Have a personal or library account? Click to login
Towards General Evaluation of Intelligent Systems: Lessons Learned from Reproducing AIQ Test Results Cover

Towards General Evaluation of Intelligent Systems: Lessons Learned from Reproducing AIQ Test Results

Open Access
|Mar 2018

References

  1. Besold, T.; Hernández-Orallo, J.; and Schmid, U. 2015. Can Machine Intelligence be Measured in the Same Way as Human intelligence? KI - Künstliche Intelligenz 29(3):291-297.10.1007/s13218-015-0361-4
  2. Breiman, L.; Friedman, J. H.; Olsen, R. A.; and Stone, C. J. 1984. Classification and Regression Trees. Belmont: Thomson Wadsworth.
  3. Bringsjord, S., and Schimanski, B. 2003. What Is Artificial Intelligence? Psychometric AI as an Answer. In Proceedings of the 18th International Joint Conference on Artificial Intelligence (IJCAI’03), 887-893.
  4. de Mey, M. 1992. The Cognitive Paradigm. Chicago and London: University of Chicago Press.
  5. Dennett, D. C. 1991. Consciousness Explained. London: Penguin Books.
  6. Descartes, R. 1637. A Discourse on Method. Oxford: Oxford University Press.
  7. Dowe, D. L., and Hájek, A. R. 1998. A Non-Behavioural, Computational Extension to the Turing Test. In Proceedings of International Conference on Computational Intelligence & Multimedia Applications (ICCIMA’98), Gippsland, Australia, 101-106.
  8. Goertzel, B. 2010. Toward a Formal Characterization of Real-World General Intelligence. In Baum, E.; Hutter, M.; and Kitzelmann, E., eds., Proceedings of the 3rd Conference on Artificial General Intelligence, AGI 2010, 19-24. Amsterdam-Beijing-Paris: Atlantis Press.10.2991/agi.2010.17
  9. Goertzel, B. 2014. Artificial General Intelligence: Concept, State of the Art, and Future Prospects. Journal of Artificial General Intelligence 5(1):1-48.10.2478/jagi-2014-0001
  10. Harnad, S. 1991. Other Bodies, Other Minds: A Machine Incarnation of an Old Philosophical Problem. Minds and Machines 1(1):43-54.10.1007/BF00360578
  11. Hernández-Orallo, J., and Dowe, D. L. 2010. Measuring Universal Intelligence: Towards an Anytime Intelligence Test. Artificial Intelligence 174(18):1508-1539.10.1016/j.artint.2010.09.006
  12. Hernandez-Orallo, J. 2000. Beyond the Turing Test. Journal of Logic, Language and Information 9(4):447-466.10.1023/A:1008367325700
  13. Hernández-Orallo, J. 2010. A (hopefully) Unbiased Universal Environment Class for Measuring Intelligence of Biological and Artificial Systems. In Baum, E.; Hutter, M.; and Kitzelmann, E., eds., Proceedings of the 3rd Conference on Artificial General Intelligence, AGI 2010, 182-183. Amsterdam-Beijing-Paris: Atlantis Press.10.2991/agi.2010.18
  14. Hernández-Orallo, J. 2015. C-Tests Revisited: Back and Forth with Complexity. In Bieger, J.; Goertzel, B.; and Potapov, A., eds., Proceedings of the 8th Conference on Artificial General Intelligence, AGI 2015, volume 9205 of Lecture notes in artificial intelligence, 272-282. Berlin: Springer.10.1007/978-3-319-21365-1_28
  15. Hernández-Orallo, J. 2017. The Measure of All Minds. Cambridge: Cambridge University Press.10.1017/9781316594179
  16. Hibbard, B. 2009. Bias and No Free Lunch in Formal Measures of Intelligence. Journal of Artificial General Intelligence 1(1):54-61.10.2478/v10229-011-0004-6
  17. Hothorn, T.; Hornik, K.; and Zeileis, A. 2006. Unbiased Recursive Partitioning: A Conditional Inference Framework. Journal of Computational and Graphical Statistics 3(15):651-674.10.1198/106186006X133933
