On social laws for artificial agent societies: off-line design
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Automated norm synthesis in an agent-based planning environment
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Exploiting domain knowledge to improve norm synthesis
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
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IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Evaluation of an automated mechanism for generating new regulations
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Automated synthesis of normative systems
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
IRON: a machine for the automated synthesis of normative systems
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
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Humans have developed jurisprudence as a mechanism to solve conflictive situations by using past experiences. Following this principle, we propose an approach to enhance a multi-agent system by adding an authority which is able to generate new regulations whenever conflicts arise. Regulations are generated by learning from previous similar situations, using a machine learning technique (based on Case-Based Reasoning) that solves new problems using previous experiences. This approach requires: to be able to gather and evaluate experiences; and to be described in such a way that similar social situations require similar regulations. As a scenario to evaluate our proposal, we use a simplified version of a traffic scenario, where agents are traveling cars. Our goals are to avoid collisions between cars and to avoid heavy traffic. These situations, when happen, lead to the synthesis of new regulations. At each simulation step, applicable regulations are evaluated in terms of their effectiveness and necessity. Overtime the system generates a set of regulations that, if followed, improve system performance (i.e. goal achievement).