Evaluating Persuasion Strategies and Deep Reinforcement Learning methods for Negotiation Dialogue agents

Abstract

In this paper we present a comparative evaluation of various negotiation strategies within an online version of the game ``Settlers of Catan''. The comparison is based on human subjects playing games against artificial game-playing agents (`bots') which implement different negotiation dialogue strategies, using a chat dialogue interface to negotiate trades. Our results suggest that a negotiation strategy that uses persuasion, as well as a strategy that is trained from data using Deep Reinforcement Learning, both lead to an improved win rate against humans, compared to previous rule-based and supervised learning baseline dialogue negotiators.

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