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- Publisher
- Springer International Publishing
- Copyright
- © Springer Nature Switzerland AG 2020
- ISBN
- 978-3-030-06166-1
- Pages
- 285 –312
- DOI
- 10.1007/978-3-030-06167-8_10
- Publisher site
- See Chapter on Publisher Site
Abstract
[In this chapter, we proposeSabbadin, Régis a non-exhaustive review of past works of the AI community on classical planning and planning underTeichteil-Königsbuch, Florent uncertainty. We first present the classical propositional STRIPS planning language. Its extensions, based on the problem description language PDDL have become a standard in the community. We briefly deal with the structural analysis of planning problems, which has initiated theVidal, Vincent development of efficient planning algorithms and associated planners. Then, we describe the Markov Decision Processes framework (MDP), initially proposed in the Operations Research community before the AI community adopted it as a framework for planning under uncertainty. Eventually, we will describe innovative (approximate or exact) MDP solution algorithms as well as recent progresses in AI in terms of knowledge representation (logics, Bayesian networks) which have been used to increase the power of expression of the MDP framework.]
Published: May 8, 2020