Skip to main content

Program Search as a Path to Artificial General Intelligence

  • Chapter
Artificial General Intelligence

Part of the book series: Cognitive Technologies ((COGTECH))

Summary

It is difficult to develop an adequate mathematical definition of intelligence. Therefore we consider the general problem of searching for programs with specified properties and we argue, using the Church-Turing thesis, that it covers the informal meaning of intelligence. The program search algorithm can also be used to optimise its own structure and learn in this way. Thus, developing a practical program search algorithm is a way to create AI.

To construct a working program search algorithm we show a model of programs and logic in which specifications and proofs of program properties can be understood in a natural way. We combine it with an extensive parser and show how efficient machine code can be generated for programs in this model. In this way we construct a system which communicates in precise natural language and where programming and reasoning can be effectively automated.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 189.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 249.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 249.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Atkins BT, Fillmore CJ, FrameNet, www.icsi.berkeley.edu/-framenet/

    Google Scholar 

  2. Baader F, Nipkow T (1998) Term Rewriting and All That, Cambridge University Press.

    Google Scholar 

  3. Bederson B, Piccolo Toolkit, www.cs.umd.edu/hcil/piccolo/

    Google Scholar 

  4. Gödel K (1931) Über formal unentscheidbare Sätze der Principia Mathematica und verwandter Systeme I, Monatshefte für Mathematik und Physik 38:173–198.

    Article  Google Scholar 

  5. Grädel E (2002) Model Checking Games, Proceedings of WOLLIC 02, vol. 67 of Electronic Notes in Theoretical Computer Science, Elsevier.

    Google Scholar 

  6. Hurd J (2002) Formal Verification of Probabilistic Algorithms, PhD thesis, University of Cambridge.

    Google Scholar 

  7. Hutter M (2000) A Theory of Universal Artificial Intelligence based on Algorithmic Complexity, Technical Report cs.AI/0004001.

    Google Scholar 

  8. Hutter M (2005) Universal Algorithmic Intelligence: A Mathematical top→down Approach, this volume.

    Google Scholar 

  9. Hutter M (2004) Universal Artificial Intelligence: Sequential Decisions based on Algorithmic Probability, Springer, Berlin.

    Google Scholar 

  10. Kaiser Ł (2003) Speagram, www.speagram.org.

    Google Scholar 

  11. Kirkegaard C (2001) Borel-A Bounded Resource Language, Project Report, University of Edinburgh.

    Google Scholar 

  12. Kolmogorov AN (1965) Three Approaches to the Quantitative Definition of Information, Problems of Information Transmission 1:1–11.

    Google Scholar 

  13. Levin LA (1973) Universal Sequential Search Problems, Problems of Information Transmission 9(3):265–266.

    Google Scholar 

  14. Li M, Vitanyi PMB (1997) An Introduction to Kolmogorov Complexity and Its Applications, Springer, Berlin.

    MATH  Google Scholar 

  15. Presburger M (1929) Über die Vollständigkeit eines gewissen Systems der Arithmetic ganzer Zahlen, in welchem die Addition als einzige Operation hervortritt, Compte-Rendus dei Congres des Math. des pays slavs.

    Google Scholar 

  16. Raskin J (2000) The Humane Interface: New Directions for Designing Interactive Systems, Addison-Wesley Professional.

    Google Scholar 

  17. Robinson A, Voronkov A, Robinson J (2001) Handbook of Automated Reasoning, newblock MIT Press, Cambridge, MA.

    MATH  Google Scholar 

  18. Schmidhuber J (2002) Optimal Ordered Problem Solver, Technical Report IDSIA-12-02.

    Google Scholar 

  19. Schmidhuber J (2005) Gödel Machines: Fully Self-Referential Optimal Universal Self-Improvers, this volume.

    Google Scholar 

  20. Schmidhuber J (2005) The New AI: General & Sound & Relevant for Physics, this volume.

    Google Scholar 

  21. Solomonoff R (1989) A System for Incremental Learning Based on Algorithmic Probability, Proceedings of the Sixth Israeli Conference on Artificial Intelligence, Computer Vision and Pattern Recognition.

    Google Scholar 

  22. Tarski A (1948) A Decision Method for Elementary Algebra and Geometry, prepared for publication by JCC Mc Kinsey, U.S. Air Force Project RAND, R-109, the RAND Corporation.

    Google Scholar 

  23. Wadler WL (1984) Listlessness is Better than Laziness, PhD thesis, Carnegie-Mellon University.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2007 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Kaiser, L. (2007). Program Search as a Path to Artificial General Intelligence. In: Goertzel, B., Pennachin, C. (eds) Artificial General Intelligence. Cognitive Technologies. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-68677-4_9

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-68677-4_9

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-23733-4

  • Online ISBN: 978-3-540-68677-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics