Symposium on Combinatorial Search最新文献_第7页

Searching with Distributional Heuristics (Student Abstract) 基于分布启发式的搜索(学生摘要)

Symposium on Combinatorial Search Pub Date : 2023-07-02 DOI: 10.1609/socs.v16i1.27317

Stephen Wissow

{"title":"Searching with Distributional Heuristics (Student Abstract)","authors":"Stephen Wissow","doi":"10.1609/socs.v16i1.27317","DOIUrl":"https://doi.org/10.1609/socs.v16i1.27317","url":null,"abstract":"Distributional heuristic search uses distributions rather than point values for its heuristic estimates of the cost to goal from any given state. Distributional heuristics are desirable as they provide search algorithms not only with a way to evaluate nodes, but also with a basis for rational decision making tailored to specific search settings. Bounded suboptimal, anytime, and contract searches have differing but related objectives that each lend themselves to probabilistic reasoning supported by distributional heuristics. In many applications, speed of planning can be more important than solution quality. Whether due to certain domains' inherent difficulty, where anything but a satisficing approach is infeasible due to time or memory constraints, or due to the limited planning time available in real-time robotics and other time-sensitive planning settings, important open questions are how best to find solutions as quickly as possible and how to find the best solution possible while subject to an explicit limit on planning time. Successful algorithms must reason not only about solution cost, possibly in relation to a suboptimality bound, but also about the relative likelihood of finding a solution under one node vs. under another, of finding a solution of a particular cost (such as in relation to that of an incumbent solution), or about the expected amount of search effort to find a goal under a given node. This dissertation takes up these issues in four parts. I (1) examine different methods for generating distributional heuristics in bounded cost heuristic search and classical planning; (2) study the contract search setting, which involves online estimation of several unknown values; (3) consider the bounded suboptimal setting; and (4) address the anytime setting.","PeriodicalId":425645,"journal":{"name":"Symposium on Combinatorial Search","volume":"2012 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131937865","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

Improvements to CPCES CPCES的改进

Symposium on Combinatorial Search Pub Date : 2023-07-02 DOI: 10.1609/socs.v16i1.27289

Xiaodi Zhang, Alban Grastien

引用次数: 0

Towards Effective Multi-Valued Heuristics for Bi-objective Shortest-Path Algorithms via Differential Heuristics 基于差分启发式的双目标最短路径算法的有效多值启发式

Symposium on Combinatorial Search Pub Date : 2023-07-02 DOI: 10.1609/socs.v16i1.27288

Han Zhang, Oren Salzman, Ariel Felner, T. K. S. Kumar, Shawn Skyler, Carlos Hernández Ulloa, Sven Koenig

{"title":"Towards Effective Multi-Valued Heuristics for Bi-objective Shortest-Path Algorithms via Differential Heuristics","authors":"Han Zhang, Oren Salzman, Ariel Felner, T. K. S. Kumar, Shawn Skyler, Carlos Hernández Ulloa, Sven Koenig","doi":"10.1609/socs.v16i1.27288","DOIUrl":"https://doi.org/10.1609/socs.v16i1.27288","url":null,"abstract":"In bi-objective graph search, each edge is annotated with a cost pair, where each cost corresponds to an objective to optimize. We are interested in finding all undominated paths from a given start state to a given goal state (called the Pareto front). Almost all existing works of bi-objective search use single-valued heuristics, which use one number for each objective, to estimate the cost between any given state and the goal state. However, single-valued heuristics cannot reflect the trade-offs between the two costs. On the other hand, multi-valued heuristics use a set of pairs to estimate the Pareto front between any given state and the goal state and are more informed than single-valued heuristics. However, they are rarely studied and have yet to be investigated in explicit state spaces by any existing work. In this paper, we are interested in using multi-valued heuristics to improve bi-objective search algorithms in explicit state spaces. More specifically, we generalize Differential Heuristics (DHs), a class of memory-based heuristics for single-objective search, to bi-objective search, resulting in Bi-objective Differential Heuristics (BO-DHs). We propose several techniques to reduce the memory usage and computational overhead of BO-DHs significantly. Our experimental results show that, with suggested improvement and tuned parameters, BO-DHs can reduce the node expansion and runtime of a bi-objective search algorithm by up to an order of magnitude, paving the way for more effective multi-valued heuristics.","PeriodicalId":425645,"journal":{"name":"Symposium on Combinatorial Search","volume":"347 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132540160","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

Generating SAS+ Planning Tasks of Specified Causal Structure 生成SAS+指定因果结构的规划任务

Symposium on Combinatorial Search Pub Date : 2023-07-02 DOI: 10.1609/socs.v16i1.27280

