|M.Sc Student||Reger Ronit|
|Subject||Learning and Exploiting Relative Weaknesses of Opponent|
|Department||Department of Computer Science||Supervisor||Professor Shaul Markovitch|
Agents in a competitive interaction can greatly benefit from adapting to a particular adversary, rather than using the same general strategy against all opponents. One method of such adaptation is Opponent Modelling, in which a model of an opponent is acquired and utilized as part of the agent's decision procedure in future interactions with this opponent. However, acquiring an accurate model of a complex opponent strategy may be computationally infeasible. In addition, if the learned model is not accurate, then using it to predict the opponent's actions may potentially harm the agent's strategy rather than improving it. We thus define the concept of opponent weakness, and present a method for learning a model of this simpler concept. We analyze examples of past behavior of an opponent in a particular domain, judging its actions using a trusted judge. We then infer a weakness model based on the opponent's actions relative to the domain state, and incorporate this model into our agent's decision procedure. We also make use of a similar “self weakness model”, allowing the agent to prefer states in which the opponent is weak and our agent strong; where we have a “relative advantage” over the opponent. Experimental results spanning two different test domains demonstrate the agents' improved performance when making use of the weakness models.