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Record 1 of 1778
Optimal Limited Contingency Planning
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Author and Affiliation:
Meuleau, Nicolas(QSS Group, Inc., United States)
Smith, David E.(NASA Ames Research Center, Moffett Field, CA, United States)
Abstract: For a given problem, the optimal Markov policy over a finite horizon is a conditional plan containing a potentially large number of branches. However, there are applications where it is desirable to strictly limit the number of decision points and branches in a plan. This raises the question of how one goes about finding optimal plans containing only a limited number of branches. In this paper, we present an any-time algorithm for optimal k-contingency planning. It is the first optimal algorithm for limited contingency planning that is not an explicit enumeration of possible contingent plans. By modelling the problem as a partially observable Markov decision process, it implements the Bellman optimality principle and prunes the solution space. We present experimental results of applying this algorithm to some simple test cases.
Publication Date: Jan 01, 2003
Document ID:
20030107363
(Acquired Nov 18, 2003)
Subject Category: MATHEMATICAL AND COMPUTER SCIENCES (GENERAL)
Document Type: Preprint
Financial Sponsor: NASA Ames Research Center; Moffett Field, CA, United States
Organization Source: NASA Ames Research Center; Moffett Field, CA, United States
Description: 10p; In English
Distribution Limits: Unclassified; Publicly available; Unlimited
Rights: No Copyright
NASA Terms: OPTIMIZATION; PLANNING; CONTINGENCY; ALGORITHMS; MATHEMATICAL MODELS; PROBLEM SOLVING; BELLMAN THEORY; MARKOV PROCESSES
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