language: en

Cooperative Bayesian and Case-Based Reasoning for Solving Multiagent Planning Tasks

David W. Aha & Li Wu Chang
Navy Center for Applied Research in AI Naval Research Laboratory,
Code 5510 Washington, DC 20375
faha; liwug@aic.nrl.navy.mil

[(202) 767-2884 / FAX: 767-3172]

January 25, 1996

Abstract We describe an integrated problem solving architecture named INBANCA in which Bayesian networks and case-based reasoning (CBR) work cooperatively on multiagent planning tasks. This includes two-team dynamic tasks, and this paper concentrates on simulated soccer as an example. Bayesian networks are used to characterize action selection whereas a case-based approach is used to determine how to implement actions. This paper has two contributions. First, we survey integrations of case-based and Bayesian approaches from the perspective of a popular CBR task decomposition framework, thus explaining what types of integrations have been attempted. This allows us to explain the unique aspects of our proposed integration. Second, we demonstrate how Bayesian nets can be used to provide environmental context, and thus feature selection information, for the case-based reasoner.

1 Introduction