[工程科技]Learning and exploiting relative weaknesses of opponent agents.pdfVIP

Learning and Exploiting Relative Weaknesses of Opponent Agents Shaul Markovitch (shaulm@cs.technion.ac.il) and Ronit Reger (ronitr@cs.technion.ac.il) Computer Science Department, Technion, Israel Institute of Technology Abstract. Agents in a competitive interaction can greatly benefit from adapting to a partic- ular 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 con- cept. 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. Keywords: Opponent Modelling, Multi-Agent Systems, Machine Learning 1. Introduction “Lasker sought to explore and make use of his opponent’s weak- nesses. Often he would choose the second or third strongest move, because this was the most