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.

A central purpose of referring expressions is to distinguish intended referents from other entities that are in the context; but how is this context determined? This paper draws a distinction between discourse context ―other entities that have been mentioned in the dialogue― and visual context ―visually available objects near the intended referent. It explores how these two different aspects of context have an impact on subsequent reference in a dialogic situation where the speakers share both discourse and visual context. In addition we take into account the impact of the reference history ―forms of reference used previously in the discourse― on forming what have been called conceptual pacts. By comparing the output of different parameter settings in our model to a data set of human-produced referring expressions, we determine that an approach to subsequent reference based on conceptual pacts provides a better explanation of our data than previously proposed algorithmic approaches which compute a new distinguishing description for the intended referent every time it is mentioned.