Abstract
Humans usually use information about previous experiences to solve new problems. Following this principle, we propose an approach to enhance a multi-agent system by including an authority that generates new regulations whenever new conflicts arise. The authority uses a unsupervised version of classical Case-Based Reasoning to learn from previous similar situations and generate regulations that solve the new problem. The scenario used to illustrate and evaluate our proposal is a simulated traffic intersection where agents are traveling cars. A traffic authority observes the scenario and generates new regulations when collisions or heavy traffic are detected. At each simulation step, applicable regulations are evaluated in terms of their effectiveness and necessity in order to generate a set of regulations that, if followed, improve system performance. Empirical evaluation shows that the traffic authority succeeds in avoiding conflicting situations by automatically generating a reduced set of traffic rules.
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References
Agotnes, T., Wooldridge, M.: Optimal Social Laws. In: Proceedings of 9th International Conference on Autonomous Agents and Multiagent Systems, pp. 667–674 (2010)
Boella, G., van der Torre, L.: Regulative and constitutive norms in normative multiagent systems. In: Proceedings of KR 2004, pp. 255–265 (2004)
Busoniu, L., Babuska, R., de Schutter, B.: A comprehensive survey of multiagent reinforcement learning. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews 38(2), 156–172 (2008), http://dx.doi.org/10.1109/TSMCC.2007.913919
Campos, J., López-Sánchez, M., Esteva, M.: Multi-Agent System adaptation in a Peer-to-Peer scenario. In: ACM Symposium on Applied Computing - Agreement Technologies Track, pp. 735–739 (2009)
Dresner, K., Stone, P.: A multiagent approach to autonomous intersection management. Journal of Artificial Intelligence Research 31, 591–656 (2008)
Griffiths, N., Luck, M.: Norm Emergence in Tag-Based Cooperation. In: 9th International Workshop on Coordination, Organization, Institutions and Norms in Multi-Agent Systems, pp. 79–86 (2010)
van der Hoek, W., Roberts, M., Wooldridge, M.: Social laws in alternating time: Effectiveness, feasibility, and synthesis. Synthese 1, 156 (2007)
Kota, R., Gibbins, N., Jennings, N.: Decentralised structural adaptation in agent organisations. In: AAMAS Workshop Organised Adaptation in MAS, pp. 54–71 (2008)
Morales, J., López-Sánchez, M., Esteva, M.: Using Experience to Generate New Regulations. In: Proceedings of the 22th International Joint Conference on Artificial Intelligence, IJCAI (2011)
Savarimuthu, B., Cranefield, S., Purvis, M., Purvis, M.: Role model based mechanism for norm emergence in artificial agent societies. In: Sichman, J.S., Padget, J., Ossowski, S., Noriega, P. (eds.) COIN 2007. LNCS (LNAI), vol. 4870, pp. 203–217. Springer, Heidelberg (2008)
Shoham, Y., Tennenholtz, M.: On social laws for artificial agent societies: off-line design. Journal of Artificial Intelligence 73(1-2), 231–252 (1995)
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Morales, J., López-Sánchez, M., Esteva, M. (2011). Evaluation of an Automated Mechanism for Generating New Regulations. In: Lozano, J.A., Gámez, J.A., Moreno, J.A. (eds) Advances in Artificial Intelligence. CAEPIA 2011. Lecture Notes in Computer Science(), vol 7023. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25274-7_2
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DOI: https://doi.org/10.1007/978-3-642-25274-7_2
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