On Defining Artificial Intelligence
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References
- Adams, E. W. 1998. A Primer of Probability Logic. Stanford, California: CSLI Publications.
- Albus, J. S. 1991. Outline for a Theory of Intelligence. IEEE Transactions on Systems, Man, and Cybernetics 21(3):473–509.
- Allen, J. F. 1998. AI growing up: the changes and opportunities. AI Magazine 19(4):13–23.
- Aristotle. 1882. The Organon, or, Logical treatises of Aristotle. London: George Bell. Translated by O. F. Owen.
- Bach, J. 2009. Principles of Synthetic Intelligence PSI: An Architecture of Motivated Cognition. Oxford: Oxford University Press.
- Beattie, C.; Leibo, J. Z.; Teplyashin, D.; Ward, T.; Wainwright, M.; Küttler, H.; Lefrancq, A.; Green, S.; Valdés, V.; Sadik, A.; Schrittwieser, J.; Anderson, K.; York, S.; Cant, M.; Cain, A.; Bolton, A.; Gaffney, S.; King, H.; Hassabis, D.; Legg, S.; and Petersen, S. 2016. DeepMind Lab. CoRR abs/1612.03801.
- Bhatnagar, S.; Alexandrova, A.; Avin, S.; Cave, S.; Cheke, L.; Crosby, M.; Feyereisl, J.; Halina, M.; Loe, B. S.; hÉigeartaigh, S. O.; Martnez-Plumed, F.; Price, H.; Shevlin, H.; Weller, A.; Winfield, A.; and Hernández-Orallo, J. 2018. Mapping Intelligence: Requirements and Possibilities. In Müller, V. C., ed., Philosophy and Theory of Artificial Intelligence 2017. Berlin: Springer. 117–135.
- Birnbaum, L. 1991. Rigor mortis: a response to Nilsson’s “Logic and artificial intelligence”. Artificial Intelligence 47:57–77.
- Bostrom, N. 2014. Superintelligence: Paths, Dangers, Strategies. Oxford, UK: Oxford University Press, 1st edition.
- Brachman, R. J. 2006. (AA)AI — more than the sum of its parts, 2005 AAAI Presidential Address. AI Magazine 27(4):19–34.
- Cabrol, N. A. 2016. Alien Mindscapes – A Perspective on the Search for Extraterrestrial Intelligence. Astrobiology 16:661–676.
- Carnap, R. 1950. Logical Foundations of Probability. Chicago: The University of Chicago Press.
- Cohen, P. R. 2005. If Not Turing’s Test, then what? AI Magazine 26:61–67.
- Davis, R. 1998. What are intelligence? and why? 1996 AAAI Presidential Address. AI Magazine 19(1):91–111.
- Domingos, P. 2018. The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World. New York, NY, USA: Basic Books, Inc.
- Executive Office of the President, USA. 2016. The National Artificial Intelligence Research and Development Strategic Plan.
- Feigenbaum, E. A., and Feldman, J., eds. 1963. Computers and Thought. New York: McGraw-Hill.
- Feigenbaum, E. A., and McCorduck, P. 1983. The Fifth Generation: Artificial Intelligence and Japan’s Computer Challenge to the world. Reading, Massachusetts: Addison-Wesley Publishing Company.
- Flach, P. 2012. Machine Learning: The Art and Science of Algorithms That Make Sense of Data. New York, NY, USA: Cambridge University Press.
- Franklin, S. 2007. A foundational architecture for artificial general intelligence. In Goertzel, B., and Wang, P., eds., Advance of Artificial General Intelligence. Amsterdam: IOS Press. 36–54.
- Freeman, W. J. 1999. Consciousness, intentionality and causality. Journal of Consciousness Studies 6(11-12):143–172.
- Goertzel, B., and Pennachin, C., eds. 2007. Artificial General Intelligence. New York: Springer.
- Goertzel, B. 2009. Cognitive Synergy: A Universal Principle for Feasible General Intelligence? Dynamical Psychology.
- Goertzel, B. 2014. Artificial General Intelligence: Concept, State of the Art, and Future Prospects. Journal of Artificial General Intelligence 5(1):1–46.
- Goldman Sachs. 2016. Profiles in Innovation: Artificial Intelligence - AI, Machine Learning and Data Fuel the Future of Productivity.
- Goldstein, S.; Princiotta, D.; and Naglieri, J. 2015. Handbook of intelligence: Evolutionary theory, historical perspective, and current concepts. New York: Springer.
