Coordination of Multiagent Systems through Explicit Valuation of Action

Abstract

The effectiveness of a system relates to its ability to fulfill its functional requirements. When a system consists of a set of collaborating agents, the overall performance may depend more on their ability to work together than on optimizing individual behavior. In fact, the goal of an agent is often at odds with the interest of the collective. The limitations of centralized control for complex systems suggest the need for decentralized coordination. This can be achieved by having each agent take explicit account of the cost of engaging in an activity as well as a measure of reward for effective behavior. The consideration of payoffs and penalties leads to an economic perspective of multiagent systems. In consequence, it is possible to draw on previous work in diverse fields, ranging from game theory to welfare economics and social policy. The promise and limitations of the explicit valuation approach are examined. To illustrate, the behavior of collaborative systems can be interpreted in terms of games of strategy; this approach permits a better understanding of the conditions for globally optimal behavior, as well as strategies for their attainment. The notions of agents and explicit valuation of action are versatile concepts. One indication of the versatility lies in the fact that these concepts cover as a special case the idea of genetic algorithms as a mechanism for learning systems.