A Study on Applying Interactive Multi-objective Optimization to Multiagent Systems

Constraint optimization problems in multiagent systems have been studied as fundamental problems of decision making and resource allocation. When each agent has its own specific interest in a problem, that task is defined as a multiobjective optimization problem with asymmetric functions. Since multi-objective problems have a number of Pareto optimal solutions in general cases, a preferred solution is selected using an appropriate criterion. The interactive methods are approaches of solution methods for multi-objective optimization problems, where a solution is repeatedly modified based on userspecified parameters. While interactive methods are developed for single users, such a framework can be considered as the base of analysis or games on multiagent systems by replacing single users with sets of agents. To study such an approach, this paper investigates a framework that resembles the interactive aspiration level methods employing a scalarization criterion that addresses unfairness among agents, and evaluates an example case of interaction.