Interactive Course-of-Action Planning using Causal Models
Ugur Kuter, Dana S. Nau, Don Gossink, John F. Lemmer · 2004
Abstract. This paper describes a new technique for interactive planning for coalition operations 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 Bayesian networks. In order to use CAT as a tool for planning for coalition operations, 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 that must be analyzed is exponential in the number of possible actions that may or may not appear in those plans. 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 an approach to quickly compute upper and lower bounds on the 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 a complete plan. This provides an exponential reduction in the amount of time needed to find a complete plan. In our experiments, our approach generated recommendations that resulted in plans that have the highest probability of success in just a few minutes. 1 Problem and Significance In planning a coalition’s course of action (i.e., a plan for the coalition to execute to achieve a desired objective or