Abstract:
Summary
Multi-Agent systems typically utilise simple, predictable agents. The usage of such agents
in large systems allows for complexity to be achieved through the interaction of these
agents. It is feasible, however, to utilise intelligent agents in smaller systems, allowing for
more agent complexity and hence a higher degree of realism in the multi-agent model. By
utilising the TD( ) Algorithm to train feedforward neural networks, intelligent agents
were successfully trained within the reinforcement learning paradigm. A methodology for
stabilising this typically unstable neural network training was found through first looking
at the relatively simple problem of Tic-Tac-Toe. Once a stable training methodology was
arrived at, the more complex task of tackling a multi-player, multi-stage card-game was
tackled. The results illustrated that a variety of scenarios can be realistically investigated
through the multi-agent model, allowing for solving of situations and better
understanding of the game itself. Yet more startling, owing to the agent’s design, the
agents learned on their own to bluff, giving much greater insight into the nature of
bluffing in such games that lend themselves to the act.
