Effective Acceptance Strategy Using Deep Reinforcement Learning in Bilateral Multi-issue Negotiation-Reference-Cited by-同舟云学术

Effective Acceptance Strategy Using Deep Reinforcement Learning in Bilateral Multi-issue Negotiation

Published:2024 Issue:0 Volume:32 Page:2-9
ISSN:1882-6652
Container-title:Journal of Information Processing
language:en
Short-container-title:Journal of Information Processing

Author:

Matsuo Hyuga¹,Fujita Katsuhide²

Affiliation:

1. Tokyo University of Agriculture and Technology

2. Institute of Global Innovation Research, Tokyo University of Agriculture and Technology

Publisher

Information Processing Society of Japan

Link

https://www.jstage.jst.go.jp/article/ipsjjip/32/0/32_2/_pdf

Reference22 articles.

1. [1] Fiedler, A. and Sackmann, D.: Automated negotiation for supply chain finance, Mes, M., Lalla-Ruiz, E. and Voß, S. (Eds.), Computational Logistics, pp.130-141, Springer International Publishing (2021).

2. [2] Etukudor, C., Couraud, B., Robu, V., Früh, W.-G., Flynn, D. and Okereke, C.: Automated negotiation for peer-to-peer electricity trading in local energy markets, Energies, Vol.13, No.4, p.920 (2020).

3. [3] Chater, N., Misyak, J., Watson, D., Griffiths, N. and Mouzakitis, A.: Negotiating the traffic: Can cognitive science help make autonomous vehicles a reality?, Trends in Cognitive Sciences, Vol.22, No.2, pp.93-95 (2018).

4. [4] Aydoğan, R., Baarslag, T., Fujita, K. and Jonker, C.: The 13th International Automated Negotiating Agents Competition(ANAC2022) (2022), available from <https://web.tuat.ac.jp/~katfuji/ANAC2022/>.

5. [5] Kawaguchi, S., Fujita, K. and Ito, T.: Agentk: Compromising strategy based on estimated maximum utility for automated negotiating agents, New Trends in Agent-Based Complex Automated Negotiations, pp.137-144, Springer (2012).