Michael Katz, Junkyu Lee, Shirin Sohrabi

{"title":"Generating SAS+ Planning Tasks of Specified Causal Structure","authors":"Michael Katz, Junkyu Lee, Shirin Sohrabi","doi":"10.1609/socs.v16i1.27280","DOIUrl":"https://doi.org/10.1609/socs.v16i1.27280","url":null,"abstract":"Recent advances in data-driven approaches in AI planning demand more and more planning tasks. The supply, however, is somewhat limited. Past International Planning Competitions (IPCs) have introduced the de-facto standard benchmarks with the domains written by domain experts. The few existing methods for sampling random planning tasks severely limit the resulting problem structure. In this work we show a method for generating planning tasks of any requested causal graph structure, alleviating the shortage in existing planning benchmarks. We present an algorithm for constructing random SAS+ planning tasks given an arbitrary causal graph and offer random task generators for the well-explored causal graph structures in the planning literature. We further allow to generate a planning task equivalent in causal structure to an input SAS+ planning task. We generate two benchmark sets: 26 collections for select well-explored causal graph structures and 42 collections for existing IPC domains. We evaluate both benchmark sets with the state-of-the-art optimal planners, showing the adequacy for adopting them as benchmarks in cost-optimal classical planning. The benchmark sets and the task generator code are publicly available at https://github.com/IBM/fdr-generator.","PeriodicalId":425645,"journal":{"name":"Symposium on Combinatorial Search","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130073076","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

K∗ and Partial Order Reduction for Top-Quality Planning 最优规划的K *与偏阶约简

Symposium on Combinatorial Search Pub Date : 2023-07-02 DOI: 10.1609/socs.v16i1.27293

Michael Katz, Junkyu Lee

引用次数: 1

SAT Feature Analysis for Machine Learning Classification Tasks 机器学习分类任务的SAT特征分析

Symposium on Combinatorial Search Pub Date : 2023-07-02 DOI: 10.1609/socs.v16i1.27292

Marco Dalla, Benjamin Provan-Bessell, Andrea Visentin, B. O’Sullivan

{"title":"SAT Feature Analysis for Machine Learning Classification Tasks","authors":"Marco Dalla, Benjamin Provan-Bessell, Andrea Visentin, B. O’Sullivan","doi":"10.1609/socs.v16i1.27292","DOIUrl":"https://doi.org/10.1609/socs.v16i1.27292","url":null,"abstract":"The extraction of meaningful features from CNF instances is crucial to applying machine learning to SAT solving, enabling algorithm selection and configuration for solver portfolios and satisfiability classification. While many approaches have been proposed for feature extraction, their relevance to these tasks is unclear. Their applicability and comparison of the information extracted and the computational effort needed are complicated by the lack of working or updated implementations, negatively affecting reproducibility. \u0000In this paper, we analyse the performance of five sets of features presented in the literature on SAT/UNSAT and problem category classification over a dataset of 3000 instances across ten problem classes distributed equally between SAT and UNSAT. To increase reproducibility and encourage research in this area, we released a Python library containing an updated and clear implementation of structural, graph-based, statistical and probing features presented in the literature for SAT CNF instances; and we define a clear pipeline to compare feature sets in a given learning task robustly.\u0000We analysed which of the computed features are relevant for the specific task and the tradeoff they provide between accuracy and computational effort. The results of the analysis provide insights into which features mostly affect an instance's satisfiability and which can be used to identify the problem's type. These insights can be used to develop more effective solver portfolios and satisfiability classification algorithms.","PeriodicalId":425645,"journal":{"name":"Symposium on Combinatorial Search","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121108094","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

Complete Search of Sliding Tile Puzzles on a Personal Computer [Extended Abstract] 在个人计算机上完全搜索滑动瓷砖拼图[扩展摘要]

Symposium on Combinatorial Search Pub Date : 2023-07-02 DOI: 10.1609/socs.v16i1.27307

O. Tarakanov

引用次数: 0

Using Machine Learning Classifiers in SAT Branching [Extended Abstract] 机器学习分类器在SAT分支中的应用[扩展摘要]

Symposium on Combinatorial Search Pub Date : 2023-07-02 DOI: 10.1609/socs.v16i1.27298

Ruth Helen Bergin, Marco Dalla, Andrea Visentin, B. O’Sullivan, G. Provan

引用次数: 0

GePA*SE: Generalized Edge-Based Parallel A* for Slow Evaluations GePA*SE:基于广义边的并行A*算法

Symposium on Combinatorial Search Pub Date : 2023-01-24 DOI: 10.48550/arXiv.2301.10347

Shohin Mukherjee, M. Likhachev

引用次数: 1

On the Reformulation of Discretised PDDL+ to Numeric Planning (Extended Abstract) 离散PDDL+到数值规划的再表述(扩展摘要)

Symposium on Combinatorial Search Pub Date : 2022-07-17 DOI: 10.1609/socs.v15i1.21797

Francesco Percassi, Enrico Scala, M. Vallati

引用次数: 0