Non-cooperative dialogue behaviour has been identified as important in a variety of application areas, including education, military operations, video games and healthcare. However, it has not been addressed using statistical approaches to dialogue management, which have always been trained for co-operative dialogue. We develop and evaluate a statistical dialogue agent which learns to perform non-cooperative dialogue moves in order to complete its own objectives in a stochastic trading game. We show that, when given the ability to perform both cooperative 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 various conditions such as risking penalties for being caught in deception. For example, we show that a non-cooperative dialogue agent can learn to win an additional 15.47% of games against a strong rule-based adversary, when compared to an optimised 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.
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