Planning in Artificial Intelligence Régis Sabbadin, Florent Teichteil-Königsbuch and Vincent Vidal Abstract In this chapter, we propose a non-exhaustive review of past works of the AI community on classical planning and planning under 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 the 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. 1 Introduction In Artificial Intelligence, the planning community has been interested in the gener- ation of plans (sequences of actions) to reach a fixed goal. These plans are usually written in concise languages of state-transformation operators, often based on propo- sitional logic. See Ghallab et al. (2004), for example, for a complete review of such approaches. The ambitious objective of the first operators-based planning system, GPS (“General Problem Solver”) of Newell and Simon (1963), was to give a com- puter the ability to simulate human reasoning. Since then, the classical planning R. Sabbadin (B ) MIAT, UR 875, INRA, 31320 Castanet-Tolosan, France e-mail: regis.sabbadin@inra.fr F. Teichteil-Königsbuch AIRBUS, Toulouse, France e-mail: florent.teichteil-koenigsbuch@airbus.com V. Vidal ONERA, Toulouse, France e-mail: Vincent.Vidal@onera.fr © Springer Nature Switzerland AG 2020 P. Marquis et al. (eds.), A Guided Tour of Artificial Intelligence Research, https://doi.org/10.1007/978-3-030-06167-8_10 285