On measuring the usefulness of modeling in a competitive and cooperative environment

AAAI Conferences 

Leonardo Garrido Ram6n Brena Centro de Inteligencia Artificial, Tecnol6gico de Monterrey Abstract This paper presents recent results of our experimental work in quantifying exactly how useful is building models about other agents using no more than the observation of others' behavior. The testbed we used in our experiments is an abstraction of the meeting scheduling problem, called the Meeting Scheduling Game, which has competitive as well as cooperative features. The agents are selfish, and use a rational decision theoretic approach based on the probabilistic models that the agent is learning. We view agent modeling as an iterative and gradual process, where every new piece of information about a particular agent is analyzed in such a way that the model of the agent is further refined. We propose a framework for measuring the performance of different modelling strategies and establish quantified lower and upper limits for the performance of any modeling strategy. Finally, we contrast the performances of a modeler from an individual and from a collective point of view, comparing the benefits for the modeler itself as well as for the group as a whole. Introduction Katia Sycara The Robotics Institute, Carnegie Mellon University Several approaches in the field of multiagent systems (MAS) (Durfee 1991; Wooldridge & Jennings 1995) make heavy use of beliefs as an internal model of the world (Bratman 1987) One form of belief of particular importance in multiagent systems are the agent's beliefs about other agents (Vidal & Durfee 1997b). This kind of belief could come from preexisting knowledge base (a kind of"prejudice"), or could be inferred from observing others' behavior. The purpuse of a modelling activity could be to benefit a specific agent, in the case of "selfish" agents, or to improve the performance of a group as a whole, in the case of cooperative agents -or even a combination of both.