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2 - Philosophical foundations

Published online by Cambridge University Press:  05 July 2014

Konstantine Arkoudas
Affiliation:
Applied Communication Sciences
Selmer Bringsjord
Affiliation:
Rensselaer Polytechnic Institute
Keith Frankish
Affiliation:
The Open University, Milton Keynes
William M. Ramsey
Affiliation:
University of Nevada, Las Vegas
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Summary

Introduction

Much work in artificial intelligence has built on concepts and theories developed by philosophers and logicians. This chapter introduces this foundational work, surveying different conceptions of AI, the philosophical dream of mechanizing human reasoning, the conceptual roots of AI, and the major theories of mind that have underpinned different strands of AI research.

What is AI?

That is itself a deep philosophical question, and attempts to systematically answer it fall within the foundations of AI as a rich topic for analysis and debate. Nonetheless, a provisional answer can be given: AI is the field devoted to building artifacts capable of displaying, in controlled, well-understood environments, and over sustained periods of time, behaviors that we consider to be intelligent, or more generally, behaviors that we take to be at the heart of what it is to have a mind. Of course this answer gives rise to further questions, most notably, what exactly constitutes intelligent behavior, what it is to have a mind, and how humans actually manage to behave intelligently. The last question is empirical; it is for psychology and cognitive science to answer. It is particularly pertinent, however, because any insight into human thought might help us to build machines that work similarly. Indeed, as will emerge in this article, AI and cognitive science have developed along parallel and tightly interwoven paths; their stories cannot be told separately. The second question, the one that asks what is the mark of the mental, is philosophical. AI has lent significant urgency to it, and conversely, we will see that careful philosophical contemplation of this question has influenced the course of AI itself. Finally, the first challenge, that of specifying precisely what is to count as intelligent behavior, has traditionally been met by proposing particular behavioral tests whose successful passing would signify the presence of intelligence.

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Publisher: Cambridge University Press
Print publication year: 2014

