A blackboard based hybrid multi-agent system for improving classification accuracy using reinforcement learning techniques

Abstract

In this paper, a general purpose multi-agent classifier system based on the blackboard architecture using reinforcement Learning techniques is proposed for tackling complex data classification problems. A trust metric for evaluating agent’s performance and expertise based on Q-learning and employing different voting processes is formulated. Specifically, multiple heterogeneous machine learning agents, are devised to form the expertise group for the proposed Coordinated Heterogeneous Intelligent Multi-Agent Classifier System (CHIMACS). To evaluate the effectiveness of CHIMACS, a variety of benchmark problems are used, including small and high dimensional datasets with and without noise. The results from CHIMACS are compared with those of individual ML models and ensemble methods. The results indicate that CHIMACS is effective in identifying classifier agent expertise and can combine their knowledge to improve the overall prediction performance.