MAS architecture and knowledge model for vehicles data communication
Abstract Completely autonomous vehicles in traffic should allow to decrease the number of road accident victims greatly, and should allow gains in terms of performance and economy. Modelling the vehicles interaction, and especially knowledge sharing, is one of the main challenges to optimize traffic flow with autonomous vehicles. We propose in this paper a model of knowledge communication between mobile agents on a traffic network. The model of knowledge and of interaction enables to propagate new knowledge without overloading the system with a too large number of communications. For that, only the new knowledge is communicated, and two agents communicate the same knowledge only once. Moreover, in order to allow agents to update their knowledge (perceived or created), a notion of degradation is used. A simulator has been built to evaluate the proposal, before to implement it in mobile robots. Some results of the simulator are proposed in this article.
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BAZZAN, A.L. A distributed approach for coordination of traffic signal agents. Autonomous Agents and Multi-Agent Systems, 10, 131–164, 2005.
BAZZAN A.L.C., DE OLIVEIRA D., and C. DA SILVA B. Learning in groups of traffic signals. Engineering Applications of Artificial Intelligence, 23(4):560 – 568, 2010.
BOUZEGHOUB, Mokrane. A framework for analysis of data freshness. In Proc. of the 2004 international workshop on Information quality in information systems (IQIS '04). ACM, New York, NY, USA, 59-67, 2004.
DIJKSTRA, E.W. A note on two problems in connexion with graphs. Numerische Mathematik 1, 269–271, 1959.
DRESNER,K., STONE, P. Mitigating catastrophic failure at intersections of autonomous vehicles. In: AAMAS Workshop on Agents in Traffic and Transportation, 78–85. Estoril, Portugal, 2008.
FERBER,J. Multi-agent systems – an introduction to distributed artificial intelligence. Addison- Wesley-Longman, 1999.
JATOWT, Adam, KAWAI, Yukiko, TANAKA, Katsumi. Calculating content recency based on timestamped and non-timestamped sources for supporting page quality estimation. In Proceedings of the 2011 ACM Symposium on Applied Computing (SAC '11). ACM, New York, NY, USA, 1151-1158, 2011.
JENNINGS, N.R., SYCARA, K., WOOLDRIDGE, M. A roadmap of agent research and development. Int. Journal of Autonomous Agents and Multi-Agent Systems 1(1), 7–38, 1998.
JØSANG, Audun, ISMAIL, Roslan, BOYD, Colin. A survey of trust and reputation systems for online service provision. Decision Support Systems, 43(2), 618-644, 2007.
JUNG, Youna, KIM, Minsoo, MASOUMZADEH, Amirreza, JOSHI, James B. D. A survey of security issue in multi-agent systems. Artificial Intelligence Review, 37, 239–260, 2012.
KETENCI U-G., AUBERLET J-M., BRÉMOND R., GRISLIN-LE STRUGEON E. (2010). Impact of attentional factors in a multi-agent traffic simulation. Proc. of the 23rd Annual Conference on Computer Animation and Social Agents, CASA 2010, Saint-Malo, France, 2010.
KHALEGHI, Bahador, KHAMIS, Alaa, KARRAY, Fakhreddine O., RAZAVI, Saiedeh N. Multisensor data fusion: A review of the state-of-the-art, Information Fusion, in Press, 2011.
LIEBERMAN, E., RATHI, A. Traffic flow theory. Oak Ridge National Laboratory, Chapter Traffic simulation, 1997.
MANDIAU, R., CHAMPION, A., AUBERLET, J.M., ESPIE, S., KOLSKI, C. Behaviour based on decision matrices for a coordination between agents in a urban traffic simulation. Appl. Intell. 28(2), 121–138, 2008.
PINYOL, Isaac, SABATER-MIR, Jordi. Computational trust and reputation models for open multi-agent systems: a review, Artificial Intelligence Review, 1-25, 2011.
POPOVICI, D., DESERTOT, M., LECOMTE, S., PEON, N. Context-aware transportation services (CATS) framework for mobile environments. Int. Journal of Next-Generation Computing 2(1), 2011.
REECE, D. A., SHAFER, S. A. A computational model of driving for autonomous vehicles. Transportation Research 27 (1), 23–50, 1993.
RUSKIN, H.J., WANG, R. Modeling traffic flow at an urban unsignalized intersection. In Proc. of the Int. Conf. on Computational Science-Part I, ICCS ’02, 381– 390. Springer-Verlag, London, UK, 2002.
VASIRANI M. and OSSOWSKI S. A computational market for distributed control of urban
road traffic systems. IEEE TRANSACTIONS on Intelligent Transportation Systems, 12(2):313–321, June 2011.
VERCOUTER, L., JAMONT, J.P. Lightweight trusted routing for wireless sensor networks. In:Y.Demazeau, M. Pechoucek, J.M. Corchado, J.B. Pérez (eds.) PAAMS, Advances in Intelligent and Soft Computing 88, 87–96. Springer, 2011.
Vissim 4.10. User Manual. Technical Report. PTV Planung Transport Verkehr AG:Karlsruhe, Germany, 2005.
YAO, JingTao, RAGHAVAN, Vijay V., WU, Zonghuan. Web information fusion: A review of the state of the art, Information Fusion, 9(4), 446-449, 2008.
Adam, E., Grislin-Le Strugeon, E., & Mandiau, R. (2013). MAS architecture and knowledge model for vehicles data communication. ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 1(1), 23–31. https://doi.org/10.14201/ADCAIJ2012112331
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