Structural Bayesian network learning in a biological decision-theoretic intelligent agent and its application to a herding problem in the context of distributed multi-agent systems

Abstract

The paper proposes a structural Bayesian network learning in a biological decision-theoretic intelligent agent model to solve a herding problem. The proposed structural learning methods show that an agent can update its world model by changing the structure of its Bayesian network with the data gathered by experience. The structural learning of the Bayesian network is accomplished by implementing a score based greedy search algorithm. The search algorithm is designed heuristically and exhaustively. A complexity analysis for the search algorithms is performed. Intelligent agent software, IntelliAgent, is written to simulate the herding problem with one sheep and one dog.