CP-nets-based user preference learning in automated negotiation through completion and correction-Reference-Cited by-同舟云学术

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CP-nets-based user preference learning in automated negotiation through completion and correction

Published:2023-04-12 Issue:9 Volume:65 Page:3567-3590
ISSN:0219-1377
Container-title:Knowledge and Information Systems
language:en
Short-container-title:Knowl Inf Syst

Author:

Cai Jianlong,Zhan Jieyu,Jiang Yuncheng

Funder

National Natural Science Foundation of China

Project of Science and Technology in Guangzhou, China

Guangdong Province Universities Pearl River Scholar Funded Scheme

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Hardware and Architecture,Human-Computer Interaction,Information Systems,Software

Link

https://link.springer.com/content/pdf/10.1007/s10115-023-01872-z.pdf

Reference52 articles.

1. Lin K, Liu Y, Lu P et al (2022) Fuzzy constraint-based agent negotiation framework for doctor-patient shared decision-making. BMC Med Inform Decis Mak 22(1):1–17. https://doi.org/10.1186/s12911-022-01963-x

2. Rajavel R, Thangarathanam M (2021) Agent-based automated dynamic sla negotiation framework in the cloud using the stochastic optimization approach. Appl Soft Comput 101(107):040. https://doi.org/10.1016/j.asoc.2020.107040

3. Moghadam FS, Zarandi MHF (2022) Mitigating bullwhip effect in an agent-based supply chain through a fuzzy reverse ultimatum game negotiation module. Appl Soft Comput 116(108):278. https://doi.org/10.1016/j.asoc.2021.108278

4. Fiedler A (2022) An agent-based negotiation protocol for supply chain finance. Comput Ind Eng 168(108):136. https://doi.org/10.1016/j.cie.2022.108136

5. de Jonge D, Bistaffa F, Levy J (2022) Multi-objective vehicle routing with automated negotiation. Appl Intell. https://doi.org/10.1007/s10489-022-03329-2

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