Agent learning in the multi-agent contracting system [MACS]

This paper presents a Bayesian learning approach for a multi-agent system, called multi-agent contracting system [MACS]. The system learns to identify an appropriate agent to answer free-text queries and keyword searches for defense contracting. This research builds on past work by some of the authors by extending MACS to a truly intelligent multi-agent system with the ability to learn from and adapt to its environment. The efficacy of MACS is determined by analyzing the accuracy and degree of learning in the system. This is accomplished by testing the system against historical data.