A study of ex ante law enforcement in norm-governed learning agents

Authors:
Régis Riveret;Dídac Busquets;Jeremy Pitt;Giuseppe Contissa;Antonino Rotolo;Giovanni Sartor
Affiliations:
Department of Electrical and Electronic Eng., Imperial College London, UK;Department of Electrical and Electronic Eng., Imperial College London, UK;Department of Electrical and Electronic Eng., Imperial College London, UK;CIRSFID and Department of Legal Studies, University of Bologna, Bologna, Italy,European University Institute, Florence, Italy;CIRSFID and Department of Legal Studies, University of Bologna, Bologna, Italy;CIRSFID and Department of Legal Studies, University of Bologna, Bologna, Italy,European University Institute, Florence, Italy
Venue:
JSAI-isAI'12 Proceedings of the 2012 international conference on New Frontiers in Artificial Intelligence
Year:
2012

Citing 2
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Artificial Intelligence
Introduction to Reinforcement Learning

Introduction to Reinforcement Learning

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Abstract

We investigate ex ante law enforcement within a population of norm-governed learning agents using a probabilistic rule-based argumentation framework. We show that this formal framework can advantageously complete a traditional analysis based on expected utilities, in particular when hyper-rational or omniscient agents are not assumed. This has significant implications for the design of self-organising electronic institutions, where the cost of monitoring and enforcement of laws and norms has to be taken into consideration.