Automated Synthesis of Compact Normative Systems
Most normative systems make use of explicit representations of norms (namely, obligations, prohibitions, and permissions) and associated mechanisms to support the self-regulation of open societies of self-interested and autonomous agents. A key problem in research on normative systems is that of how...
| Autores: | , , , , |
|---|---|
| Tipo de documento: | artigo |
| Estado: | Versión aceptada para publicación |
| Data de publicação: | 2015 |
| País: | España |
| Recursos: | Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya) |
| Repositório: | Recercat. Dipósit de la Recerca de Catalunya |
| OAI Identifier: | oai:recercat.cat:2445/124912 |
| Acesso em linha: | https://hdl.handle.net/2445/124912 |
| Access Level: | Acceso aberto |
| Palavra-chave: | Intel·ligència artificial Intel·ligència artificial distribuïda Sistemes multiagent Artificial intelligence Distributed artificial intelligence Multiagent systems |
| Resumo: | Most normative systems make use of explicit representations of norms (namely, obligations, prohibitions, and permissions) and associated mechanisms to support the self-regulation of open societies of self-interested and autonomous agents. A key problem in research on normative systems is that of how to synthesise effective and efficient norms. Manually designing norms is time consuming and error prone. An alternative is to automatically synthesise norms. However, norm synthesis is a computationally complex problem. We present a novel online norm synthesis mechanism, designed to synthesise compact normative systems. It yields normative systems composed of concise (simple) norms that effectively coordinate a multiagent system (MAS) without lapsing into overregulation. Our mechanism is based on a central authority that monitors a MAS, searching for undesired states. After detecting undesirable states, the central authority then synthesises norms aimed to avoid them in the future. We demonstrate the effectiveness of our approach through experimental results. |
|---|