Skip to main content Accessibility help
×
Home
Hostname: page-component-7f7b94f6bd-59m7g Total loading time: 1.794 Render date: 2022-06-29T23:26:14.129Z Has data issue: true Feature Flags: { "shouldUseShareProductTool": true, "shouldUseHypothesis": true, "isUnsiloEnabled": true, "useRatesEcommerce": false, "useNewApi": true } hasContentIssue true

25 - Artificial Intelligence

from Part V - Intelligence and Information Processing

Published online by Cambridge University Press:  13 December 2019

Robert J. Sternberg
Affiliation:
Cornell University, New York
Get access

Summary

Artificial intelligence (AI) is a scientific discipline that seeks to understand intelligence through the design and construction of intelligent machines. AI and cognitive science have a strong two-way relationship: Cognitive psychology often has inspired AI theories, and AI research has led to new theories of cognition that have been tested through psychological experimentation. While AI theories of cognition often are under-constrained, cognitive theories of AI tend to be over-constrained. Nevertheless, AI is useful for cognitive psychologists both as a source of new ideas and insights, and an experimental testbed. In this chapter, we describe some of the basic concepts and methods of AI by taking robot navigation in a city as an illustrative example. We also briefly discuss the history of AI, methods for assessing progress in AI, and some of AI’s potential impacts on society.

Type
Chapter
Information
Publisher: Cambridge University Press
Print publication year: 2020

