NDLI: Building efficient partial plans using Markov decision processes

Content Provider	IEEE Xplore Digital Library
Author	Laroche, P.
Copyright Year	2000
Description	Author affiliation: LORIA, INRIA-Lorraine, Vandoeuvre-les-Nancy, France (Laroche, P.)
Abstract	Markov decision processes (MDP) have been widely used as a framework for planning under uncertainty. They allow to compute optimal sequences of actions in order to achieve a given goal, accounting for actuator uncertainties. But algorithms classically used to solve MDPs are intractable for problems requiring a large state space. Plans are computed considering the whole state space, without using any knowledge about the initial state of the problem. We propose a new technique to build partial plans for a mobile robot, considering only a restricted MDP which contains a small set of states composing a path between the initial state and the goal state. To ensure good quality of the solution, the path has to be very similar to the one which would have been computed on the whole environment. We present a new method to compute partial plans, showing that representing the environment as a directed graph can be very helpful to find near-optimal paths. Partial plans obtained using this method are very similar to complete plans, and computing times are considerably reduced.
Starting Page	156
Ending Page	163
File Size	751481
Page Count	8
File Format	PDF
ISBN	0769509096
ISSN	10823409
DOI	10.1109/TAI.2000.889862
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2000-11-15
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	State-space methods Uncertainty Process planning Actuators Mobile robots Model driven engineering Artificial intelligence Stochastic processes Joining processes Equations
Content Type	Text
Resource Type	Article

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