Study On Dynamic Multi-Agent Model And Decision

The complex dynamic decision problem is an important part of the complex system research in Artificial Intelligence domain. Based on Bayesian technology and decision theory, Multi-Agent Dynamic Influence Diagrams(MADIDs) model is presented for modeling the dynamic Multi-Agent system, which is a dynamic decision model with more strong knowledge representation ability. The method of approximating distribution, inference algorithms and Multi-Agent coordination are discussed. The main research contents and innovations in this dissertation are as follows:(1) A structural decomposition method of Influence Diagrams(IDs) is presented, and an Influence Diagram can be composed into two parts: probability structure and utility structure. A new MDL scoring is presented for reducing dependency on data, which merges the prior knowledge of network structures. Based on the new MDL scoring, a PS-EM algorithm is proposed for learning probability structure of IDs. The utility function of IDs is the sum form of the each local utility function, and a Neural Network is constructed for learning local utility functions of utility part. The experiment results show that PS-EM algorithm is efficient.(2) Based on analyzing some probability decision models, Multi-Agent Dynamic Influence Diagrams(MADIDs) are presented by introducing a temporal aspect into the framework of MAIDs, and coordination relationships in dynamic environment can be modeled. To efficiently compute the probability distribution of MADIDs, a method of hierarchical decomposition is presented for approximating distribution of MADIDs under the guidance of the strategic relativity among Agents, and the errors are analyzed based on the KL divergence.(3) Aimming at the high computation complexity of the 1.5 slice junction tree exact inference algorithm and the large error of BK approximate inference algorithm, an extensional BK (EBK) approximate inference algorithm is proposed. MADIDs are hierarchically decomposed for improving the efficiency of inference in EBK algorithm, and the conditionally independent separators are induced for decreasing the error of the inference, and the inference of decision nodes and utility nodes are added for inferring MADIDs. The particle filter algorithm and factored particle algorithm are discussed, and a junction tree factored particle filter(JFP) algorithm is presented by combing the advantages of the junction trees and particle filter. JFP algorithm converts the distribution of MADIDs into the local factorial form for improving computational efficiency; For decreasing error, the inference is performed by propagating factor particle on junction tree. Some simulative experiments are performed in the RoboCup simulation environment to verify and compare above algorithms, the results of which are quite satisfactory.(4) The method of Multi-Agent Coordination using Coordination Graph (CG) is discussed; further, an extensional Coordination Graph is presented by inductting roles into CG to decrease the coordination communication. A Multi-Agent Coordination method is given based on MADIDs, where the coordination is realized by inference of environment and computation of local utility; and the communication of local coordination is avoided by modeling the opponent.