Interactive Planning under Uncertainty with Casual Modeling and Analysis

摘要

This paper describes a new technique for interactive planning under conditions of uncertainty. Our approach is based on the use of the Air Force Research Laboratory's Causal Analysis Tool (CAT), a system for creating and analyzing causal models similar to Bayes networks. In order to use CAT as a tool for planning, users go through an iterative process in which they use CAT to create and analyze alternative plans. One of the biggest difficulties is that the number of possible plans is exponential. In any planning problem of significant size, it is impossible for the user to create and analyze every possible plan; thus users can spend days arguing about which actions to include in their plans. To solve this problem, we have developed a way to quickly compute the minimum and maximum probabilities of success associated with a partial plan, and use these probabilities to recommend which actions the user should include in the plan in order to get the plan that has the highest probability of success. This provides an exponential reduction in amount of time needed to find the best plan.