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Automated Planning
book

Automated Planning

by Malik Ghallab, Dana Nau, Paolo Traverso
May 2004
Intermediate to advanced content levelIntermediate to advanced
635 pages
19h 46m
English
Morgan Kaufmann
Content preview from Automated Planning
380 Chapter 16 Planning Based on Markov Decision Processes
reachability and extended goals (Section 16.4). The chapter ends with discussion
and exercises.
16.2 Planning in Fully Observable Domains
Under the hypothesis of full observability, we first introduce stochastic systems,
policies, and utility functions and formalize planning as an optimization problem
(Section 16.2.1). We then present and discuss some basic MDP planning algorithms:
policy, value, and real-time iteration (Section 16.2.2).
16.2.1 Domains, Plans, and Planning Problems
Domains as Stochastic Systems. A stochastic system is a nondeterministic state-
transition system with a probability distribution on each state transition. It is a tuple
= (S, A, P), where:
S is a finite set of states. ...
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Publisher Resources

ISBN: 9781558608566