- Gottfredson, L. S. 1997. Mainstream science on intelligence: an editorial with 52 signatories, history, and bibliography. Intelligence 24:13–23.
- Hájek, A. 2012. Interpretations of Probability. In Zalta, E. N., ed., The Stanford Encyclopedia of Philosophy. Winter 2012 edition. URL: http://plato.stanford.edu/archives/win2012/entries/probability-interpret/, accessed in May 20, 2019.
- Hawkins, J., and Blakeslee, S. 2004. On Intelligence. New York: Times Books.
- Hayes, P., and Ford, K. 1995. Turing Test Considered Harmful. In Mellish, C. S., ed., Proceedings of the 14th International Joint Conference on Artificial Intelligence - Volume 1, 972–977.
- Hayes, P. J. 1977. In Defense of Logic. In Reddy, R., ed., Proceedings of the 5th International Joint Conference on Artificial Intelligence, 559–565.
- Hearst, M. A., and Hirsh, H. 2000. AI’s Greatest Trends and Controversies. IEEE Intelligent Systems 8–17.
- Hernández-Orallo, J. 2017. The Measure of All Minds: Evaluating Natural and Artificial Intelligence. Cambridge: Cambridge University Press.
- Hofstadter, D. R., and FARG. 1995. Fluid Concepts and Creative Analogies: Computer Models of the Fundamental Mechanisms of Thought. New York: Basic Books.
- Hofstadter, D. R. 1979. Gödel, Escher, Bach: an Eternal Golden Braid. New York: Basic Books.
- Hofstadter, D. R. 1985. Waking up from the Boolean dream, or, subcognition as computation. In Metamagical Themas: Questing for the Essence of Mind and Pattern. New York: Basic Books. chapter 26.
- Holland, J. H. 1992. Adaptation in Natural and Artificial Systems: An Introductory Analysis With Applications to Biology, Control, and Artificial Intelligence. Cambridge, Massachusetts: MIT Press.
- Hopcroft, J. E.; Motwani, R.; and Ullman, J. D. 2007. Introduction to Automata Theory, Languages, and Computation. Boston: Addison-Wesley, 3rd edition.
- Hsu, F.-h.; Campbell, M. S.; and Hoane, Jr., A. J. 1995. Deep Blue System Overview. In Proceedings of the 9th International Conference on Supercomputing, 240–244. New York, NY, USA: ACM.
- Hume, D. 1748. An Enquiry Concerning Human Understanding. London.
- Hutter, M. 2005. Universal Artificial Intelligence: Sequential Decisions based on Algorithmic Probability. Berlin: Springer.
- Kirsh, D. 1991. Foundations of AI: the big issues. Artificial Intelligence 47:3–30.
- Koene, R., and Deca, D. 2013. Editorial: Whole Brain Emulation seeks to Implement a Mind and its General Intelligence through System Identification. Journal of Artificial General Intelligence 4:1–9.
- Kowalski, R. 1979. Logic for Problem Solving. New York: North Holland.
- Kuhn, T. S. 1970. The Structure of Scientific Revolutions. Chicago University Press, 2nd edition.
- Kurzweil, R. 2006. The Singularity Is Near: When Humans Transcend Biology. New York: Penguin Books.
- Laird, J. E.; Wray, R. E.; Marinier, R. P.; and Langley, P. 2009. Claims and challenges in evaluating human-level intelligent systems. In Goertzel, B.; Hitzler, P.; and Hutter, M., eds., Proceedings of the Second Conference on Artificial General Intelligence, 91–96.
- Laird, J. E. 2012. The Soar Cognitive Architecture. Cambridge, Massachusetts: MIT Press.
- Lake, B. M.; Ullman, T. D.; Tenenbaum, J. B.; and Gershman, S. J. 2017. Building machines that learn and think like people. Behavioral and Brain Sciences 40:E253.
- LeCun, Y.; Bengio, Y.; and Hinton, G. 2015. Deep Learning. Nature 521:436–444.
- Legg, S., and Hutter, M. 2007. Universal intelligence: a definition of machine intelligence. Minds & Machines 17(4):391–444.
- Leimeister, J. M. 2010. Collective Intelligence. Business & Information Systems Engineering 2(4):245–248.
- Luger, G. F. 2008. Artificial Intelligence: Structures and Strategies for Complex Problem Solving. Boston: Pearson, 6th edition.
