Utility theory in conflict resolution

In this paper, multi-attribute utility theory is extended to accommodate adversarial problem solving situations involving multiple interacting agents. Such situations are resolved by partial goal satisfaction and persuasion, and have only scantily been described in the AI literature. Utility theory is shown to provide a computational framework to (a) generate a compromise solution that partially satisfies the conflicting goals of the agents, (b) evaluate whether a solution is an improvement on a previously rejected one, and (c) determine the effectiveness of persuasive arguments. Our examples are taken from the domain of labor mediation and are implemented in a computer program, called the PERSUADER.