Abstract Humans have developed jurisprudence as a mechanism to solve con?ictive 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 con?icts 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 simpli?ed version of a traf?c scenario, where agents are traveling cars. Our goals are to avoid collisions between cars and to avoid heavy traf?c. 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).