  18. Hutter, M., and Legg, S. 2007. Temporal Difference Updating without a Learning Rate. In Platt, J. C.; Koller, D.; Singer, Y.; and Roweis, S. T., eds., Advances in Neural Information Processing Systems 20, 705-712. Curran Associates, Inc.
  19. Insa-Cabrera, J.; Dowe, D. L.; Espa˜na-Cubillo, S.; Hernández-Lloreda, M. V.; and Hernández-Orallo, J. 2011. Comparing Humans and AI Agents. In Schmidhuber, J.; Th´orisson, K. R.; and Looks, M., eds., Proceedings of the 4th Conference on Artificial General Intelligence, AGI 2011, volume 6830 of Lecture notes in artificial intelligence, 122-132. Berlin: Springer.10.1007/978-3-642-22887-2_13
  20. Legg, S., and Hutter, M. 2007a. A Collection of Definitions of Intelligence. In Goertzel, B., and Wang, P., eds., Advances in Artificial General Intelligence: Concepts, Architectures and Algorithms, volume 157 of Frontiers in Artificial Intelligence and Applications. Amsterdam: IOS Press. 17-24.
  21. Legg, S., and Hutter, M. 2007b. Universal Intelligence: A Definition of Machine Intelligence. Minds and Machines 17(4):391-444.10.1007/s11023-007-9079-x
  22. Legg, S., and Veness, J. 2011. AIQ: Algorithmic Intelligence Quotient [source codes]. https: //github.com/mathemajician/AIQ. Accessed: 2017-06-26.
  23. Legg, S., and Veness, J. 2013. An Approximation of the Universal Intelligence Measure. In Dowe, D. L., ed., Algorithmic Probability and Friends. Bayesian Prediction and Artificial Intelligence, volume 7070 of Lecture Notes in Computer Science. Berlin: Springer. 236-249.10.1007/978-3-642-44958-1_18
  24. Müller, U. 1993. dev/lang/brainfuck-2.lha in Aminet. http://aminet.net/package.php?package=dev/lang/brainfuck-2.lha. Accessed: 2017-06-26.
  25. Schweizer, P. 2012. The Externalist Foundations of a Truly Total Turing Test. Minds and Machines 22(3):191-212.10.1007/s11023-012-9272-4
  26. Searle, J. R. 1980. Minds, Brains, and Programs. Behavioral and Brain Sciences 3(3):417-457.10.1017/S0140525X00005756
  27. Sun, R. 2007. The Importance of Cognitive Architectures: An Analysis Based on CLARION. Journal of Experimental & Theoretical Artificial Intelligence 19(2):159-193.10.1080/09528130701191560
  28. Turing, A. M. 1950. Computing Machinery and Intelligence. Mind 59(236):433-460.10.1093/mind/LIX.236.433
  29. Vadinský, O. 2015. Towards an Artificially Intelligent System: Possibilities of General Evaluation of Hybrid Paradigm. In Besold, T. R.; Lamb, L. C.; Icard, T.; and Miikkulainen, R., eds., Proceedings of the 10th International Workshop on Neural-Symbolic Learning and Reasoning NeSy’15, 23-29. Buenos Aires: IJCAI.
  30. Veness, J.; Ng, K. S.; Hutter, M.; Uther, W.; and Silver, D. 2011. A Monte Carlo AIXI Approximation. Journal of Artificial Intelligence Research 40(1):95-142.10.1613/jair.3125
  31. Watkins, C. 1989. Learning from Delayed Rewards. Ph.D. Dissertation, Kings College, Cambridge, England.
Language: English
Page range: 1 - 54
Submitted on: Feb 17, 2017
Accepted on: Feb 6, 2018
Published on: Mar 7, 2018
Published by: Artificial General Intelligence Society
In partnership with: Paradigm Publishing Services
Publication frequency: 2 issues per year

© 2018 Ondřej Vadinský, published by Artificial General Intelligence Society
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.