combinatorial Search","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130289519","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
Joint Chance Constrained Probabilistic Simple Temporal Networks via Column Generation (Extended Abstract) 基于列生成的联合机会约束概率简单时态网络(扩展摘要)
Symposium on Combinatorial Search Pub Date : 2022-07-17 DOI: 10.1609/socs.v15i1.21794
Andrew Murray, Michael Cashmore, A. Arulselvan, J. Frank
{"title":"Joint Chance Constrained Probabilistic Simple Temporal Networks via Column Generation (Extended Abstract)","authors":"Andrew Murray, Michael Cashmore, A. Arulselvan, J. Frank","doi":"10.1609/socs.v15i1.21794","DOIUrl":"https://doi.org/10.1609/socs.v15i1.21794","url":null,"abstract":"Probabilistic Simple Temporal Networks (PSTN) are used to represent scheduling problems under uncertainty. In a temporal network that is Strongly Controllable (SC) there exists a concrete schedule that is robust to any uncertainty. We solve the problem of determining Chance Constrained PSTN SC as a Joint Chance Constrained optimisation problem via column generation, lifting the usual assumptions of independence and Boole's inequality typically leveraged in PSTN literature. Our approach offers on average a 10 times reduction in cost versus previous methods.","PeriodicalId":425645,"journal":{"name":"Symposium on Combinatorial Search","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121168725","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
MA3: Model-Accuracy Aware Anytime Planning with Simulation Verification for Navigating Complex Terrains MA3:模型精度感知随时规划与仿真验证导航复杂地形
Symposium on Combinatorial Search Pub Date : 2022-07-17 DOI: 10.1609/socs.v15i1.21753
M. Das, D. Conover, Sungmin Eum, H. Kwon, M. Likhachev
{"title":"MA3: Model-Accuracy Aware Anytime Planning with Simulation Verification for Navigating Complex Terrains","authors":"M. Das, D. Conover, Sungmin Eum, H. Kwon, M. Likhachev","doi":"10.1609/socs.v15i1.21753","DOIUrl":"https://doi.org/10.1609/socs.v15i1.21753","url":null,"abstract":"Off-road and unstructured environments often contain complex patches of various types of terrain, rough elevation changes, deformable objects, etc. An autonomous ground vehicle traversing such environments experiences physical interactions that are extremely hard to model at scale and thus very hard to predict. Nevertheless, planning a safely traversable path through such an environment requires the ability to predict the outcomes of these interactions instead of avoiding them. One approach to doing this is to learn the interaction model offline based on collected data. Unfortunately, though, this requires large amounts of data and can often be brittle. Alternatively, models using physics-based simulators can generate large data and provide a reliable prediction. However, they are very slow to query online within the planning loop. This work proposes an algorithmic framework that utilizes the combination of a learned model and a physics-based simulation model for fast planning. Specifically, it uses the learned model as much as possible to accelerate planning while sparsely using the physics-based simulator to verify the feasibility of the planned path. We provide a theoretical analysis of the algorithm and its empirical evaluation showing a significant reduction in planning times.","PeriodicalId":425645,"journal":{"name":"Symposium on Combinatorial Search","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121401264","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
Additive Pattern Databases for Decoupled Search 解耦搜索的加性模式数据库
Symposium on Combinatorial Search Pub Date : 2022-07-17 DOI: 10.1609/socs.v15i1.21766
Silvan Sievers, Daniel Gnad, Á. Torralba
{"title":"Additive Pattern Databases for Decoupled Search","authors":"Silvan Sievers, Daniel Gnad, Á. Torralba","doi":"10.1609/socs.v15i1.21766","DOIUrl":"https://doi.org/10.1609/socs.v15i1.21766","url":null,"abstract":"Abstraction heuristics are the state of the art in optimal classical planning as\u0000heuristic search. Despite their success for explicit-state search, though,\u0000abstraction heuristics are not available for decoupled state-space search, an\u0000orthogonal reduction technique that can lead to exponential savings by decomposing\u0000planning tasks. In this paper, we show how to compute pattern database (PDB)\u0000heuristics for decoupled states. The main challenge lies in how to additively employ\u0000multiple patterns, which is crucial for strong search guidance of the heuristics. We\u0000show that in the general case, for arbitrary collections of PDBs, computing the\u0000heuristic for a decoupled state is exponential in the number of leaf components of\u0000decoupled search. We derive several variants of decoupled PDB heuristics that allow\u0000to additively combine PDBs avoiding this blow-up and evaluate them empirically.","PeriodicalId":425645,"journal":{"name":"Symposium on Combinatorial Search","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127535763","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
Heuristic Search for SSPs with Lexicographic Preferences over Multiple Costs 具有多重成本的词典偏好的ssp启发式搜索
Symposium on Combinatorial Search Pub Date : 2022-07-17 DOI: 10.1609/socs.v15i1.21760
Shuwa Miura, K. H. Wray, S. Zilberstein
