Print Email Facebook Twitter Integrating fairness in opponent modeling Title Integrating fairness in opponent modeling Author Trestioreanu, Ilinca (TU Delft Electrical Engineering, Mathematics and Computer Science; TU Delft Interactive Intelligence) Contributor Kuilman, S.K. (mentor) Cavalcante Siebert, L. (mentor) Weinmann, M. (graduation committee) Degree granting institution Delft University of Technology Programme Computer Science and Engineering Project CSE3000 Research Project Date 2022-06-22 Abstract Is there a way to incorporate fairness in the opponent modeling component of an automated agent? Since opponent modeling plays an important role in a negotiation strategy, it is reasonable to research how fairness can be integrated into this component, as it influences the outcome of the negotiation. A first step towards finding an answer to this question is to define fairness and how this definition can be translated to algorithmic fairness. The next step is to investigate already available opponent models and assess whether their strategy can be considered fair or not. This paper analyses the process of Bayesian learning in the context of opponent modeling, and tries to reveal possible flaws or strengths that the model has embedded in it, with the hope to add relevant information in the area of automated negotiation. Subject Opponent ModellingBayesian LearningFairness To reference this document use: http://resolver.tudelft.nl/uuid:213b3953-d0a6-441b-87d1-b6d52e5b188d Part of collection Student theses Document type bachelor thesis Rights © 2022 Ilinca Trestioreanu Files PDF Integrating_fairness_in_o ... deling.pdf 266.98 KB Close viewer /islandora/object/uuid:213b3953-d0a6-441b-87d1-b6d52e5b188d/datastream/OBJ/view