COMB : scalable concession-driven opponent models using Bayesian learning for preference learning in bilateral multi-issue automated negotiation
Shengbo Chang, Katsuhide Fujita
| Year of publication: |
2024
|
|---|---|
| Authors: | Chang, Shengbo ; Fujita, Katsuhide |
| Published in: |
Group decision and negotiation. - Dordrecht [u.a.] : Springer Science + Business Media B.V., ISSN 1572-9907, ZDB-ID 1478683-7. - Vol. 33.2024, 5, p. 1143-1190
|
| Subject: | Bayesian learning | Behavior assumption | Multi-issue negotiation | Opponent modeling | Preference learning | Lernprozess | Learning process | Verhandlungstheorie | Bargaining theory | Lernen | Learning | Bayes-Statistik | Bayesian inference | Spieltheorie | Game theory | Präferenztheorie | Theory of preferences | Verhandlungen | Negotiations | Experiment |
Online Resource
| Type of publication: | Article |
|---|---|
| Type of publication (narrower categories): | Aufsatz in Zeitschrift ; Article in journal |
| Language: | English |
| Other identifiers: | 10.1007/s10726-024-09889-7 [DOI] |
| Source: | ECONIS - Online Catalogue of the ZBW |
Persistent link: https://www.econbiz.de/10015186394