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International Conference on Artificial Intelligence Planning Systems最新文献

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Plan Representation for Robotic Agents 机器人代理的计划表示
International Conference on Artificial Intelligence Planning Systems Pub Date : 2002-04-23 DOI: 10.1007/3-540-36381-5_3
M. Beetz
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引用次数: 16
CaMeL: Learning Method Preconditions for HTN Planning 骆驼:HTN规划的学习方法前提
International Conference on Artificial Intelligence Planning Systems Pub Date : 2002-04-23 DOI: 10.21236/ada448055
O. Ilghami, Dana S. Nau, Hector Muñoz-Avila, D. Aha
{"title":"CaMeL: Learning Method Preconditions for HTN Planning","authors":"O. Ilghami, Dana S. Nau, Hector Muñoz-Avila, D. Aha","doi":"10.21236/ada448055","DOIUrl":"https://doi.org/10.21236/ada448055","url":null,"abstract":"A great challenge in using any planning system to solve real-world problems is the difficulty of acquiring the domain knowledge that the system will need. We present away to address part of this problem, in the context of Hierarchical Task Network (HTN) planning, by having the planning system incrementally learn conditions for HTN methods under expert supervision. We present a general formal framework for learning HTN methods, and a supervised learning algorithm, named CaMeL, based on this formalism. We present theoretical results about CaMeL's soundness, completeness, and convergence properties. We also report experimental results about its speed of convergence under different conditions. The experimental results suggest that CaMeL has the potential to be useful in real-world applications.","PeriodicalId":215268,"journal":{"name":"International Conference on Artificial Intelligence Planning Systems","volume":"121 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122439386","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: 58
Probabilistic Hybrid Action Models for Predicting Concurrent Percept-Driven Robot Behavior 预测并发感知驱动机器人行为的概率混合动作模型
International Conference on Artificial Intelligence Planning Systems Pub Date : 2000-04-14 DOI: 10.1613/jair.1565
M. Beetz, H. Grosskreutz
{"title":"Probabilistic Hybrid Action Models for Predicting Concurrent Percept-Driven Robot Behavior","authors":"M. Beetz, H. Grosskreutz","doi":"10.1613/jair.1565","DOIUrl":"https://doi.org/10.1613/jair.1565","url":null,"abstract":"This paper develops Probabilistic Hybrid Action Models (PHAMs), a realistic causal model for predicting the behavior generated by modern concurrent percept-driven robot plans. PHAMs represent aspects of robot behavior that cannot be represented by most action models used in AI planning: the temporal structure of continuous control processes, their non-deterministic effects, and several modes of their interferences. \u0000 \u0000The main contributions of the paper are: (1) PHAMs, a model of concurrent percept-driven behavior, its formalization, and proofs that the model generates probably, qualitatively accurate predictions; and (2) a resource-efficient inference method for PHAMs based on sampling projections from probabilistic action models and state descriptions. We discuss how PHAMs can be applied to planning the course of action of an autonomous robot office courier based on analytical and experimental results.","PeriodicalId":215268,"journal":{"name":"International Conference on Artificial Intelligence Planning Systems","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2000-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116854132","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: 21
Deduction-Based Refinement Planning 基于演绎的细化计划
International Conference on Artificial Intelligence Planning Systems Pub Date : 1996-05-29 DOI: 10.22028/D291-24967
W. Stephan, Susanne Biundo-Stephan
{"title":"Deduction-Based Refinement Planning","authors":"W. Stephan, Susanne Biundo-Stephan","doi":"10.22028/D291-24967","DOIUrl":"https://doi.org/10.22028/D291-24967","url":null,"abstract":"We introduce a method of deduction-based refinement planning where prefabricated general solutions are adapted to special problems. Refinement proceeds by stepwise transforming non-constructive problem specifications into executable plans. For each refinement step there is a correctness proof guaranteeing the soundness of refinement and with that the generation of provably correct plans. By solving the hard deduction problems once and for all on the abstract level, planning on the concrete level becomes more efficient. With that, our approach aims at making deductive planning feasible in realistic contexts. Our approach is based on a temporal logic framework that allows for the representation of specifications and plans on the same linguistic level. Basic actions and plans are specified using a programming language the constructs of which are formulae of the logic. Abstract solutions are represented as—possibly recursive—procedures. It is this common level of representation and the fluid transition between specifications and plans our refinement process basically relies upon.","PeriodicalId":215268,"journal":{"name":"International Conference on Artificial Intelligence Planning Systems","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1996-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130840195","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: 32
Generating Parallel Execution Plans with a Partial-order Planner 用偏序规划器生成并行执行计划
International Conference on Artificial Intelligence Planning Systems Pub Date : 1994-06-13 DOI: 10.21236/ada285888
Craig A. Knoblock
{"title":"Generating Parallel Execution Plans with a Partial-order Planner","authors":"Craig A. Knoblock","doi":"10.21236/ada285888","DOIUrl":"https://doi.org/10.21236/ada285888","url":null,"abstract":"Many real-world planning problems require generating plans that maximize the parallelism inherent in a problem. There are a number of partial-order planners that generate such plans; however, in most of these planners it is unclear under what conditions the resulting plans will be correct and whether the planner can even find a plan if one exists. This paper identifies the underlying assumptions about when a partial plan can be executed in parallel, defines the classes of parallel plans that can be generated by different partial-order planners, and describes the changes required to turn UCPOP into a parallel execution planner. In addition, we describe how this planner can be applied to the problem of query access planning, where parallel execution produces substantial reductions in overall execution time.","PeriodicalId":215268,"journal":{"name":"International Conference on Artificial Intelligence Planning Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1994-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130879542","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: 74
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