Agent Decision using Granular Computing in Traffic System

In recent years multi-agent systems have emerged as one of the interesting architectures facilitating distributed collaboration and distributed problem solving. Each node (agent) of the network might pursue its own agenda, exploit its environment, develop its own problem solving strategy and establish required communication strategies. Within each node of the network, one could encounter a diversity of problem-solving approaches. Quite commonly the agents can realize their processing at the level of information granules that is the most suitable from their local points of view. Information granules can come at various levels of granularity. Each agent could exploit a certain formalism of information granulation engaging a machinery of fuzzy sets, interval analysis, rough sets, just to name a few dominant technologies of granular computing. Having this in mind, arises a fundamental issue of forming effective interaction linkages between the agents so that they fully broadcast their findings and benefit from interacting with others.





References:
[1] O. Acampora, G., Loia, V.: A Proposal of Ubiquitous Fuzzy Computing
for Ambient Intelligence. Information Sciences 178(3), 631-646 (2008)
[2] Bouchon-Meunier, B.: Aggregation and Fusion of Imperfect
Information. Physica-Verlag, Heidelberg (1998)
[3] Cheng, C.B., Chan, C.C.H., Lin, K.C.: Intelligent Agents for Emarketplace:
Negotiation with Issue Trade-offs by Fuzzy Inference
Systems. Decision Support Systems 42(2), 626- 638 (2006)
[4] Doctor, F., Hagras, H., Callaghan, V.: A Type-2 Fuzzy Embedded Agent
to Realise Ambient Intelligence in Ubiquitous Computing
Environments. Information Sciences 171(4),309-334 (2005)
[5] Grzymala-Busse J. W., Stefanowski J.: Three discretization methods for
rule induction. International Journal of Intelligent Systems 16(1), 29-38
(2001).
[6] Kwon, O., Im, G.P., Lee, K.C.: MACE-SCM: A Multi-Agent and Case-
Based Reasoning Collaboration Mechanism for Supply Chain
Management under Supply and Demand Uncertainties. Expert Systems
with Applications 33(3), 690-705 (2007)
[7] Pawlak, Z.: Rough sets. Int. J. Comput. Inform. Sci. 11, 341-356 (1982)
[8] Pawlak, Z.: Rough Sets. Theoretical Aspects of Reasoning About Data.
Kluwer Academic Publishers, Dordrecht (1991)
[9] Pawlak, Z., Busse, J.G., Slowinski, R., Ziarko, R.W.: Rough sets.
Commun. ACM. 38(11), 89-95 (1995)
[10] Pawlak, Z., Skowron, A.: Rudiments of Rough Sets. Information
Sciences 177(1), 3-27 (2007)
[11] Pedrycz, W.: Knowledge-Based Clustering. J. Wiley, Hoboken (2005)
[12] Yao, Y.Y.: Two Views of the Theory of Rough Sets in Finite Universes.
Int. J. Approximate Reasoning. 15, 291-317 (1996)
[13] Yao, Y.Y.: Probabilistic Approaches to Rough Sets. Expert Systems
20(5), 287-297 (2003)
[14] Yu, R., Iung, B., Panetto, H.: A Multi-Agents Based E-maintenance
System with Case based Reasoning Decision Support. Engineering
Applications of Artificial Intelligence 16(4), 321-333 (2003)
[15] Wang, T.W., Tadisina, S.K.: Simulating Internet-based Collaboration: A
Cost-benefit Case Study using a Multi-agent Model. Decision Support
Systems 43(2), 645-662 (2007)
[16] Zadeh, L.A.: Toward a Generalized Theory of Uncertainty (GTU)-an
Outline. Information Sciences 172, 1-40 (2005)