Knowledge Representation, Communication, and Update In Probability-Based Multiagent Systems

In this dissertation, we define a cooperative multiagent system where the agents use locally designed Bayesian networks to represent their knowledge. Agents communi- cate via message passing where the messages are beliefs in shared variables that are represented as probability distributions. Messages are treated as soft evidence in the receiver agents, where the belief in the receiving agent is replaced by the publishing agent’s belief. We call this the oracular assumption, where one agent is an expert or more knowledgeable of particular variables. As a result, the agents are organized in a publisher-subscriber hierarchy. A central problem of message passing in prob- abilistic systems is the so called rumor problem, where cycles in message passing cause redundant influence of beliefs. We develop algorithms to identify and solve the rumor problem in the context of our multiagent system. We compare and contrast our system with the MSBN multiagent model. Central to our agent model is the notion of soft evidential update. We develop methods to efficiently perform probabilistic update in Bayesian networks where the soft evidence is respected. We analyze the theoretical and experimental complexity of our methods and compare them with other methods that have been proposed. Finally, we implement several multiagent systems for experimentation using our multiagent system and MSBNs. We devise performance measures to compare the two systems. From this comparison, we provide guidance for the design of probabilistic multiagent systems