- Marcus, G.; Rossi, F.; and Veloso, M. M. 2016. Beyond the Turing Test. AI Magazine 37(1):3–4.
- Markram, H. 2006. The Blue Brain Project. Nature Reviews Neuroscience 7(2):153–160.
- Marr, D. 1977. Artificial intelligence: a personal view. Artificial Intelligence 9:37–48.
- Marr, D. 1982. Vision: A Computational Investigation into the Human Representation and Processing of Visual Information. San Francisco: W. H. Freeman & Co.
- McCarthy, J.; Minsky, M.; Rochester, N.; and Shannon, C. 1955. A Proposal for the Dartmouth Summer Research Project on Artificial Intelligence. URL: http://www-formal.stanford.edu/jmc/history/dartmouth.html, accessed in May 20, 2019.
- McCarthy, J. 1988. Mathematical logic in artificial intelligence. Dædalus 117(1):297–311.
- McCarthy, J. 2007. From here to human-level AI. Artificial Intelligence 171:1174–1182.
- McCorduck, P. 2004. Machines Who Think: A Personal Inquiry into the History and Prospects of Artificial Intelligence. Natick, MA: A. K. Peters, Ltd., 2nd edition.
- McCulloch, W. S., and Pitts, W. H. 1943. A logical calculus of ideas immanent in neural activity. Bulletin of Mathematical Biophysics 5:115–133.
- McDermott, D. 1987. A critique of pure reason. Computational Intelligence 3:151–160.
- McDermott, D. 2007. Level-headed. Artificial Intelligence 171:1183–1186.
- Medin, D. L., and Ross, B. H. 1992. Cognitive Psychology. Fort Worth: Harcourt Brace Jovanovich.
- Michalski, R.; Carbonell, J.; and Mitchell, T., eds. 1984. Machine Learning: An Artificial Intelligence Approach. Springer-Verlag.
- Minsky, M.; Singh, P.; and Sloman, A. 2004. The St. Thomas common sense symposium: designing architectures for human-level intelligence. AI Magazine 25(2):113–124.
- Minsky, M. 1983. Introduction to the COMTEX Microfiche Edition of the Early MIT Artificial Intelligence Memos. AI Magazine 4(1):19–22.
- Minsky, M. 1985a. The Society of Mind. New York: Simon and Schuster.
- Minsky, M. 1985b. Why intelligent aliens will be intelligible. In Regis, E., ed., Extraterrestrials: Science and Alien Intelligence. Cambridge: Cambridge University Press. 117–128.
- Minsky, M. 1990. Logical vs. analogical or symbolic vs. connectionist or neat vs. scruffy. In Winston, P. H., and Shellard, S. A., eds., Artificial Intelligence at MIT, Vol. 1: Expanding Frontiers. Cambridge, Massachusetts: MIT Press. 218–243.
- Minsky, M. 2006. The Emotion Machine: Commonsense Thinking, Artificial Intelligence, and the Future of the Human Mind. Simon & Schuster.
- Monett, D., and Lewis, C. W. P. 2018. Getting clarity by defining Artificial Intelligence - A Survey. In Müller, V. C., ed., Philosophy and Theory of Artificial Intelligence 2017. Berlin: Springer. 212–214.
- Newell, A., and Simon, H. A. 1963. GPS, a program that simulates human thought. In Feigenbaum, E. A., and Feldman, J., eds., Computers and Thought. McGraw-Hill, New York. 279–293.
- Newell, A., and Simon, H. A. 1976. Computer science as empirical inquiry: symbols and search. Communications of the ACM 19(3):113–126.
- Newell, A. 1990. Unified Theories of Cognition. Cambridge, Massachusetts: Harvard University Press.
- Nilsson, N. J. 1986. Probabilistic logic. Artificial Intelligence 28:71–87.
- Nilsson, N. J. 1991. Logic and artificial intelligence. Artificial Intelligence 47:31–56.
- Nilsson, N. J. 1998. Artificial Intelligence: A New Synthesis. San Francisco: Morgan Kaufmann.
- Nilsson, N. J. 2005. Human-level artificial intelligence? Be serious! AI Magazine 26(4):68–75.
- Nilsson, N. J. 2009. The Quest for Artificial Intelligence: A History of Ideas and Achievements. Cambridge: Cambridge University Press.
