Abstract. Similarly to institutions in human societies, an Electronic Institution (EI) provides a structured framework for a Multi-Agent System (MAS) to regulate agents' interactions. However, current EIs cannot regulate a previously existing dynamic social system and deal with its agent population behaviour changes. This paper suggests a solution consisting of two EI extensions to incorporate situatedness and adaptation to the institution. These two properties are usually present at an agent level, but this paper studies how to bring them to an organisational level. While exposing our approach, we use a tra c scenario example to illustrate its concepts.

Organisations in Multi-Agent Systems (MAS) have proven to be successful in regulating agent societies. Nevertheless, changes in agents' behaviour or in the dynamics of the environment may lead to a poor fullment of the system's purposes, and so the entire organisation needs to be adapted. In this paper we focus on endowing the organisation with adaptation capabilities, instead of expecting agents to be capable of adapting the organisation by

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.

Existing organisational centred multi-agent systems regulate agents' activities. However, population/environmental changes may lead to a poor fulfilment of system's goals, and therefore, adapting the whole organisation becomes key. In this paper, we propose to use Case-Based Reasoning learning to adapt norms that regulate agents' behaviour. Moreover, we empirically evaluate this approach in a P2P scenario.

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