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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
392 Chapter 16 Planning Based on Markov Decision Processes
algorithm will eventually terminate and return a policy. If we add the condition that
the search space is strongly connected, i.e., there is a path with positive probability
from every state to every other state, then the algorithm will eventually return an
optimal policy.
It has been shown experimentally that real-time value iteration can solve much
larger problems than standard value and policy iteration [84]. The trade-off is that
the solution is not optimal and the algorithm is not complete; in fact, it may even
not terminate.
16.3 Planning under Partial Observability
Planning under Partial Observability in MDP (POMDP) relaxes the assumption
that the controller has complete knowledge about ...
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Publisher Resources

ISBN: 9781558608566