Abstract
In this paper a system for monitoring a biotechnical system is presented. Some of the events associated with the state of metabolic reactions are indistinguishable, mainly due to lack of appropriate sensors and measurement capabilities. Therefore, a solution is needed to identify the state in which the reactor currently is, based on the information and measurements available in real time. The solution presented in this paper is based on a multi agent system, in which particular agents identify the state of the process based on selected measurements. Those partial identification results are than used to provide a cumulative result by means of a voting mechanism between all the particular agents. Such a solution seems to be a promising alternative to standard monitoring and identification methods.
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References
Jennings, N.R., Sycara, K., Wooldridge, M.: A Roadmap of Agent Research and Development. In: Autonomous Agents and Multi–Agent Systems, vol. 1, pp. 7–38. Kluwer Academic Publishers, Boston (1998)
Weiss, G. (ed.): MultiAgent Systems: A Modern Approach to Distributed Artificial Intelligence. MIT Press, Cambridge (1999)
Sutton, R.S., Barto, A.G.: Reinforcement learning - an introduction. MIT Press, Cambridge (1998)
Watkins, C.J.C.H.: Learning from delayed rewards. Ph.D. thesis. University of Cambridge, England (1989)
Watkins, C.J.C.H., Dayan, P.: Technical note: Q-learning. Machine Learning 8, 279–292 (1992)
Cassandras, C.G., Lafortune, S.: Introduction to discrete event system. Springer, New York (2008)
Werbos, P.J.: Approximate dynamic programming for real-time control and neural modeling. In: Handbook of Intelligent Control. Van Nostrand Reinhold, New York (1992)
Kim, J.-H., Lewis, F.L.: Model-free H ∞ control design for unknown linear discrete-time systems via Q-learning with LMI. Automatica 46, 1320–1326 (2010)
Murakoshi, K., Mizuno, J.: A parameter control method in reinforcement learning to rapidly follow unexpected environmental changes. BioSystems 77, 109–117 (2004)
Croll, P.R., Sharkey, A.J.C., Bass, J.M., Sharkey, N.E., Fleming, P.J.: Dependable, Intelligent Voting for Real-time Control Software. Engng. Applic. Artif. Intell. 8, 615–623 (1995)
Latif-Shabgahi, G., Bennet, S., Bass, J.M.: Smoothing voter: a novel voting algorithm for handling multiple errors in fault-tolerant control systems. Microprocessors and Microsystems 27, 303–313 (2003)
Conitzer, V.: Comparing multiagent systems research in combinatorial auctions and voting. Ann. Math. Artif. Intell. 58, 239–259 (2010)
Hong, J., Prabhu, V.V.: Distributed Reinforcement Learning Control for Batch Sequencing and Sizing in Just-In-Time Manufacturing Systems. Applied Intelligence 20, 71–87 (2004)
Ohshita, T., Shin, J.-S., Miyazaki, M., Lee, H.-H.: A cooperative behavior learning control of multi-robot using trace information. Artif. Life Robotics 13, 144–147 (2008)
Park, K.-H., Kim, Y.-J., Kim, J.-H.: Modular Q-learning based multi-agent cooperation for robot soccer. Robotics and Autonomous Systems 35, 109–122 (2001)
Distante, C., Anglani, A., Taurisano, F.: Target Reaching by Using Visual Information and Q-learning Controllers. Autonomous Robots 9, 41–50 (2000)
Kirchner, F.: Q-learning of complex behaviours on a six-legged walking machine. Robotics and Autonomous Systems 25, 253–262 (1998)
Rahimiyan, M., Mashhadi, M.R.: Supplier’s optimal bidding strategy in electricity pay-as-bid auction: Comparison of the Q-learning and a model-based approach. Electric Power Systems Research 78, 165–175 (2008)
Tillotson, P.R.J., Wu, Q.H., Hughes, P.M.: Multi-agent learning for routing control within an Internet environment. Engineering Applications of Artificial Intelligence 17, 179–185 (2004)
Syafiie, S., Tadeo, F., Martinez, E.: Model-free learning control of neutralization processes using reinforcement learning. Engineering Applications of Artificial Intelligence 20, 767–782 (2007)
Wilson, J.A., Martinez, E.C.: Neuro-fuzzy modeling and control of a batch process involving simultaneous reaction and distillation. Computers Chem. Engng. 21, S1233–S1238 (1997)
Momot, A., Małysiak-Mrozek, B.z., Kozielski, S., Mrozek, D., Hera, Ł., Górczyńska-Kosiorz, S., Momot, M.: Improving Performance of Protein Structure Similarity Searching by Distributing Computations in Hierarchical Multi-Agent System. In: Pan, J.-S., Chen, S.-M., Nguyen, N.T. (eds.) ICCCI 2010. LNCS, vol. 6421, pp. 320–329. Springer, Heidelberg (2010)
Choiński, D., Metzger, M., Nocoń, W.: Multiscale three-phase flow simulation dedicated to model based control. In: Bubak, M., van Albada, G.D., Dongarra, J., Sloot, P.M.A. (eds.) ICCS 2008, Part II. LNCS, vol. 5102, pp. 261–270. Springer, Heidelberg (2008)
Nocoń, W., Metzger, M.: Predictive Control of Decantation in Batch Sedimentation Process. AICHE Journal 56, 3279–3283 (2010)
Ni, B.-J., Fanf, F., Rittmann, B.E., Yu, H.-Q.: Modeling Microbial Products in Activated Sludge under Feast-Famine Conditions. Environ. Sci. Technol. 43, 2489–2497 (2009)
Cassandras, C.G., Lafortune, S.: Introduction to discrete event system. Springer, New York (2008)
Partalas, I., Feneris, I., Vlahavas, I.: Multi-Agent Reinforcement Learning using Strategies and Voting. In: 19th IEEE International Conference on Tools with Artificial Intelligence, pp. 318–324 (2007)
Gołacki, M., Koźlak, J., Żabińska, M.: Holonic-Based Environment for Solving Transportation Problems. In: Mařík, V., Strasser, T., Zoitl, A. (eds.) HoloMAS 2009. LNCS, vol. 5696, pp. 193–202. Springer, Heidelberg (2009)
Metzger, M., Choinski, D., Nocon, W.: Pilot switcheable SBR and SOCP processes for biological wastewater treatment. Activity Report – Institute of Automatic Control, Gliwice (2010)
National Instruments documentation, Using the LabVIEW Shared Variable, http://zone.ni.com/devzone/cda/tut/p/id/4679
Nocoń, W.: Requirement Specification for Agent-Based Cooperative Control of Dynamical Systems. In: Luo, Y. (ed.) CDVE 2010. LNCS, vol. 6240, pp. 270–277. Springer, Heidelberg (2010)
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Choinski, D., Metzger, M., Nocon, W. (2011). Voting in Multi-Agent System for Improvement of Partial Observations. In: O’Shea, J., Nguyen, N.T., Crockett, K., Howlett, R.J., Jain, L.C. (eds) Agent and Multi-Agent Systems: Technologies and Applications. KES-AMSTA 2011. Lecture Notes in Computer Science(), vol 6682. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22000-5_37
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DOI: https://doi.org/10.1007/978-3-642-22000-5_37
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