- Peirce, C. S. 1931. Collected Papers of Charles Sanders Peirce, volume 2. Cambridge, Massachusetts: Harvard University Press.
- Piaget, J. 1960. The Psychology of Intelligence. Paterson, New Jersey: Littlefield, Adams & Co.
- Piaget, J. 1963. The Origins of Intelligence in Children. New York: W.W. Norton & Company, Inc. Translated by M. Cook.
- Poole, D. L., and Mackworth, A. K. 2017. Artificial Intelligence: Foundations of Computational Agents. Cambridge: Cambridge University Press, 2 edition.
- Reeke, G. N., and Edelman, G. M. 1988. Real brains and artificial intelligence. Dædalus 117(1):143–173.
- Regis, E., ed. 1985. Extraterrestrials: Science and alien intelligence.
- Roland, A., and Shiman, P. 2002. Strategic computing : DARPA and the quest for machine intelligence, 1983-1993. Cambridge, Massachusetts: MIT Press.
- Rumelhart, D. E., and McClelland, J. L. 1986. PDP models and general issues in cognitive science. In Rumelhart, D. E., and McClelland, J. L., eds., Parallel Distributed Processing: Explorations in the Microstructure of Cognition, Vol. 1, Foundations. Cambridge, Massachusetts: MIT Press. 110–146.
- Russell, S., and Norvig, P. 2002. Artificial Intelligence: A Modern Approach. Upper Saddle River, New Jersey: Prentice Hall, 2nd edition.
- Russell, S., and Norvig, P. 2010. Artificial Intelligence: A Modern Approach. Upper Saddle River, New Jersey: Prentice Hall, 3rd edition.
- Russell, S., and Wefald, E. H. 1991. Do the Right Thing: Studies in Limited Rationality. Cambridge, Massachusetts: MIT Press.
- Russell, S. 1997. Rationality and intelligence. Artificial Intelligence 94:57–77.
- Schank, R. C. 1991. Where is the AI? AI Magazine 12(4):38–49.
- Searle, J. 1980. Minds, brains, and programs. The Behavioral and Brain Sciences 3:417–424.
- Shannon, C. E., and Weaver, W. 1949. The mathematical theory of communication. Urbana, IL: The University of Illinois Press.
- Shapiro, S. C. 1992. Artificial Intelligence. In Shapiro, S. C., ed., Encyclopedia of Artificial Intelligence. New York: John Wiley, 2 edition. 54–57.
- Silver, D.; Huang, A.; Maddison, C. J.; Guez, A.; Sifre, L.; van den Driessche, G.; Schrittwieser, J.; Antonoglou, I.; Panneershelvam, V.; Lanctot, M.; Dieleman, S.; Grewe, D.; Nham, J.; Kalchbrenner, N.; Sutskever, I.; Lillicrap, T.; Leach, M.; Kavukcuoglu, K.; Graepel, T.; and Hassabis, D. 2016. Mastering the game of Go with deep neural networks and tree search. Nature 529:484–489.
- Simon, H. A.; Langley, P. W.; and Bradshaw, G. L. 1981. Scientific Discovery as Problem Solving. Synthese 47:1–27.
- Simon, H. A. 1957. Models of Man: Social and Rational. New York: John Wiley.
- Simon, H. A. 1983. Reason in Human Affairs. Stanford, California: Stanford University Press.
- Solomonoff, R. J. 1964. A formal theory of inductive inference. Part I and II. Information and Control 7(1-2):1–22,224–254.
- Thórisson, K. R.; Bieger, J.; Li, X.; and Wang, P. 2019. Cumulative Learning. In Proceedings of the Twelfth Conference on Artificial General Intelligence. To appear.
- Thórisson, K. R. 2012. A New Constructivist AI: From Manual Methods to Self-Constructive Systems. In Wang, P., and Goertzel, B., eds., Theoretical Foundations of Artificial General Intelligence. Paris: Atlantis Press. 145–171.
- Thórisson, K. R. 2013. Reductio ad Absurdum: On Oversimplification in Computer Science and its Pernicious Effect on Artificial Intelligence Research. AGI-13 workshop on Formalizing Mechanisms for Artificial General Intelligence and Cognition, Beijing, China, July 31st. Retrieved from http://alumni.media.mit.edu/~kris/ftp/Thorisson-ReductioAdAbsurdum-AGI2013.pdf in May 20, 2019.
- Tomasello, M. 2000. Primate Cognition: Introduction to the Issue. Cognitive Science 24(3):351–361.
