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A Concise Introduction to Models and Methods for Automated PlanningClassical Planning: Variations and Extensions

A Concise Introduction to Models and Methods for Automated Planning: Classical Planning:... [Most of the current state-of-the-art classical planners are based on the heuristic search formulation where plans are searched forward from the initial state using heuristics derived from the problem. This basic idea, however, has been extended in a number of ways, like in the use of structural information, also obtained automatically from the problem, in the form of “helpful actions” and “landmarks.” In this chapter, we look at extensions and variations of the basic framework, at the heuristics developed for optimal planning, and at other computational approaches to classical and related forms of planning such as temporal and hierarchical task network planning.] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

A Concise Introduction to Models and Methods for Automated PlanningClassical Planning: Variations and Extensions

Part of the Synthesis Lectures on Artificial Intelligence and Machine Learning Book Series
Geffner, Hector; Bonet, Blai
Springer Journals — Jan 1, 2013
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13 pages

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Publisher
Springer International Publishing
Copyright
© Springer Nature Switzerland AG 2013
ISBN
978-3-031-00436-0
Pages
37 –50
DOI
10.1007/978-3-031-01564-9_3
Publisher site
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Abstract

[Most of the current state-of-the-art classical planners are based on the heuristic search formulation where plans are searched forward from the initial state using heuristics derived from the problem. This basic idea, however, has been extended in a number of ways, like in the use of structural information, also obtained automatically from the problem, in the form of “helpful actions” and “landmarks.” In this chapter, we look at extensions and variations of the basic framework, at the heuristics developed for optimal planning, and at other computational approaches to classical and related forms of planning such as temporal and hierarchical task network planning.]

Published: Jan 1, 2013

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