The background of the presentation is a perspective on the development of wireless technology from 2000 to 2010. The foreground of the presentation is a contrasted understanding of intelligence in "natural" wireless (human communication) versus "artificial" wireless (communication between devices). Invited talk, presented at The Twelfth Annual Conference on Innovative Applications of Artificial Intelligence (IAAI-2000), Austin, TX, August, 2000.

First appeared asRussell, S. J., Subramanian, D., and Parr, R. , "Provably bounded optimal agents", IJCAI-93, pp. 338-€“345. Journal of Artificial Intelligence Research, 1 (1995), pp.1-36.

This book (originally published in 1994 by Ellis Horwood) is now out of print. The copyright now resides with the editors who have decided to make the material freely available on the web.This book is based on the EC (ESPRIT) project StatLog which compare and evaluated a range of classification techniques, with an assessment of their merits, disadvantages and range of application. This integrated volume provides a concise introduction to each method, and reviews comparative trials in large-scale commercial and industrial problems. It makes accessible to a wide range of workers the complex issue of classification as approached through machine learning, statistics and neural networks, encouraging a cross-fertilization between these discplines.

Phil. Trans. R. Soc. Lond. A. 1994 349 1689. Intelligence is a complex, natural phenomenon exhibited by humans and many other living things, without sharply defined boundaries between intelligent and unintelligent behaviour. Artificial inteliigence focuses on the phenomenon of intelligent behaviour, in humans or machines. Experimentation with computer programs allows us to manipulate their design and intervene in the environmental conditions in ways that are not possible with humans. Thus, experimentation can help us to understand what principles govern intelligent action and what mechanisms are sufficient for computers to replicate intelligent behaviours.

Computational Linguistics, 19 (4)- 571-90.

Computational Linguistics, 19 (2), 313-30.

Machine Learning: An Artificial Intelligence Approach contains tutorial overviews and research papers representative of trends in the area of machine learning as viewed from an artificial intelligence perspective. The book is organized into six parts. Part I provides an overview of machine learning and explains why machines should learn. Part II covers important issues affecting the design of learning programs-particularly programs that learn from examples. It also describes inductive learning systems.

Kluwer. See also: Symbolic Model Checking: An Approach to the State Explosion Problem. Doctoral thesis, Carnegie Mellon University, 1992 (http://www.kenmcmil.com/pubs/thesis.pdf). J.R. Burch, E.M. Clarke, K.L. McMillan, D.L. Dill, L.J. Hwang, Symbolic model checking: 1020 States and beyond, Information and Computation, Volume 98, Issue 2, June 1992, Pages 142-170 (http://www.sciencedirect.com/science/article/pii/089054019290017A). Burch, J. R.; Clarke, E.M.; McMillan, K. L.; Dill, D.L., Sequential circuit verification using symbolic model checking, Design Automation Conference, 1990. Proceedings, 27th ACM/IEEE, vol., no., pp.46,51, 24-28 Jun 1990. (https://ieeexplore.ieee.org/document/114827) Burch, J.R.; Clarke, E.M.; Long, D.E.; McMillan, K.L.; Dill, D.L., Symbolic model checking for sequential circuit verification, Computer-Aided Design of Integrated Circuits and Systems, IEEE Transactions on, vol.13, no.4, pp.401,424, Apr 1994 (https://ieeexplore.ieee.org/document/275352). E. M. Clarke, O. Grumberg, K. L. McMillan, and X. Zhao. 1995. Efficient generation of counterexamples and witnesses in symbolic model checking. In Proceedings of the 32nd annual ACM/IEEE Design Automation Conference (DAC '95). ACM, New York, NY, USA, 427-432 (http://dl.acm.org/citation.cfm?id=217565). Burch, Jerry R.; Clarke, Edmund M.; Long, David E.; McMillan, Kenneth L.; and Dill, David L., Symbolic Model Checking for Sequential Circuit Verification. IEEE Transactions On Computer-Aided Design of Integrated Circuits and Systems, Vol. 13, No. 4, pp. 401-424, April 1994 (http://www.cs.cmu.edu/~emc/papers/Conference%20Papers/Sequential%20circuit%20verification%20using%20symbolic%20model%20checking.pdf).

In Graves, S. C., Zipkin, P. H., and Kan, A. H. G. R. (Eds.), Logistics of Production and Inventory: Handbooks in Operations Research and Management Science, Volume 4, pp. 445–522. North-Holland.

In VISION-21 Symposium. NASA Lewis Research Center and the Ohio Aerospace Institute.