Combining Multiple Perspectives

Abstract

We consider a group of Bayesian learners whose interactions with the environment and other agents allow them to improve their model of the dependency among various factors that have influence on their interactions with the environment. Effective collaboration can improve the performance of isolated individual learners. We present a mechanism to pool together the knowledge of many modelers in the domain, each of whom may have only partial access to the environment. The application domain used in this study is a multiagent negotiation problem. We present results to compare the performance of such knowledge-composition against isolated learners, as also against a learner who has complete access to the environment.