Utility-based sequential decision-making in evidential cooperative multi-agent systems

This paper presents a new approach to building utility-based models of decision-making in time-constrained situations with limited resources. A particular hierarchical homogenous multi-agent architecture has been considered. The proposed system combines agents' beliefs within the framework of evidence theory and after each observation maps the current set Of cumulative pignistic probabilities into one of two actions: "defer decision" or "decide hypothesis i". The system maximizes the expected utility of delayed decisions minus cost. The process of system adaptation to the environment is guided by reinforcement learning. The utilities-from-experts problem is simplified by learning utilities directly from feedback on the quality of the decisions. The results of a case study are presented.