Motivation and Design of the Conversational Components of DraftAgent for Human-Agent Negotiation

Abstract

In sync with the significant interest in industry and the general populace about future potential of human-AI collaboration [14], academic researchers have been pushing the frontier of new modalities of peer-level and ad-hoc human agent collaboration [4,15]. We have been particularly interested in research on agents representing human users in negotiating deals with other human and autonomous agents [6,11,13]. We present the design motivation and key components of the conversational aspect of our agent entry into the Human-Agent League(HAL) (http://web.tuat.ac.jp/~katfuji/ANAC2020/cfp/ham_cfp.pdf )of the 2020 Automated Negotiation Agent Competition (ANAC). We explore how language can be used to promote human-agent collaboration even in the domain of a competitive negotiation. We present small scale in-lab testing to demonstrate the potential of our approach.