33 resultados para COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE


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Lacey, N. J., Lee, M. H. (2003). The Epistemological Foundations of Artificial Agents. Minds and Machines, 13, 339-365.

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N.J. Lacey and M.H. Lee, ?The Implications of Philosophical Foundations for Knowledge Representation and Learning in Agents?, Springer-Verlag Lecture Notes on Artificial Intelligence, Vol 2636 on Adaptive Agents and Multi-Agent Systems, 2002.

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Lacey N and Lee M.H., The Implications of Philosophical Foundations for Knowledge Representation and Learning in Agents, in Proc. AISB?01 Symposium on Adaptive Agents and Multi-agent Systems, York, March 2001, pp13-24.

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Whelan, K. E. and King, R. D. (2004) Intelligent software for laboratory automation. Trends in Biotechnology 22 (9): 440-445

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Coghill, G. M., Garrett, S. M. and King, R. D. (2004) Learning Qualitative Metabolic Models. European Conference on Artificial Intelligence (ECAI'04)

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King R. D., Whelan, K. E., Jones, F. M., Reiser, P. G. K., Bryant, C. H., Muggleton, S., Kell, D. B. and Oliver, S. G. (2004) Functional genomic hypothesis generation and experimentation by a robot scientist. Nature 427 (6971) p247-252

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Thomas, L.A., Ratcliffe, M.B. and Thomasson, B. J., Can Object (Instance) Diagrams Help First Year Students Understand Program Behaviour? in Diagrammatic Representation and Inference, Diagrams 2004, editors A. Blackwell, K. Marriot and Atushi Shimojima, Springer Lecture Notes on Artificial Intelligence, 2980.

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Sauze, C and Neal, M. 'Endocrine Inspired Modulation of Artificial Neural Networks for Mobile Robotics', Dynamics of Learning Behavior and Neuromodulation Workshop, European Conference on Artifical Life 2007, Lisbon, Portugal, September 10th-14th 2007.

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Schierz, A. (2007). Monitoring knowledge: a text-based approach. Terminology, 13 (2), 125-154. Sponsorship: EPSRC DTG Project IQ, EU IST-FET FP6-516169

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R. Daly, Q. Shen and S. Aitken. Speeding up the learning of equivalence classes of Bayesian network structures. Proceedings of the 10th International Conference on Artificial Intelligence and Soft Computing, pages 34-39.

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J. Keppens, Q. Shen and B. Schafer. Probabilistic abductive computation of evidence collection strategies in crime investigation. Proceedings of the 10th International Conference on Artificial Intelligence and Law, pages 215-225.

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J. Keppens and Q. Shen. Compositional model repositories via dynamic constraint satisfaction with order-of-magnitude preferences. Journal of Artificial Intelligence Research, 21:499-550, 2004.

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IEEE Transactions on Knowledge and Data Engineering, vol. 15, no. 5, pp. 1338-1343, 2003.

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Huelse, M., Wischmann, S., Manoonpong, P., Twickel, A.v., Pasemann, F.: Dynamical Systems in the Sensorimotor Loop: On the Interrelation Between Internal and External Mechanisms of Evolved Robot Behavior. In: M. Lungarella, F. Iida, J. Bongard, R. Pfeifer (Eds.) 50 Years of Artificial Intelligence, LNCS 4850, Springer, 186 - 195, 2007.

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Y. Zhu, S. Williams and R. Zwiggelaar, 'Computer technology in detection and staging of prostate carcinoma: a review', Medical Image Analysis 10 (2), 178-199 (2006)