Approximate Computation of Multi-Agent Dynamic Influence Diagrams

Due to high dimension and uncertainty of the complex system, the complexity system is often hard to represent and process, and the knowledge representation and computation methods of complex systems are open hard problems in complex system research. At present, MAIDs can not model dynamic environment and it is difficult for multi-agent MDPs to represent structural relations among agents; so a multi-agent dynamic influence diagrams (MADIDs) model is given to representation relations among multi-agents in dynamic environment by local factor probability form. The computation of joint probability distribution and joint utility function of MADIDs are a high dimension problem, so the approximate computation methods are researched. A distribution approximation method of hierarchical decomposition of probability structural MADIDs is studied; based on analysis of the complexity and the error of the distribution approximation method, a function δ(k) is introduced to establish equilibrium between precision and complexity of approximate distribution. Then a BP neural network is given to approximately compute utility structural MADIDs by learning local utility. Finally, given model instances, the experiment results show the validity of the approximation computation method of the MADIDs model.