Attitude-driven decision making for multi-agent team formation in open and dynamic environments

Abstract

Multi-agent systems are applied to distributed problem-solving applications because of their ability to overcome the limitations that individual agents face when solving complex problems. Large numbers of agents acting as problem-solvers on networks suggest a virtual marketplace. In this marketplace, groups of self-interested agents can interact to solve highly constrained and distributed problems by assuming varying roles and forming “temporary teams”. This dissertation presents a decision making mechanism for multi-agent team formation between self-interested agents in a competitive, open and dynamic environment. An agent perceives environmental uncertainties, and models those uncertainties into simplified categories such as risks and benefits. The dissertation further demonstrates how an agent’s attitudes shape how risk and rewards are weighted when making decisions among multiple alternatives. Accordingly, agent-borne attitudes toward proactive behavior, risk, reward, and urgency are proposed as the basis of the proposed team formation mechanism. Finally, a learning technique assists an agent in continuously learning what attitudes it needs in order to adapt to dynamic environments and increase its resulting rewards.