Challenges and Main Results of the Automated Negotiating Agents Competition (ANAC) 2019

Conference paper (2020)

Authors

Reyhan Aydoğan Interactive Intelligence - EEMCS , Özyeğin University
T. Baarslag Universiteit Utrecht, Centrum Wiskunde & Informatica (CWI)
Claus Aranha University of Tsukuba
C.M. Jonker Interactive Intelligence - EEMCS
Katsuhide Fujita National Institute of Advanced Industrial Science and Technology (AIST), Tokyo University of Agriculture and Technology
Johnathan Mell University of Southern California
Jonathan Gratch University of Southern California
Dave de Jonge IIIA-CSIC, Bellaterra
Yasser Mohammad NEC Corporation, National Institute of Advanced Industrial Science and Technology (AIST)
Shinji Nakadai NEC Corporation, National Institute of Advanced Industrial Science and Technology (AIST)
Satoshi Morinaga National Institute of Advanced Industrial Science and Technology (AIST), NEC Corporation
Hirotaka Osawa University of Tsukuba
Research Group
Interactive Intelligence (EEMCS) (TU Delft)
Copyright: © 2020 Reyhan Aydoğan, T. Baarslag, Katsuhide Fujita, Johnathan Mell, Jonathan Gratch, Dave de Jonge, Yasser Mohammad, Shinji Nakadai, Satoshi Morinaga, Hirotaka Osawa, Claus Aranha, C.M. Jonker
DOI: https://doi.org/10.1007/978-3-030-66412-1_23
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Published Date

2020

Language

English

Reuse Rights

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Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Intelligent Systems

Research Group

Interactive Intelligence

Abstract

The Automated Negotiating Agents Competition (ANAC) is a yearly-organized international contest in which participants from all over the world develop intelligent negotiating agents for a variety of negotiation problems. To facilitate the research on agent-based negotiation, the organizers introduce new research challenges every year. ANAC 2019 posed five negotiation challenges: automated negotiation with partial preferences, repeated human-agent negotiation, negotiation in supply-chain management, negotiating in the strategic game of Diplomacy, and in the Werewolf game. This paper introduces the challenges and discusses the main findings and lessons learnt per league.

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