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References

Baars, B. J. (1986). The Cognitive Revolution in Psychology. New York: Guilford Press.Google Scholar
Bacon, F. (2002). The New Organon. Cambridge University Press.Google Scholar
Barnes, B. and Bloor, D. (1982). Relativism, rationalism, and the sociology of science, in Hollis, M. and Lukes, S. (eds.), Rationality and Relativism (pp. 21–47). Oxford: Blackwell.Google Scholar
Block, N. (1978). Troubles with functionalism, in Savage, C. W. (ed.), Perception and Cognition: Issues in the Foundations of Psychology (pp. 261–325). Minneapolis, MN: University of Minnesota Press.Google Scholar
Block, N. (1981). Psychologism and behaviorism, Philosophical Review 90: 5–43.CrossRefGoogle Scholar
Bringsjord, S. (1992). What Robots Can and Can’t Be. Dordrecht: Kluwer.CrossRefGoogle Scholar
Bringsjord, S. (1995), Could, how could we tell if, and why should–androids have inner lives? in Ford, K., Glymour, C., and Hayes, P. (eds.), Android Epistemology (pp. 93–122). Cambridge, MA: MIT Press.Google Scholar
Bringsjord, S. and Zenzen, M. (1997). Cognition is not computation: The argument from irreversibility, Synthese 113: 285–320.CrossRefGoogle Scholar
Brooks, R. A. (1991). Intelligence without reason, Technical report 1293, MIT Artificial Intelligence Laboratory.
Chomsky, N. (1996). A review of B. F. Skinner’s “Verbal Behavior,” in Geirsson, H. and Losonsky, M. (eds.), Readings in Language and Mind (pp. 413–41). Oxford: Blackwell.Google Scholar
Church, A. (1936). An unsolvable problem of elementary number theory, American Journal of Mathematics 58: 345–63.CrossRefGoogle Scholar
Cole, D. (2009). The Chinese Room argument, in E. N. Zalta (ed.), The Stanford Encyclopedia of Philosophy (Winter 2009 Edition), .
Davis, E. and Morgenstern, L. (2004). Progress in formal commonsense reasoning, Introduction to the special issue on formalization of common sense, Artificial Intelligence Journal 153: 1–12.CrossRefGoogle Scholar
Davis, M. (2001). The early history of automated deduction, in Robinson, A. and Voronkov, A. (eds.), Handbook of Automated Reasoning, vol. I (pp. 3–15). Amsterdam: Elsevier.CrossRefGoogle Scholar
Davis, M. and Putnam, H. (1960). A computing procedure for quantification theory, Journal of the Association for Computing Machinery 7: 201–15.CrossRefGoogle Scholar
Descartes, R. (1911). The Philosophical Works of Descartes, vol. I (trans.Haldane, E. S. and Ross, G. R. T.). Cambridge University Press.Google Scholar
Descartes, R. (1988). Descartes: Selected Philosophical Writings, Cambridge University Press.CrossRefGoogle Scholar
Dreyfus, H. L. (1972). What Computers Can’t Do: A Critique of Artificial Reason. New York: Harper & Row.Google Scholar
Dreyfus, H. L. (1992). What Computers Still Can’t Do: A Critique of Artificial Reason. Cambridge, MA: MIT Press.Google Scholar
Fodor, J. A. (1978). Tom Swift and his procedural grandmother, Cognition 6: 229–47.CrossRefGoogle Scholar
Fodor, J. A. (1987). Psychosemantics: The Problem of Meaning in the Philosophy of Mind. Cambridge, MA: MIT Press.Google Scholar
Fodor, J. A. and Pylyshyn, Z. W. (1988). Connectionism and cognitive architecture: A critical analysis, Cognition 28: 139–96.CrossRefGoogle ScholarPubMed
Gardner, H. (1985). The Mind’s New Science: A History of the Cognitive Revolution. New York: Basic Books.Google Scholar
Gould, S. J. and Lewontin, R. (1979). The spandrels of San Marco and the Panglossian paradigm: A critique of the adaptationist paradigm, Proceedings of the Royal Society of London B 205: 581–98.CrossRefGoogle Scholar
Hanson, N. R. (1958). Patterns of Discovery. Cambridge University Press.Google Scholar
Harnad, S. (1990). The symbol grounding problem, Physica D 42: 335–46.CrossRefGoogle Scholar
Harnad, S. (1991). Other bodies, other minds: A machine incarnation of an old philosophical problem, Minds and Machines 1: 43–54.Google Scholar
Haugeland, J. (1985). Artificial Intelligence: The Very Idea. Cambridge, MA: MIT Press.Google Scholar
Hempel, C. G. (1985). Thoughts on the limitations of discovery by computer, in Schaffner, K. (ed.), Logic of Discovery and Diagnosis in Medicine (pp. 115–22). Berkeley, CA: University of California Press.Google Scholar
Johnson-Laird, P. N. (1977). Procedural semantics, Cognition 5: 189–214.CrossRefGoogle Scholar
Kirsh, D. (1991). Today the earwig, tomorrow man?, Artificial Intelligence 47: 161–84.CrossRefGoogle Scholar
Koza, J. R. (1992). Genetic Programming: On the Programming of Computers by Means of Natural Selection. Cambridge, MA: MIT Press.Google Scholar
