A Negotiation-Based Genetic Framework for Multi-Agent Credit Assignment

Abstract

Multi agent systems are a well-defined solution for implementing dynamic complex environments. One of the open issues of these systems is credit assignment problem. The main concern of credit assignment problem is to properly distributing feedback of overall performance, and brings about learning in each individual agent. In this paper a genetic framework for solving Multi-agent credit assignment problem is proposed. Our framework, Negotiation Based Credit Assignment, NBCA, applies negotiation for both enriching agents’ knowledge as well as organizing populations by a mode analyzer. The proposed architecture includes a mentor agent which responsible for credit assignment without any context related information leading to a general solution. Furthermore, the mentor agent does not receive any information regarding correctness of a particular agent’s behavior. Carry and non-Carry cases have been considered for evaluating this method. In addition, the effects of noise as a source of uncertainty on NBCA performance are examined. Our finding indicated that the proposed method is superior to previous credit assignment approaches. This is due to the argumentation and negotiation features of multi agent systems that are used to accomplish team learning and credit assignment respectively. The analysis of obtained results which are theoretically discussed, demonstrate that, in comparison with KEBCA (OR-type), our approach performs better than KEBCA after 5000 trials in 0% noisy environment. However, it performs worse than KEBCA in 10% and 30% noisy environment.KeywordsMulti Agent SystemMultiagent SystemNegotiation ProcessNoisy EnvironmentMutation PopulationThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.