Opponent modeling for virtual human negotiators

Abstract

Negotiation is a challenging domain for virtual human research. One aspect of this problem, known as opponent modeling, is discovering what the other party wants from the negotiation. Research in automated negotiation has yielded a number opponent modeling techniques but we show that these methods do not easily transfer to human-agent settings. We propose a more effective heuristic for inferring preferences both from a negotiator’s pattern of offers and verbal statements about their preferences. This method has the added advantage that it can detect negotiators that lie about their preferences. We discuss several ways the method can enhance the capabilities of a virtual human negotiator.