NegoChat: a chat-based negotiation agent

To date, a variety of automated negotiation agents have been cre-ated. While each of these agents has been shown to be effective in negotiating with people in specific environments, they lack natu-ral language processing support required to enable real-world types of interactions. In this paper we present NegoChat, the first nego-tiation agent that successfully addresses this limitation. NegoChat contains several significant research contributions. First, we found that simply modifying existing agents to include an NLP module is insufficient to create these agents. Instead, the agents ’ strate-gies must be modified to address partial agreements and issue-by-issue interactions. Second, we present NegoChat’s negotiation al-gorithm. This algorithm is based on bounded rationality, and specif-ically Aspiration Adaptation Theory (AAT). As per AAT, issues are addressed based on people’s typical urgency, or order of impor-tance. If an agreement cannot be reached based on the value the human partner demands, the agent retreats, or downwardly lowers the value of previously agreed upon issues so that a “good enough” agreement can be reached on all issues. This incremental approach is fundamentally different from all other negotiation agents, includ-ing the state-of-the-art KBAgent. Finally, we present a rigorous evaluation of NegoChat, showing its effectiveness.