Abstract
The following chapter explores learning internet agents. In recent years, with the massive increase in the amount of available information on the Internet, a need has arisen for being able to organize and access that data in a meaningful and directed way. Many well-explored techniques from the field of AI and machine learning have been applied in this context. In this paper, special emphasis is placed on neural network approaches in implementing a learning agent. First, various important approaches are summarized. Then, an approach for neural learning internet agents is presented, one that uses recurrent neural networks for the learning of classifying a textual stream of information. Experimental results are presented showing that a neural network model based on a recurrent plausibility network can act as a scalable, robust and useful news routing agent. concluding section examines the need for a hybrid integration of various techniques to achieve optimal results in the problem domain specified, in particular exploring the hybrid integration of Preference Moore machines and recurrent networks to extract symbolic knowledge.
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Balabanovic, M., Shoham, Y.: Learning information retrieval agents: Experiments with automated web browsing. In: Proceedings of the 1995 AAAI Spring Symposium on Information Gathering from Heterogeneous, Distributed Environments, Stanford, CA (1995)
Balabanovic, M., Shoham, Y., Yun, Y.: An adaptive agent for automated web browsing. Technical Report CS-TN-97-52, Stanford University (1997)
Cohen, W.: Learning rules that classify e-mail. In: AAAI Spring Symposium on Machine Learning in Information Access, Stanford, CA (1996)
Cooley, R., Mobasher, B., Srivastava, J.: Web mining: Information and pattern discovery on the world wide web. In: International Conference on Tools for Artificial Intelligence, Newport Beach, CA (November 1997)
Craven, M., DiPasquo, D., Freitag, D., McCallum, A., Mitchell, T., Nigam, K., Slattery, S.: Learning to extract symbolic knowledge from the world wide web. In: Proceedings of the 15th National Conference on Artificial Intelligence, Madison, WI (1998)
Edwards, P., Bayer, D., Green, C.L., Payne, T.R.: Experience with learning agents which manage internet-based information. In: AAAI Spring Symposium on Machine Learning in Information Access, Stanford, CA, pp. 31–40 (1996)
Elman, J.L.: Finding structure in time. Technical Report CRL 8901, University of California, San Diego, CA (1988)
Elman, J.L.: Distributed representations, simple recurrent networks, and grammatical structure. Machine Learning 7, 195–226 (1991)
Freitag, D.: Information extraction from html: Application of a general machine learning approach. In: National Conference on Artificial Intelligence, Madison, Wisconsin, pp. 517–523 (1998)
Fuernkranz, J., Mitchell, T., Riloff, E.: A case study in using linguistic phrases for text categorization on the WWW. In: Proceedings of the AAAI 1998 Workshop on Learning for Text Categorisation, Madison, WI (1998)
Giles, L., Omlin, C.W.: Extraction, insertion and refinement of symbolic rules in dynamically driven recurrent neural networks. Connection Science 5, 307–337 (1993)
Haykin, S.: Neural Networks: A Comprehensive Foundation. Macmillan College Publishing Company, New York (1994)
Hendler, J.: Developing hybrid symbolic/connectionist models. In: Barnden, J.A., Pollack, J.B. (eds.) Advances in Connectionist and Neural Computation Theory. High Level Connectionist Models, vol. 1, pp. 165–179. Ablex Publishing Corporation, Norwood (1991)
Holte, R., Drummond, C.: A learning apprentice for browsing. In: AAAI Spring Symposium on Software Agents, Stanford, CA (1994)
Honavar, V.: Symbolic artificial intelligence and numeric artificial neural networks: towards a resolution of the dichotomy. In: Sun, R., Bookman, L.A. (eds.) Computational Architectures integrating Neural and Symbolic Processes, pp. 351–388. Kluwer, Boston (1995)
Honkela, S.: Self-organizing maps in symbol processing. In: Sun, R., Wermter, S. (eds.) Hybrid Neural Systems 1998. LNCS, vol. 1778, pp. 348–362. Springer, Heidelberg (2000)
Hoyle, M.A., Lueg, C.: Open SESAME: A look at personal assisitants. In: Proceedings of the Interanational Conference on the Practical Applications of Intelligent Agents and Multi-Agent Technology, London, pp. 51–56 (1997)
Hull, D., Pedersen, J., Schutze, H.: Document routing as statistical classification. In: AAAI Spring Symposium on Machine Learning in Information Access, Stanford, CA (1996)
