Date: 2012
Type: Article
Probabilistic Rule-Based Argumentation for Norm-Governed Learning Agents
Artificial intelligence and Law, 2012, 20, 4, 383-420
RIVERET, Régis, ROTOLO, Antonino, SARTOR, Giovanni, Probabilistic Rule-Based Argumentation for Norm-Governed Learning Agents, Artificial intelligence and Law, 2012, 20, 4, 383-420
- https://hdl.handle.net/1814/26277
Retrieved from Cadmus, EUI Research Repository
This paper proposes an approach to investigate norm-governed learning agents which combines a logic-based formalism with an equation-based counterpart. This dual formalism enables us to describe the reasoning of such agents and their interactions using argumentation, and, at the same time, to capture systemic features using equations. The approach is applied to norm emergence and internalisation in systems of learning agents. The logical formalism is rooted into a probabilistic defeasible logic instantiating Dung’s argumentation framework. Rules of this logic are attached with probabilities to describe the agents’ minds and behaviours as well as uncertain environments. Then, the equation-based model for reinforcement learning, defined over this probability distribution, allows agents to adapt to their environment and self-organise.
Cadmus permanent link: https://hdl.handle.net/1814/26277
Full-text via DOI: 10.1007/s10506-012-9134-7
ISSN: 1572-8382; 0924-8463
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