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Título: | Robust regulation adaptation in multi-agent systems |
Autor: | Campos, Jordi; López-Sánchez, Maite CSIC ORCID ; Salamo, Maria; Avila, Pedro; Rodríguez-Aguilar, Juan Antonio CSIC ORCID CVN | Palabras clave: | Organisation Case-based Regulation Reasoning Adaptation Machine Learning Centred MAS |
Fecha de publicación: | 2013 | Editor: | Association for Computing Machinery | Citación: | ACM Transactions on Autonomous and Adaptive Systems 8 (3), 2013 | Resumen: | Adaptive organisation-centred multi-agent systems can dynamically modify their organisational components to better accomplish their goals. Our research line proposes an abstract distributed architecture (2- LAMA) to endow an organisation with adaptation capabilities. This article focuses on regulation-adaptation based on a machine learning approach, in which adaptation is learned by applying a tailored case-based reasoning method. We evaluate the robustness of the system when it is populated by non compliant agents. The evaluation is performed in a peer-to-peer sharing network scenario. Results show that our proposal significantly improves system performance and can cope with regulation violators without incorporating any specific regulation-compliance enforcement mechanisms. © 2013 ACM. | URI: | http://hdl.handle.net/10261/133778 | DOI: | 10.1145/2517328 | Identificadores: | doi: 10.1145/2517328 issn: 1556-4665 |
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