×

zbMATH — the first resource for mathematics

Norms and learning in probabilistic logic-based agents. (English) Zbl 1297.68224
Ågotnes, Thomas (ed.) et al., Deontic logic in computer science. 11th international conference, DEON 2012, Bergen, Norway, July 16-18, 2012. Proceedings. Berlin: Springer (ISBN 978-3-642-31569-5/pbk). Lecture Notes in Computer Science 7393. Lecture Notes in Artificial Intelligence, 123-138 (2012).
Summary: This paper proposes a new simulation approach for investigating phenomena such as norm emergence and internalization in large groups of learning agents. We define a probabilistic defeasible logic instantiating Dung’s argumentation framework. Rules of this logic are attached to probabilities and describe the agents’ minds and behaviour. We thus adopt the paradigm of reinforcement learning over this probability distribution to allow agents to adapt to their environment.
For the entire collection see [Zbl 1250.03001].

MSC:
68T42 Agent technology and artificial intelligence
68T27 Logic in artificial intelligence
PDF BibTeX XML Cite
Full Text: DOI