A Robust regulation adaptation in multi-agent systems

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 paper focuses on regulationadaptation 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 noncompliant agents. The evaluation is performed in a peer-to-peer sharing network scenario. Results show that our proposal significantly increases system performance and can cope with regulation violators without incorporating any specific regulation-compliance enforcement mechanism.