Joachims, T.: Text categorization with support vector machines: learning with many relevant features. In: Proceedings of the European Conference on Machine Learning, Chemnitz, Germany (1998)
Joachims, T., Freitag, D., Mitchell, T.: Webwatcher: A tour guide for the world wide web. In: Fifteenth International Joint Conference on Articial Intelligence, Nagoya, Japan (1997)
Jordan, M.I.: Attractor dynamics and parallelism in a connectionist sequential machine. In: Proceedings of the Eighth Conference of the Cognitive Science Society, Amherst, MA, pp. 531–546 (1986)
Kaski, S., Honkela, T., Lagus, K., Kohonen, T.: WEBSOM - self-organizing maps of document collections. Neurocomputing 21, 101–117 (1998)
Kohonen, T.: Self-Organization and Associative Memory, 3rd edn. Springer, Berlin (1989)
Kohonen, T.: Self-Organizing Maps. Springer, Berlin (1995)
Kohonen, T.: Self-organisation of very large document collections: State of the art. In: Proceedings of the International Conference on Ariticial Neural Networks, pp. 65–74. Skovde, Sweden (1998)
Lawrence, S., Giles, C.L.: Searching the world wide web. Science 280, 98–100 (1998)
Lewis, D.D.: Reuters-21578 text categorization test collection (1997), http://www.research.att.com/~lewis
Liere, R., Tadepalli, P.: The use of active learning in text categorisation. In: AAAI Spring Symposium on Machine Learning in Information Access, Stanford, CA (1996)
Lin, T., Horne, B.G., Tino, P., Giles, C.L.: Learning long-term dependencies in NARX recurrent neural networks. IEEE Transactions on Neural Networks 7(6), 1329–1338 (1996)
Menczer, F., Belew, R., Willuhn, W.: Articial life applied to adaptive informa- tion agents. In: Proceedings of the 1995 AAAI Spring Symposium on Information Gathering from Heterogeneous, Distributed Environments (1995)
Michie, D., Spiegelhalter, D.J., Taylor, C.C. (eds.): Machine Learning, Neural and Statistical Classification. Ellis Horwood, New York (1994)
Miikkulainen, R.: Subsymbolic Natural Language Processing. MIT Press, Cambridge (1993)
Mitchell, T.M.: Machine Learning. WCB/McGraw-Hill, New York (1997)
Nigam, K., McCallum, A., Thrun, S., Mitchell, T.: Learning to classify text from labeled and unlabeled documents. In: Proceedings of the National Conference on Articial Intelligence, Madison, WI (1998)
Papka, R., Callan, J.P., Barto, A.G.: Text-based information retrieval using exponentiated gradient descent. In: Advances in Neural Information Processing Systems, vol. 9. MIT Press, Denver (1997)
Perkowitz, M., Etzioni, O.: Adaptive web sites: an AI challenge. In: International Joint Conference on Articial Intelligence, Nagoya, Japan (1997)
Quinlan, J.R.: Induction of decision trees. Machine Learning 1, 81–106 (1986)
Siegelmann, H.T., Horne, B.G., Giles, C.L.: Computational capabilities of recurrent NARX neural networks. Technical Report CS-TR-3408, University of Maryland, College Park (1995)
Spiliopoulou, M., Faulstich, L.C., Winkler, K.: A data miner analyzing the navigational behavior of web users. In: ACAI-1999 Workshop on Machine Learning in User Modeling, Crete (July 1999)
Sum, J.P.F., Kan, W.K., Young, G.H.: A note on the equivalence of NARX and RNN. Neural Computing and Applications 8, 33–39 (1999)
Sun, R.: Integrating Rules and Connectionism for Robust Commonsense Reasoning. Wiley, New York (1994)
Sun, R., Peterson, T.: Multi-agent reinforcement learning: Weighting and partitioning. In: Neural Networks (1999)
Sutton, R.S., Barto, A.G.: Reinforcement Learning: an Introduction. MIT Press, Cambridge (1998)
Tecuci, G.: Building Intelligent Agents: An Apprenticeship Multistrategy Learning Theory, Methodology, Tool and Case Studies. Academic Press, San Diego (1998)
Wermter, S.: Hybrid Connectionist Natural Language Processing. Chapman and Hall, Thomson International (1995)
Wermter, S., Panchev, C., Arevian, G.: Hybrid neural plausibility networks for news agents. In: Proceedings of the National Conference on Artificial Intelligence, Orlando, USA, pp. 93–98 (1999)
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Wermter, S., Arevian, G., Panchev, C. (2000). Towards Hybrid Neural Learning Internet Agents. In: Wermter, S., Sun, R. (eds) Hybrid Neural Systems. Hybrid Neural Systems 1998. Lecture Notes in Computer Science(), vol 1778. Springer, Berlin, Heidelberg. https://doi.org/10.1007/10719871_11
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DOI: https://doi.org/10.1007/10719871_11
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