Learning non-cooperative dialogue behaviours
Ioannis Efstathiou, Oliver Lemon · 2014
Non-cooperative dialogue behaviour has been identified as important in a vari-ety of application areas, including educa-tion, military operations, video games and healthcare. However, it has not been ad-dressed using statistical approaches to di-alogue management, which have always been trained for co-operative dialogue. We develop and evaluate a statistical dia-logue agent which learns to perform non-cooperative dialogue moves in order to complete its own objectives in a stochas-tic trading game. We show that, when given the ability to perform both coopera-tive and non-cooperative dialogue moves, such an agent can learn to bluff and to lie so as to win games more often – against a variety of adversaries, and under var-ious conditions such as risking penalties for being caught in deception. For exam-ple, we show that a non-cooperative dia-logue agent can learn to win an additional 15.47 % of games against a strong rule-based adversary, when compared to an op-timised agent which cannot perform non-cooperative moves. This work is the first to show how an agent can learn to use non-cooperative dialogue to effectively meet its own goals. 1