TY - GEN
T1 - NegoChat
T2 - 13th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2014
AU - Rosenfeld, Avi
AU - Zuckerman, Inon
AU - Segal-Halevi, Erel
AU - Drein, Osnat
AU - Kraus, Sarit
N1 - Publisher Copyright:
Copyright © 2014, International Foundation for Autonomous Agents and Multiagent Systems (www.ifaamas.org). All rights reserved.
PY - 2014
Y1 - 2014
N2 - To date, a variety of automated negotiation agents have been created. While each of these agents has been shown to be effective in negotiating with people in specific environments, they lack natural language processing support required to enable real-world types of interactions. In this paper we present NegoChat, the first negotiation 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' strategies must be modified to address partial agreements and issue-by-issue interactions. Second, we present NegoChat's negotiation algorithm. This algorithm is based on bounded rationality, and specifically Aspiration Adaptation Theory (AAT). As per AAT, issues are addressed based on people's typical urgency, or order of importance. 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, including the state-of-the-art KBAgent. Finally, we present a rigorous evaluation of NegoChat, showing its effectiveness.
AB - To date, a variety of automated negotiation agents have been created. While each of these agents has been shown to be effective in negotiating with people in specific environments, they lack natural language processing support required to enable real-world types of interactions. In this paper we present NegoChat, the first negotiation 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' strategies must be modified to address partial agreements and issue-by-issue interactions. Second, we present NegoChat's negotiation algorithm. This algorithm is based on bounded rationality, and specifically Aspiration Adaptation Theory (AAT). As per AAT, issues are addressed based on people's typical urgency, or order of importance. 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, including the state-of-the-art KBAgent. Finally, we present a rigorous evaluation of NegoChat, showing its effectiveness.
KW - Chat agent
KW - Human-agent systems
KW - Negotiation
UR - http://www.scopus.com/inward/record.url?scp=84911440441&partnerID=8YFLogxK
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AN - SCOPUS:84911440441
T3 - 13th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2014
SP - 525
EP - 532
BT - 13th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2014
Y2 - 5 May 2014 through 9 May 2014
ER -