Abstract
Syntactic pattern recognition-based agents have been proven to be a useful tool for constructing real-time process control intelligent systems. In the paper the problem of self-learning schemes in the agents is discussed. Learning capabilities are very important if practical applications of the agents are considered, since the agents should be able to accumulate knowledge about the environment and flexible react to the changes in the environment. As it is shown in the paper, the learning scheme in the agents can be based on a suitable grammatical inference algorithm.
Chapter PDF
References
Alquézar, R., Sanfeliu, A.: Recognition and learning of a class of context-sensitive languages described by augmented regular expressions. Pattern Recognition 30, 163–182 (1997)
Flasiński, M.: Automata-Based Multi-agent Model as a Tool for Constructing Real-Time Intelligent Control Systems. In: Dunin-Keplicz, B., Nawarecki, E. (eds.) CEEMAS 2001. LNCS (LNAI), vol. 2296, pp. 103–110. Springer, Heidelberg (2002)
Flasiński, M., Jurek, J.: Dynamically Programmed Automata for Quasi Context Sensitive Languages as a Tool for Inference Support in Pattern Recognition-Based Real-Time Control Expert Systems. Pattern Recognition 32, 671–690 (1999)
De La Higuera, C.: Current Trends in Grammatical Inference. In: Amin, A., Pudil, P., Ferri, F.J., Iñesta, J.M. (eds.) SPR 2000 and SSPR 2000. LNCS, vol. 1876, pp. 28–31. Springer, Heidelberg (2000)
De La Higuera, C.: A bibliographical study of grammatical inference. Pattern Recognition 38, 1332–1348 (2005)
Jurek, J.: Syntactic Pattern Recognition-Based Agents for Real-Time Expert Systems. In: Dunin-Keplicz, B., Nawarecki, E. (eds.) CEEMAS 2001. LNCS (LNAI), vol. 2296, pp. 161–168. Springer, Heidelberg (2002)
Jurek, J.: Towards grammatical inferencing of GDPLL(k) grammars for applications in syntactic pattern recognition-based expert systems. In: Rutkowski, L., Siekmann, J.H., Tadeusiewicz, R., Zadeh, L.A. (eds.) ICAISC 2004. LNCS (LNAI), vol. 3070, pp. 604–609. Springer, Heidelberg (2004)
Jurek, J.: Recent developments of the syntactic pattern recognition model based on quasi-context sensitive languages. Pattern Recognition Letters 26, 1011–1018 (2005)
Jurek, J.: Generalisation of a Language Sample for Grammatical Inference of GDPLL(k) Grammars. In: Computer Recognition Systems 2. Advances in Soft Computing series, pp. 282–288. Springer, Heidelberg (2007)
Negnevitsky, M.: Artificial Intelligence. A Guide to Intelligent Systems. Addison-Wesley, Reading (2002)
Niederberger, C., Gross, M.: Hierarchical and Heterogenous Reactive Agents for Real-Time Applications. Computer Graphics Forum 22, 323–331 (2003)
Russell, S.J., Norvig, P.: Artificial Intelligence: A Modern Approach, 2nd edn. Prentice-Hall, Englewood Cliffs (2002)
Soto, I., Garijo, M., Iglesias, C.A., Ramos, M.: An agent architecture to fulfill real-time requirements. In: Proceedings of the Fourth International Conference on Autonomous Agents, Barcelona, Spain, June 03–07, 2000, pp. 475–482 (2000)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Jurek, J. (2008). Grammatical Inference as a Tool for Constructing Self-learning Syntactic Pattern Recognition-Based Agents. In: Bubak, M., van Albada, G.D., Dongarra, J., Sloot, P.M.A. (eds) Computational Science – ICCS 2008. ICCS 2008. Lecture Notes in Computer Science, vol 5103. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69389-5_79
Download citation
DOI: https://doi.org/10.1007/978-3-540-69389-5_79
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-69388-8
Online ISBN: 978-3-540-69389-5
eBook Packages: Computer ScienceComputer Science (R0)