Anytime synthetic projection: maximizing the probability of goal satisfaction

Authors:
Mark Drummond;John Bresina
Affiliations:
Sterling Federal Systems, NASA Ames Research Center, Moffett Field, CA;Sterling Federal Systems, NASA Ames Research Center, Moffett Field, CA and Computer Science Department at Rutgers University
Venue:
AAAI'90 Proceedings of the eighth National conference on Artificial intelligence - Volume 1
Year:
1990

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Abstract

This paper presents a projection algorithm for incremental control rule synthesis. The algorithm synthesizes an initial set of goal-achieving control rules using a combination of situation probability and estimated remaining work as a search heuristic. This set of control rules has a certain probability of satisfying the given goal. The probability is incrementally increased by synthesizing additional control rules to handle "error" situations the execution system is likely to encounter when following the initial control rules. By using situation probabilities the algorithm achieves a computationally effective balance between the limited robustness of triangle tables and the absolute robustness of universal plans.