Genetic-Aided Multi-Issue Bilateral Bargaining for Complex Utility Functions
In this M.S. Thesis, a non-mediated multi-issue bilateral bargaining model for complex utility functions is presented. Before the negotiation process, each agent samples its own utility function by means of a genetic algorithm (GA). During the negotiation process, genetic operators are applied over...
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| Formato: | tesis de maestría |
| Fecha de publicación: | 2010 |
| País: | España |
| Recursos: | Universitat Politècnica de València (UPV) |
| Repositorio: | RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia |
| Idioma: | inglés |
| OAI Identifier: | oai:riunet.upv.es:10251/13847 |
| Acesso em linha: | https://riunet.upv.es/handle/10251/13847 |
| Access Level: | acceso abierto |
| Palavra-chave: | Automated negotiation Multi-agent systems Soft computing LENGUAJES Y SISTEMAS INFORMATICOS Máster Universitario en Inteligencia Artificial, Reconocimiento de Formas e Imagen Digital-Màster Universitari en Intel·ligència Artificial, Reconeixement de Formes i Imatge Digital |
| Resumo: | In this M.S. Thesis, a non-mediated multi-issue bilateral bargaining model for complex utility functions is presented. Before the negotiation process, each agent samples its own utility function by means of a genetic algorithm (GA). During the negotiation process, genetic operators are applied over the opponent's and one's own proposals in order to sample new proposals that are interesting for both parties. |
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