Lakatos, I. (1976). Proofs and Refutations: The Logic of Mathematical Discovery. Cambridge University Press.CrossRefGoogle Scholar
Langley, P. W., Simon, H. A., Bradshaw, G. L., and Zytkow, J. M. (1987). Scientific Discovery: Computational Explorations of the Creative Process. Cambridge, MA: MIT Press.Google Scholar
Lenat, D. B. (1976). AM: An Artificial Intelligence Approach to Discovery in Mathematics as Heuristic Search. Ph.D. thesis, Stanford University.Google Scholar
Langley, P. W., Simon, H. A., Bradshaw, G. L., and Zytkow, J. M. (1983). EURISKO: A Program that learns new heuristics and domain concepts: The nature of Heuristics III: Program design and results, Artificial Intelligence 21: 61–98.Google Scholar
McCarthy, J. (1962). Computer programs for checking mathematical proofs, in Proceedings of the Symposium in Pure Math, Recursive Function Theory (pp. 219–28). Providence, RI: American Mathematical Society.CrossRefGoogle Scholar
McCulloch, W. S. and Pitts, W. A. (1943). A logical calculus of the ideas immanent in nervous activity, Bulletin of Mathematical Biophysics 5: 115–33.CrossRefGoogle Scholar
Mill, J. S. (1874). System of Logic. New York: Harper and Brothers.Google Scholar
Minsky, M. (1986). The Society of Mind. New York: Simon and Schuster.Google Scholar
Moravec, H. (1999). Robot: Mere Machine to Transcendent Mind. Oxford University Press.Google Scholar
Muggleton, S. (1992). Inductive logic programming, in Muggleton, S. (ed.), Inductive Logic Programming (pp. 3–27), London: Academic Press.Google Scholar
Newell, A. and Simon, H. A. (1976). Computer science as empirical inquiry: Symbols and search, Communications of the Association for Computing Machinery 19: 113–26.CrossRefGoogle Scholar
Peirce, C. S. (1960). Collected Papers of C. S. Peirce. Cambridge, MA: Harvard University Press.Google Scholar
Pólya, G. (1945). How to Solve It: A New Aspect of Mathematical Method. Princeton University Press.Google Scholar
Port, R. F. and van Gelder, T. (1995). Mind as Motion: Explorations in the Dynamics of Cognition. Cambridge, MA: MIT Press.Google Scholar
Putnam, H. (1960). Minds and machines, in Hook, S. (ed.), Dimensions of Mind (pp. 138–64). New YorkUniversity Press.Google Scholar
Pylyshyn, Z. (1989). Computing in cognitive science, in Posner, M. I. (ed.), Foundations of Cognitive Science (pp. 49–92). Cambridge, MA: MIT Press.Google Scholar
Pylyshyn, Z. (1991). Rules and Representations: Chomsky and representational realism, in Kasher, A. (ed.), The Chomskyan Turn (pp. 231–51). Oxford: Blackwell.Google Scholar
Reichenbach, H. (1938). Experience and Prediction. University of Chicago Press.Google Scholar
Robinson, J. A. (1965). A machine-oriented logic based on the resolution principle, Journal of the Association for Computing Machinery 12: 23–41.CrossRefGoogle Scholar
Robinson, J. A. and Voronkov, A. (eds.) (2001). Handbook of Automated Reasoning, vol. 1. Amsterdam: Elsevier.
Rumelhart, D. E., McClelland, J. L., and the PDP Research Group. (1986). Parallel Distributed Processing: Explorations in the Microstructure of Cognition, vol. I: Foundations. Cambridge, MA: MIT Press.Google Scholar
Russell, B. (1940). An Inquiry into Meaning and Truth. London: George Allen and Unwin.Google Scholar
Russell, S. and Norvig, P. (2003). Artificial Intelligence: A Modern Approach (2nd edn.). Upper Saddle River, NJ: Prentice Hall.Google Scholar
Searle, J. (1980). Minds, brains and programs, Behavioral and Brain Sciences 3: 417–24.CrossRefGoogle Scholar
Searle, J. (1984). Minds, Brains, and Science. Cambridge, MA: Harvard University Press.Google Scholar
Sperling, G. (1960). The information available in brief visual presentations, Psychological Monographs: General and Applied 74: 1–29.CrossRefGoogle Scholar
Tolman, E. C. (1948). Cognitive maps in rats and men, Psychological Review 55, 189–208.CrossRefGoogle ScholarPubMed
Turing, A. M. (1936). On computable numbers with applications to the Entscheidungsproblem, Proceedings of the London Mathematical Society 42, 230–65.Google Scholar
Tolman, E. C. (1950). Computing machinery and intelligence, Mind 59: 433–60.Google Scholar
Wang, H. (1960). Toward mechanical mathematics, IBM Journal of Research and Development 4: 2–22.CrossRefGoogle Scholar
Winograd, T. (1990). Thinking machines: Can there be? Are we?, in Partridge, D. and Wilks, Y. (eds.), The Foundations of Artificial Intelligence (pp. 167–89). Cambridge University Press.CrossRefGoogle Scholar

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