A Norm-Based Probabilistic Decision-Making Model for Autonomic Traffic Networks
- 1.1k Downloads
We propose a norm-based agent-oriented model of decision-making of semi-autonomous vehicles in urban traffic scenarios. Computational norms are used to represent the driving rules and conventions that influence the distributed decision-making process of the vehicles. As norms restrict the admissible behaviour of the agents, we propose to represent them as constraints, and we express the agents’ individual and group decision-making in terms of distributed constraint optimization problems. The uncertain nature of the driving environment is reflected in our model through probabilistic constraints – collective norm compliance is considered as a stochastic distributed constraint optimization problem. In this paper, we introduce the basic conceptual and algorithmic ingredients of our model, including the norms provisioning and enforcement mechanisms (where electronic institutions are used), the norm semantics, as well as methods of the agents’ cooperative decision-making. For motivation and illustration of our approach, we study a cooperative multi-lane highway driving scenario; we propose a formal model, and illustrate our approach by a small example.
Keywordscooperative traffic management multi-agent decision-making computational norms and institutions probabilistic distributed constraint optimization resampling
Unable to display preview. Download preview PDF.
- 1.Görmer, J., Ehmke, J., Fiosins, M., Schmidt, D., Schumacher, H., Tchouankem, H.: Decision support for dynamic city traffic management using vehicular communication. In: Proc. of 1st Int. Conf. on Simulation and Modeling Methodologies, Technologies and Applications (SIMULTECH), pp. 327–332. SciTePress (2011)Google Scholar
- 2.Müller, J.P. (ed.): The Design of Intelligent Agents. LNCS (LNAI), vol. 1177. Springer, Heidelberg (1996)Google Scholar
- 4.Dresner, K., Stone, P.: Multiagent traffic management: A reservation-based intersection control mechanism. In: 3rd International Joint Conference on Autonomous Agents and Multiagent Systems, pp. 530–537 (July 2004)Google Scholar
- 5.Boissier, O., Padget, J., Dignum, V., Lindemann, G., Matson, E., Ossowski, S., Sichman, J.S., Vázquez-Salceda, J. (eds.): ANIREM and OOOP 2005. LNCS (LNAI), vol. 3913. Springer, Heidelberg (2006)Google Scholar
- 8.Lacey, N., Hexmoor, H.: A constraint-based approach to multiagent planning. In: Proc. of 13th Midwest AI and Cognitive Science Conference, pp. 1–6 (2002)Google Scholar
- 9.Yokoo, M.: Distributed Constraint Satisfaction: Foundations of Cooperation in Multi-agent Systems. Springer Series on Agent Technology. Springer (2001)Google Scholar
- 10.Bowring, E., Tambe, M., Yokoo, M.: Multiply-constrained distributed constraint optimization. In: Proc. of the 5th Int. Joint Conf. on Autonomous Agents and Multiagent Systems (AAMAS 2006), pp. 1413–1420 (2006)Google Scholar
- 11.Fiosins, M., Fiosina, J., Müller, J.P., Görmer, J.: Reconciling strategic and tactical decision making in agent-oriented simulation of vehicles in urban traffic. In: Proc. of the 4th Int. ICST Conf. Simulation Tools and Techniques, pp. 144–151. ACM Digital Library (2011)Google Scholar
- 12.Huhn, M., Müller, J.P., Görmer, J., Homoceanu, G., Le, N.T., Märtin, L., Mumme, C., Schulz, C., Pinkwart, N., Müller-Schloer, C.: Autonomous agents in organized localities regulated by institutions. In: Proc. of IEEE DEST 2011, pp. 54–61 (2011)Google Scholar
- 13.Fiosina, J., Fiosins, M.: Cooperative kernel-based forecasting in decentralized multi-agent systems for urban traffic networks. In: Proc. of Ubiquitous Data Mining (UDM) Workshop at the 20th European Conf. on Artif. Intelligence, pp. 3–7 (2012)Google Scholar