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A Concise Introduction to Models and Methods for Automated PlanningMDP Planning: Stochastic Actions and Full Feedback

A Concise Introduction to Models and Methods for Automated Planning: MDP Planning: Stochastic... [Markov Decision Processes (MDPs) generalize the model underlying classical planning by allowing actions with stochastic effects and fully observable states. In this chapter, we look at a variety of MDP models and the basic algorithms for solving them: from offline methods based on dynamic programming and heuristic search, to online methods where the action to do next is obtained by solving simplifications, like finite-horizon versions of the problem or deterministic relaxations.] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

A Concise Introduction to Models and Methods for Automated PlanningMDP Planning: Stochastic Actions and Full Feedback

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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17 pages

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Publisher
Springer International Publishing
Copyright
© Springer Nature Switzerland AG 2013
ISBN
978-3-031-00436-0
Pages
79 –96
DOI
10.1007/978-3-031-01564-9_6
Publisher site
See Chapter on Publisher Site

Abstract

[Markov Decision Processes (MDPs) generalize the model underlying classical planning by allowing actions with stochastic effects and fully observable states. In this chapter, we look at a variety of MDP models and the basic algorithms for solving them: from offline methods based on dynamic programming and heuristic search, to online methods where the action to do next is obtained by solving simplifications, like finite-horizon versions of the problem or deterministic relaxations.]

Published: Jan 1, 2013

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