Learning action models from plan examples using weighted MAX-SAT
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TLDR
This paper develops an algorithm called ARMS (action-relation modelling system) for automatically discovering action models from a set of successful observed plans, and lays the theoretical foundations of the learning problem and evaluates the effectiveness of ARMS empirically.About:
This article is published in Artificial Intelligence.The article was published on 2007-02-01 and is currently open access. It has received 224 citations till now. The article focuses on the topics: Action language & Action model learning.read more
Citations
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Journal ArticleDOI
From action to activity
TL;DR: Experiments demonstrated that the approach is able to recognize activities with high accuracy from temporal patterns, and that temporal patterns can be used effectively as a mid-level feature for activity representation.
Book
Automated Planning and Acting
TL;DR: This book presents a comprehensive paradigm of planning and acting using the most recent and advanced automated-planning techniques, and explains the computational deliberation capabilities that allow an actor to reason about its actions, choose them, organize them purposefully, and act deliberately to achieve an objective.
Journal ArticleDOI
Deliberation for autonomous robots: A survey
Félix Ingrand,Malik Ghallab +1 more
TL;DR: A global overview of deliberation functions in robotics is presented and the main characteristics, design choices and constraints of these functions are discussed.
Journal ArticleDOI
Review: a review of machine learning for automated planning
TL;DR: This paper reviews recent techniques in machine learning for the automatic definition of planning knowledge and reviews the advances in the related field of reinforcement learning, organized according to the target of the learning process.
Journal ArticleDOI
Learning complex action models with quantifiers and logical implications
TL;DR: This article presents a novel algorithm called LAMP (Learning Action Models from Plan traces), to learn action models with quantifiers and logical implications from a set of observed plan traces with only partially observed intermediate state information.
References
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Book
Computers and Intractability: A Guide to the Theory of NP-Completeness
TL;DR: The second edition of a quarterly column as discussed by the authors provides a continuing update to the list of problems (NP-complete and harder) presented by M. R. Garey and myself in our book "Computers and Intractability: A Guide to the Theory of NP-Completeness,” W. H. Freeman & Co., San Francisco, 1979.
Proceedings Article
Fast algorithms for mining association rules
TL;DR: Two new algorithms for solving thii problem that are fundamentally different from the known algorithms are presented and empirical evaluation shows that these algorithms outperform theknown algorithms by factors ranging from three for small problems to more than an order of magnitude for large problems.
Proceedings Article