An artificial immune system for data analysis

Timmis J and Neal M J. An artificial immune system for data analysis. In Proceedings of 3rd international workshop on information processing in cells and tissues (IPCAT), Indianapolis, U.S.A., 1999.

A resource limited artificial immune system for data analysis.

Timmis J and Neal M J. A resource limited artificial immune system for data analysis. In Proceedings of ES2000 - Research and Development of Intelligent Systems, pages 19-32, Cambrige, U.K., 2000. Springer.

Investigating the evolution and stability of a resource limited artificial immune system.

Timmis J and Neal M J. Investigating the evolution and stability of a resource limited artificial immune system. In Proceedings of GECCO - special workshop on artificial immune systems, pages 40-41. AAAI press, 2000.

Qualitative Modelling of Linear Networks in Engineering Applications

Lee M.H., Qualitative Modelling of Linear Networks in Engineering Applications, in Proc. ECAI?2000, 14th European Conf. on Artificial Intelligence, Berlin, August 19th - 25th 2000, pp161-5.

Growth of Motor Coordination in Early Robot Learning

M. H. Lee and Q. Meng, Growth of Motor Coordination in Early Robot Learning, IJCAI-05, 2005.

Qualitative Circuit Models in Failure Analysis Reasoning

Lee M.H., Qualitative Circuit Models in Failure Analysis Reasoning, AI Journal. vol 111, pp239-276.1999.

Automating Mechanical FMEA Using Functional Models

Hughes, N., Chou E., Price, C. J. Lee M. H.(1999). Automating Mechanical FMEA Using Functional Models, Proceedings 12th Int. Florida AI Research Soc. Conf. (FLAIRS-99), AAAI Press, May 1999, pp. 394-398.

The Epistemological Foundations of Artificial Agents

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

The Implications of Philosophical Foundations for Knowledge Representation and Learning in Agents

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.

The Implications of Philosophical Foundations for Knowledge Representation and Learning in Agents

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.

Intelligent software for laboratory automation

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

Learning Qualitative Metabolic Models

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

Functional genomic hypothesis generation and experimentation by a robot scientist

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

Can Object (Instance) Diagrams Help First Year Students Understand Program Behaviour?

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.

Endocrine Inspired Modulation of Artificial Neural Networks for Mobile Robotics

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.