Negotiation games with structured post-hoc intents

Abstract

An important class of negotiation games that use human language do not have predefined ‘moves’: it is up to the agents in the game to define moves via natural language that will lead them towards their goal. In the context of other games, however, a notion of intents — structured moves from a predefined set — have been found to be useful. In this paper, we show that it is possible to define and learn post-hoc intents in a practical way for AI agents in a negotiation game, using a text-to-text Transformer model; we show that this improves agent performance, and further allows the definition of a wider range of agents for training.