Using Bayesian Networks to Model Agent Relationships

An agent-society of the future is envisioned to be as complex as a human society. Just like human societies, such multiagent systems (MAS) deserve an in-depth study of the dynamics, relationships, and interactions of the constituent agents. An agent in a MAS may have only approximate a priori estimates of the trustworthiness of another agent. But it can learn from interactions with other agents, resulting in more accurate models of these agents and their dependencies together with the inÑuences of other environmental factors. Such models are proposed to be represented as Bayesian or belief networks. An objective mechanism is presented to enable an agent elicit crucial information fromthe environment regarding the true nature of the other agents. This mechanismallows the modeling agent to choose actions that will produce guaranteed minimal improvement of the model accuracy. The working of the proposedmaximin entropy procedure is demonstrated in a multiagent scenario.