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Computing as compression: SP20

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Abstract

SP20 is a software simulation of a ‘new generation’ computing system, based on the conjecture that computing may usefully be understood as information compression by pattern matching, unification and metrics-guided search.19) The design of the system aims to exploit the potential of these processes as fully as possible to integrate and rationalise diverse kinds of computing and to achieve more flexibility and ‘intelligence’ than conventional computers.

The organisation of SP20 is described, highlighting advances compared with previous models. The main advances are: a much more efficient search method which is scaleable to large data sets; an improved ability to find alternative answers to problems; and an ability to find patterns which are discontinuous or fragmented as well as coherent patterns of contiguous symbols.

Analytic and empirical evidence is presented confirming the computational properties of the model. In a serial processing environment, its time complexity is O(N 2), whereN is the number of symbols processed. In a high-parallel environment, the time complexity of this model should approach O(N). The space complexity of the model in serial or parallel environments appears to be O(N).

The capabilities and shortcomings of SP20 are described in different areas of computing: best-match information retrieval, pattern recognition and de-referencing of identifiers; unsupervised inductive learning of grammars, object-oriented structures and rules; execution of functions; deductive and probabilistic inference; parsing; planning and problem solving. A selection of examples are presented, highlighting the new capabilities of the model.

Weaknesses in the design of the model are summarised and planned future developments are described in outline.

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References

  1. Ashford, J. and Willett, P.,Text Retrieval and Document Databases, Bromley: Chartwell-Bratt, 1988.

    Google Scholar 

  2. Birtwistle, G. M., Dahl, O-J., Myhrhaug, B., and Nygaard, K.,Simula Begin, New York: Van Nostrand Reinhold, 1973.

    Google Scholar 

  3. Carroll, D. M., Pogue, C. A., and Willett, P., “Bibliographic Pattern Matching Using the ICL Distributed Array Processor,”Journal of the American Society for Information Science, 39, 6, pp. 390–399, 1988.

    Article  Google Scholar 

  4. Chan, S. C., Wong, A. K. C., and Chiu, D. K. Y., “A Survey of Multiple Sequence Comparison Methods,”Bulletin of Mathematical Biology, 54, 4, pp. 563–598, 1992.

    MATH  Google Scholar 

  5. Chomsky, N.,Syntactic Structures, The Hague: Mouton, 1957.

    Google Scholar 

  6. Gazdar, G. and Mellish, C.,Natural Language Processing in Prolog, Wokingham: Addison-Wesley, 1989.

    Google Scholar 

  7. Lee, D. L., “ALTEP—A Cellular Processor for High-Speed Pattern Matching,”New Generation Computing, 4, pp. 225–244, 1986.

    Article  MATH  Google Scholar 

  8. Lieberherr, K. J., Bergstein, P., and Silva-Lepe, I., “From Objects to Classes: Algorithms for Optimal Object-Oriented Design,”Software Engineering Journal, 6, 4, pp. 205–228, 1991.

    Google Scholar 

  9. Mak, V. W-K., Lee, K. C., and Frieder, O., “Exploiting Parallelism in Pattern Matching: An Information Retrieval Application,”ACM Transactions on Information Systems, 9, 1, pp. 52–74, 1991.

    Article  Google Scholar 

  10. Pereira, F. C. N. and Warren, D. H. D., “Definite Clause Grammars for Language Analysis—A Survey of the Formalism and a Comparison with Augmented Transition Networks,”Artificial Intelligence, 13, pp. 231–278, 1980.

    Article  MATH  MathSciNet  Google Scholar 

  11. Piatetsky-Shapiro, G. and Frawley, W. J. (eds.),Knowledge Discovery in Databases, Cambridge, Mass.: MIT Press, 1991.

    Google Scholar 

  12. Winston, P. H.,Artificial Intelligence, third edition, Reading, Mass.: Addison-Wesley, 1992.

    Google Scholar 

  13. Wolff, J. G., “An Algorithm for the Segmentation of an Artificial Language Analogue,”British Journal of Psychology, 66, pp. 79–90, 1975.

    Google Scholar 

  14. Wolff, J. G., “Simplicity and Power: Some Unifying Ideas in Computing,”Computer Journal, 33, 6, pp. 518–534, 1990. (reproduced in Ref. 15), Chapter 4).

    Article  MathSciNet  Google Scholar 

  15. Wolff, J. G.,Towards a Theory of Cognition and Computing, Chichester: Ellis Horwood, 1991.

    Google Scholar 

  16. Wolff, J. G., “On the Integration of Learning, Logical Deduction and Probabilistic Inductive Inference,”Proceedings of the First International Workshop on Inductive Logic Programming, Viana de Castelo, Portugal, pp. 177–191, March 1991.

  17. Wolff, J. G., “A Scaleable Technique for Best-Match Retrieval of Sequential Information Using Metrics-Guided Search,”Journal of Information Science, 20, 1, pp. 16–28, 1994.

    Article  Google Scholar 

  18. Wolff, J. G., “Towards a New Concept of Software,”Software Engineering Journal, 9, 1, pp. 27–38, 1994.

    Article  Google Scholar 

  19. Wolff, J. G., “Computing as Compression: Overview of the SP Theory and System,”New Generation Computing, this volume.

  20. Wolff, J. G., “Computing and Information Compression: A Reply,” to appear inAI Communications, 7, 3/4, pp. 203–219, 1994.

  21. Wolff, J. G., “An Alternative Scaleable Technique for Best-Match Retrieval of Sequential Information Using Metrics-Guided Search,” in preparation.

  22. Wolff, J. G. and Chipperfield, A. J., “Unifying Computing: Inductive Learning and Logic,” inResearch and Development in Expert Systems VII (T. R. Addis and R. M. Muir, eds.) (Proceedings of Expert Systems ’90, Reading, England, September 1990), pp. 263–276, 1990.

  23. Zhang, Y. and Orlowska, M. E., “An Improvement on the Automatic Tool for Relational Database Design,”Information Systems, 15, 6, pp. 647–651, 1990.

    Article  Google Scholar 

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Authors and Affiliations

  1. School of Electronic Engineering and Computer Systems, University of Wales, Dean Street, LL57 1UT, Bangor, Gwynedd, UK

    J. Gerard Wolff

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  1. J. Gerard Wolff
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Dr. Gerry Wolff: He is a former lecturer in psychology and is currently a lecturer in computer systems engineering in the School of Electronic Engineering and Computer Systems, University of Wales, Bangor. He has industrial experience in software engineering and as an IBM Fellow. He initiated and directed the PAL project which (with Professor Newell’s CHAT system) won the British Computer Society’s Social Benefit Award for 1988. He has extensive publications in computing, cognitive science and artificial intelligence, including a book on cognition and computing. He is a Chartered Engineer and a Member of the British Computer Society.

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Wolff, J.G. Computing as compression: SP20. New Gener Comput 13, 215–241 (1995). https://doi.org/10.1007/BF03038314

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  • DOI: https://doi.org/10.1007/BF03038314

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