<title>Bayesian methods for interpretation and control in multiagent vision systems</title>

Abstract

Interpretation of images is a context dependent activity and, therefore, saturated with uncertainty. It is outlined how causal probabilistic networks (CPNs) together with strict and efficient Bayesian methods can be used for modeling contexts and for interpretation of findings. For illustration purposes a 2-agent system consisting of an interpreter using a CPN and a findings catcher using an image processor is designed. It is argued that the architecture should be a system of agents with instincts, each of them acting to improve their own situation. Going through an interpretation session, it is shown how the Bayesian paradigm very neatly supports the agents-with-instincts control paradigm such that the system through private benefit maximizing in an efficient way reaches its goal.