Group 8: Yet Another Chess Playing Multi-Agent System

Designing agent behaviors is rarely straight forward, especially if the natural environment of the agent is open and complex (as is the case of the Internet). In this paper, we describe our behavior discriminative agent (BDA) model which aims at supporting designers of such agents. The behaviors are here themselves represented by agents so the BDA agent comprises a multi-agent system, where behavior agents interact with a coordinator agent which decides the actions of the BDA agent. The BDA agent uses the Q-learning technique and learns by sensing the consequences of its actions and updates its belief in the behaviors continually. We implement our agent model in a chess game setting and present some early results.