Abstract Much of human dialogue occurs in semi-cooperative settings, where agents with different goals attempt to agree on common decisions. Negotiations require complex communication and reasoning skills, but success is easy to measure, making this an interesting task for AI. We gather a large dataset of human-human negotiations on a multi-issue bargaining task, where agents who cannot observe each other’s reward functions must reach an agreement (or a deal) via natural language dialogue. For the first time, we show it is possible to train end-to-end models for negotiation, which must learn both linguistic and reasoning skills with no annotated dialogue states. We also introduce dialogue rollouts, in which the model plans ahead by simulating possible complete continuations of the conversation, and find that this technique dramatically improves performance. Our code and dataset are publicly available (this https URL).
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Models learn to be deceptive. Deception can be an effective negotiation tactic. We found numerous cases of our models initially feigning interest in a valueless item, only to later ‘compromise’ by conceding it. Figure 7 shows an example
Facebook researchers claim to have trained an agent to negotiate and that along the way it learnt deception.
https://arxiv.org/abs/1706.05125
Deal or No Deal? End-to-End Learning for Negotiation Dialogues
Mike Lewis, Denis Yarats, Yann N. Dauphin, Devi Parikh, Dhruv Batra(Submitted on 16 Jun 2017)
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