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All issues Volume 77 (2016) MATEC Web Conf., 77 (2016) 09004 References
Open Access
Issue
MATEC Web Conf.
Volume 77, 2016
2016 3rd International Conference on Mechanics and Mechatronics Research (ICMMR 2016)
Article Number 09004
Number of page(s) 5
Section Artificial Intelligence
DOI https://doi.org/10.1051/matecconf/20167709004
Published online 03 October 2016
  1. D.C. Luckham. Event Processing for Business: Organizing the Real-Time Enterprise. Wiley Press, Dec. 2011. [Google Scholar]
  2. K. Broda, K. Clark, R Miller, et al. SAGE: a logical agent-based environment monitoring and control system. Ambient intelligence, Lecture Notes in Computer Science, vol 5859. Springer, Berlin, Heidelberg, pp 112–117. [CrossRef] [Google Scholar]
  3. A.J. Demers, J. Gehrke, M. Hong, et al. Towards expressive publish/subscribe systems. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v 3896 LNCS, 2006, pp. 627–644. [Google Scholar]
  4. Y. Engel, O. Etzion. Towards proactive event-driven computing. In Proc. of Fifth ACM International Conference on Distributed Event-Based Systems, DEBS 2011, New York, pp.125–136. [Google Scholar]
  5. O. Etzion, P. Niblett. Event Processing in Action. Manning Publications, 2010. [Google Scholar]
  6. Q. S. Jia. On State Aggregation to Approximate Complex Value Functions in Large-Scale Markov Decision Processes. IEEE Transactions on Automatic Control, 56(2), pp. 333–344. [Google Scholar]
  7. Q. S. Jia and Q. C. Zhao. Strategy optimization for controlled Markov process with descriptive complexity constraint. Science China Series F: Inform. Sci., 52(11), pp. 1993–2005. [Google Scholar]
  8. C. Zhang, V. Lesser. Coordinated Multi-Agent Reinforcement Learning in Networked Distributed POMDPs. In Proc. of the 25th AAAI Conference on Artificial Intelligence and the 23rd Innovative Applications of Artificial Intelligence Conference, August 7, 2011, San Francisco, CA, United states, pp. 764–770. [Google Scholar]
  9. A. Fares, W. Gomaa. Multi-Agent Reinforcement Learning Control for Ramp Metering. Advances in Intelligent Systems and Computing, Vol. 1089, pp. 167–173. [Google Scholar]
  10. E. Wu, Y. Diao and S. Rizvi. High-performance complex event processing over streams. In Proc. of 2006 ACM SIGMOD international conference on Management of data, June 27-29, 2006, Chicago, IL, USA. [Google Scholar]
  11. Y.H Wang, K. Cao and X.M Zhang. Complex Event Processing over Distributed Probabilistic Event Streams. COMPUTERS & MATHEMATICS WITH APPLICATIONS, 66 (2013), pp1808–1821. [CrossRef] [Google Scholar]
  12. A. Pascale, M. Nicoli. Adaptive Bayesian network for traffic flow prediction. In Proc. of Statistical Signal Processing Workshop (SSP), 2011 IEEE, pp.177–180. [Google Scholar]
  13. M. Papageorgiou, H. Hadj-Salem, J. M. Blosseville. Alinea: A local feedback control law for on-ramp metering. Transportation Research Record (1320) (1991). [Google Scholar]
  14. R. Stranders, A. Farinelli, A. Rogers, et al. Decentralised coordination of mobile sensors using the max-sum algorithm. In IJCAI, issue 2009, pp.299–304. [Google Scholar]
  15. M. Behrisch, L. Bieker, J. Erdmann, et al. Sumo - simulation of urban mobility: An overview. Proceedings of the third international conference on advances in system simulation, Barcelona, Spain, October, pp. 63–68. [Google Scholar]

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