{"title":"Heuristic Search for SSPs with Lexicographic Preferences over Multiple Costs","authors":"Shuwa Miura, K. H. Wray, S. Zilberstein","doi":"10.1609/socs.v15i1.21760","DOIUrl":"https://doi.org/10.1609/socs.v15i1.21760","url":null,"abstract":"Real-world decision problems often involve multiple competing objectives. The Stochastic Shortest Path (SSP) with lexicographic preferences over multiple costs offers an expressive formulation for many practical problems. However, the existing solution methods either lack optimality guarantees or require costly computations over the entire state space. We propose the first heuristic algorithm for this problem, based on the heuristic algorithm for Constrained SSPs. Our experiments show that our heuristic search algorithm can compute optimal policies while avoiding a large portion of the state space. We further analyze the theoretical properties of the problem, showing the conditions under which SSPs with lexicographic preferences have a proper optimal policy.","PeriodicalId":425645,"journal":{"name":"Symposium on Combinatorial Search","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126871802","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
Urban Traffic Control via Planning with Global State Constraints (Extended Abstract) 基于全局状态约束的城市交通规划控制(扩展摘要)
Symposium on Combinatorial Search Pub Date : 2022-07-17 DOI: 10.1609/socs.v15i1.21789
Franc Ivankovic, M. Vallati, L. Chrpa, M. Roveri
{"title":"Urban Traffic Control via Planning with Global State Constraints (Extended Abstract)","authors":"Franc Ivankovic, M. Vallati, L. Chrpa, M. Roveri","doi":"10.1609/socs.v15i1.21789","DOIUrl":"https://doi.org/10.1609/socs.v15i1.21789","url":null,"abstract":"Planning with global state constraints is an extension of classical planning such that some properties of each state are derived via a set of rules common to all states. This approach is important for the application of planning techniques in manipulating cyber-physical systems, and has been shown to be effective in practice. Urban Traffic Control (UTC) deals with the control and management of traffic in urban regions, and includes the optimisation of traffic signals configuration to minimise traffic congestion and travel delays. In this paper, we briefly introduce how to cast the UTC problem into the formalism of planning with global state constraints, and we perform a preliminary experimental evaluation considering significant scenarios taken from the literature, and a new one based on real-world data. The results show that the approach is feasible, and the quality of generated solutions has been confirmed in simulation using existing symbolic models.","PeriodicalId":425645,"journal":{"name":"Symposium on Combinatorial Search","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124607735","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
IPO-MAXSAT: Combining the In-Parameter-Order Strategy for Covering Array Generation with MaxSAT Solving (Extended Abstract) IPO-MAXSAT:结合参数阶策略的覆盖阵列生成与MaxSAT求解(扩展摘要)
Symposium on Combinatorial Search Pub Date : 2022-07-17 DOI: 10.1609/socs.v15i1.21788
Irene Hiess, Ludwig Kampel, Michael Wagner, D. Simos
{"title":"IPO-MAXSAT: Combining the In-Parameter-Order Strategy for Covering Array Generation with MaxSAT Solving (Extended Abstract)","authors":"Irene Hiess, Ludwig Kampel, Michael Wagner, D. Simos","doi":"10.1609/socs.v15i1.21788","DOIUrl":"https://doi.org/10.1609/socs.v15i1.21788","url":null,"abstract":"Covering arrays (CAs) are discrete objects appearing in combinatorial design theory that find practical applications, most prominently in software testing. The generation of optimized CAs is a difficult combinatorial optimization problem being subject to ongoing research. Previous studies have shown that many different algorithmic approaches are best suited for different instances of CAs. In this extended abstract we describe the IPO-MAXSAT algorithm, which adopts the prominent IPO strategy for CA generation and uses MaxSAT solving\u0000to optimize the occurring sub-problems.","PeriodicalId":425645,"journal":{"name":"Symposium on Combinatorial Search","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133866721","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
Mutex Propagation in Multi-Agent Path Finding for Large Agents 大型智能体多智能体寻径中的互斥体传播
Symposium on Combinatorial Search Pub Date : 2022-07-17 DOI: 10.1609/socs.v15i1.21776
Han Zhang, Yutong Li, Jiaoyang Li, T. K. S. Kumar, Sven Koenig
{"title":"Mutex Propagation in Multi-Agent Path Finding for Large Agents","authors":"Han Zhang, Yutong Li, Jiaoyang Li, T. K. S. Kumar, Sven Koenig","doi":"10.1609/socs.v15i1.21776","DOIUrl":"https://doi.org/10.1609/socs.v15i1.21776","url":null,"abstract":"Mutex propagation and its concomitant symmetry-breaking techniques have proven useful in Multi-Agent Path Finding (MAPF) with point agents. In this paper, we show that they can be easily generalized to richer MAPF problems. In particular, we demonstrate their application to MAPF with ``Large'' Agents (LA-MAPF). Here, agents can occupy multiple points at the same time according to their fixed shapes and sizes. While existing rule-based symmetry-breaking techniques are difficult to generalize from point agents to large agents, mutex-based symmetry-breaking techniques can be generalized easily. In a Conflict-Based Search (CBS) framework for LA-MAPF, we also develop a mutex-based conflict-selection strategy to further enhance the efficiency of the search. Through experiments on various maps, we show that our techniques significantly improve MC-CBS, a state-of-the-art optimal LA-MAPF algorithm, in terms of both success rate and runtime.","PeriodicalId":425645,"journal":{"name":"Symposium on Combinatorial Search","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116249646","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
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