- Turing, A. M. 1950. Computing machinery and intelligence. Mind LIX:433–460.
- Tversky, A., and Kahneman, D. 1974. Judgment under uncertainty: heuristics and biases. Science 185:1124–1131.
- von Neumann, J. 1958. The Computer and the Brain. New Haven, CT: Yale University Press.
- Wang, P., and Goertzel, B. 2007. Introduction: Aspects of artificial general intelligence. In Goertzel, B., and Wang, P., eds., Advance of Artificial General Intelligence. Amsterdam: IOS Press. 1–16.
- Wang, P., and Hammer, P. 2015. Issues in Temporal and Causal Inference. In Bieger, J.; Goertzel, B.; and Potapov, A., eds., Proceedings of the Eighth Conference on Artificial General Intelligence, 208–217.
- Wang, P., and Li, X. 2016. Different Conceptions of Learning: Function Approximation vs. Self-Organization. In Steunebrink, B.; Wang, P.; and Goertzel, B., eds., Proceedings of the Ninth Conference on Artificial General Intelligence, 140–149.
- Wang, P.; Liu, K.; and Dougherty, Q. 2018. Conceptions of Artificial Intelligence and Singularity. Information 9(4).
- Wang, P. 1994. On the Working Definition of Intelligence. Technical Report 94, Center for Research on Concepts and Cognition, Indiana University, Bloomington, Indiana.
- Wang, P. 1995. Non-Axiomatic Reasoning System: Exploring the Essence of Intelligence. Ph.D. Dissertation, Indiana University.
- Wang, P. 1996. Heuristics and normative models of judgment under uncertainty. International Journal of Approximate Reasoning 14(4):221–235.
- Wang, P. 2001. Wason’s cards: what is wrong. In Chen, L., and Zhuo, Y., eds., Proceedings of the Third International Conference on Cognitive Science, 371–375.
- Wang, P. 2004a. The limitation of Bayesianism. Artificial Intelligence 158(1):97–106.
- Wang, P. 2004b. Problem solving with insufficient resources. International Journal of Uncertainty, Fuzziness and Knowledge-based Systems 12(5):673–700.
- Wang, P. 2004c. Toward a unified artificial intelligence. In Papers from the 2004 AAAI Fall Symposium on Achieving Human-Level Intelligence through Integrated Research and Systems, 83–90.
- Wang, P. 2005. Experience-grounded semantics: a theory for intelligent systems. Cognitive Systems Research 6(4):282–302.
- Wang, P. 2006a. Artificial Intelligence: What it is, and what it should be. In Lebiere, C., and Wray, R., eds., Papers from the AAAI Spring Symposium on Between a Rock and a Hard Place: Cognitive Science Principles Meet AI-Hard Problems, 97–102.
- Wang, P. 2006b. Rigid Flexibility: The Logic of Intelligence. Dordrecht: Springer.
- Wang, P. 2008. What do you mean by “AI”. In Wang, P.; Goertzel, B.; and Franklin, S., eds., Proceedings of the First Conference on Artificial General Intelligence, 362–373.
- Wang, P. 2009. Case-by-case problem solving. In Goertzel, B.; Hitzler, P.; and Hutter, M., eds., Proceedings of the Second Conference on Artificial General Intelligence, 180–185.
- Wang, P. 2011. The Assumptions on Knowledge and Resources in Models of Rationality. International Journal of Machine Consciousness 3(1):193–218.
- Wang, P. 2012. Theories of Artificial Intelligence – Meta-theoretical considerations. In Wang, P., and Goertzel, B., eds., Theoretical Foundations of Artificial General Intelligence. Paris: Atlantis Press. 305–323.
- Wang, P. 2013. Non-Axiomatic Logic: A Model of Intelligent Reasoning. Singapore: World Scientific.
- Wason, P. C., and Johnson-Laird, P. N. 1972. Psychology of Reasoning: Structure and Content. Cambridge, Massachusetts: Harvard University Press.
- Wiener, N. 1948. Cybernetics, or control and communication in the animal and the machine. New York: John Wiley & Sons, Inc.
DOI: https://doi.org/10.2478/jagi-2019-0002 | Journal eISSN: 1946-0163
Language: English
Page range: 1 - 37
Submitted on: Mar 29, 2019
Accepted on: Aug 12, 2019
Published on: Aug 19, 2019
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