A kernel-oriented model for autonomous-agent coalition-formation in general environments

Abstract

An important way for autonomous agents to execute tasks and to maximize payoff is to share resources and cooperate on task execution by creating coalitions of agents. Among individual rational agents, such coalitions will form if, and only if, each member of a coalition gains more if it joins the coalition than it could gain previously. There are several models of creating such coalitions and dividing the joint payoff among the members. In this paper we present a model for coalition formation and payoff distribution in general environments. We focus on a reduced complexity kernel-oriented coalition formation model. The model is partitioned into two levels — a social level and a strategic level. This partition enables one to distinguish between regulations that must be agreed upon and are forced by the designers of the agents, and strategies by which each agent acts and that can be adopted at will.