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Albus, J. S. (1991). Outline for a theory of intelligence. IEEE Transactions on Systems, Man, and Cybernetics, 21(3), 473509.CrossRefGoogle Scholar
Ali, K., & Goel, A. (1996). Combining navigational planning and reactive control. Proceedings of the AAAI-96 Workshop on Reasoning About Actions, Planning and Control: Bridging the Gap (pp. 17). Portland: AAAI Press.Google Scholar
Anderson, J. R. (2013). The adaptive character of thought. New York: Psychology Press.CrossRefGoogle Scholar
Anderson, J. R., & Lebiere, C. (1998). The atomic components of thought. Mahwah, NJ: Erlbaum.Google Scholar
Arkin, R. (1999). Behavior-based robotics. Cambridge, MA: MIT Press.Google Scholar
Berners-Lee, T., Hendler, J., & Lassila, O. (2001). Semantic web. Scientific American, 284(5), pp. 3543.CrossRefGoogle Scholar
Besold, T., Schlorlemmer, M., & Smaill, A. (Eds.) (2015) Computational creativity research: Towards creative machines. New York: Atlantis Press.Google Scholar
Boström, N. (2014). Superintelligence: Paths, dangers, strategies. Oxford: Oxford University Press.Google Scholar
Bringsjord, S., & Schimanski, B. (2003). What is artificial intelligence? Psychometric AI as an answer. Proceedings of the 18th International Joint Conference on Artificial Intelligence (IJCAI-03) (pp. 887893). San Francisco: Morgan Kaufmann.Google Scholar
Buchanan, B., & Shortliffe, E. (1984). Rule based expert systems: The Mycin experiments of the Stanford Heuristic Programming Project. Boston: Addison-Wesley.Google Scholar
Cox, M., & Raja, A. (2011), Metareasoning: Thinking about thinking, Cambridge, MA: MIT Press.CrossRefGoogle Scholar
Craik, K. (1943). The nature of explanation. Cambridge, UK: Cambridge University Press.Google Scholar
Davies, J., & Francis, A. G. (2013). The role of artificial intelligence research methods in cognitive science. In West, R. & Stewart, T. (Eds.), Proceedings of the 12th International Conference on Cognitive Modeling (pp. 439444). Ottawa: Carleton University.Google Scholar
Evans, T. G. (1968). A program for the solution of a class of geometric-analogy intelligence-test questions. In Minsky, M. (Ed.), Semantic information processing (pp. 271353). Cambridge, MA: MIT Press.Google Scholar
Ford, K., Hayes, P., Glymour, C., & Allen, J. (2015). Cognitive orthoses: Toward human-centered AI. AI Magazine, 36(4), 58.CrossRefGoogle Scholar
Glasgow, J., Narayanan, N. H., & Chandrasekaran, B. (Eds.) (1995). Diagrammatic reasoning: Cognitive and computational perspectives. Cambridge, MA: MIT Press.Google Scholar
Goel, A., Ali, K., Donnellan, M., Gomez, A., & Callantine, T. (1994). Multistrategy adaptive navigational path planning. IEEE Expert, 9(6), 5765.CrossRefGoogle Scholar
Goel, A., Stroulia, E., Chen, Z., & Rowland, P. (1997). Model-based reconfiguration of schema-based reactive control architectures. In Proceedings of the AAAI Fall Symposium on Model-Directed Autonomous Systems (pp. 16). Cambridge, MA: AAAI.Google Scholar
Harnad, S. (1992). The Turing test is not a trick: Turing indistinguishability is a scientific criterion. SIGART Bulletin, 3(4), 910.CrossRefGoogle Scholar
Hsu, F., Campbell, M., & Hoane, A. (1995). Deep Blue system overview. In Wolfe, M. (Ed.), Procs. the 1995 International Conference on Supercomputing (pp. 240244).New York: ACM Press.Google Scholar
Kahneman, D. (2011). Thinking, fast and slow. New York: Farrar, Straus and Giroux.Google Scholar
Kolodner, J. (1993). Case-based reasoning. San Francisco: Morgan Kaufmann.CrossRefGoogle Scholar
Kotseruba, I., Gonzalez, O., & Tsotsos, J. (2016). A review of 40 years of cognitive architecture research: Focus on perception, attention, learning and applications. The Computing Research Repository (CoRR). arXiv preprint arXiv:1610.08602, 1–74.
Kunda, M., McGreggor, K., & Goel, A. (2013). A computational model for solving problems from the Raven’s Progressive Matrices intelligence test using iconic visual representations. Cognitive Systems Research, 22, 4766.CrossRefGoogle Scholar
Kurzweil, R. (2005). The singularity is near: When humans transcend biology. New York: Viking Adult.Google Scholar
Laird, J. E. (2012). The Soar cognitive architecture. Cambridge, MA: MIT press.CrossRefGoogle Scholar
Laird, J. E., Lebiere, C., & Rosenbloom, P. S. (2017). A standard model of the mind: Toward a common computational framework across artificial intelligence, cognitive science, neuroscience, and roboticsAI Magazine38(4). https://doi.org/10.1609/aimag.v38i4.2744CrossRefGoogle Scholar
Laird, J., Newell, A., & Rosenbloom, P. (1987). Soar: An architecture for general intelligence. Artificial Intelligence, 33, 164.CrossRefGoogle Scholar
Langley, P. (2012). The cognitive systems paradigm. Advances in Cognitive Systems, 1, 313.Google Scholar
Langley, P., Laird, J., & Rogers, S. (2009). Cognitive architectures: Research issues and challengesCognitive Systems Research10(2), 141160.CrossRefGoogle Scholar
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learningNature521(7553), 436444.CrossRefGoogle ScholarPubMed
Lenat, D., & Guha, R. (1990). Building large knowledge based systems: Representation and inference in the Cyc project. Boston: Addison-Wesley Longman.Google Scholar
Lindsay, R., Buchanan, B., Feigenbaum, E., & Lederberg, J. (1980). Applications of artificial intelligence for chemical inference: The Dendral project. New York: McGraw-Hill.Google Scholar
Marcus, G., Rossi, F., & Veloso, M. (2016). Beyond the Turing test. Special issue, AI Magazine, 37(1), 3101.CrossRefGoogle Scholar
Marr, D. (1982). Vision. New York: Henry Holt.Google Scholar
McCarthy, J. (1988). Mathematical logic in AI. Daedalus, 117(1), 297311.Google Scholar
McCarthy, J., Minsky, M., Rochester, N., & Shannon, C. (1955/2006). A proposal for the Dartmouth summer research project on artificial intelligence. AI Magazine, 27(4), 1214.Google Scholar
McClelland, J. L., Rumelhart, D. E., & PDP Research Group (1986). Parallel distributed processing: Explorations in the microstructure of cognition, vol. 2, Psychological and biological models. Cambridge, MA: MIT Press.Google Scholar
Minsky, M. L. (1975). A framework for representing knowledge. In Winston, P. H. (Ed.), The psychology of computer vision (pp. 182). New York: McGraw-Hill.Google Scholar
Minsky, M. L., & Papert, S. A. (1969). Perceptrons. Cambridge, MA: MIT Press.Google Scholar
Mitchell, M. (1998). An introduction to genetic algorithms. Cambridge, MA: MIT Press.Google Scholar
Newell, A., Shaw, J. C., & Simon, H. A. (1958). Elements of a theory of problem solving. Psychological Review, 63(3), 151166.CrossRefGoogle Scholar
Newell, A., & Simon, H. A. (1972). Human problem solving. Englewood Cliffs, NJ: Prentice-Hall.Google Scholar
Pearl, J. (1988). Probabilistic reasoning in intelligent systems. San Francisco: Morgan Kauffman.Google Scholar
Pearl, J. (2000). Causality: Models, reasoning and inference. New York: Cambridge University Press.Google Scholar
Piaget, J. (1952). The origins of intelligence in children. New York: International Universities Press.CrossRefGoogle Scholar
Pinker, S. (2018). Enlightenment now: The case for reason. Science, humanism, and progress. New York: Viking.Google Scholar
Quillian, M. (1968). Semantic Memory. In Minsky, M. (Ed.), Semantic information processing (pp. 227270). Cambridge, MA: MIT Press.Google Scholar
Rabiner, L., & Juang, B. H. (1986). An introduction to hidden Markov models. IEEE ASSP Magazine, January, 416.CrossRef
Raphael, B. (1976). The thinking computer. New York: W. H. Freeman.Google Scholar
Raven, J. C. (1962). Advanced Progressive Matrices Set II. London: H. K. Lewis.Google Scholar
Rumelhart, D. E., McClelland, J. L., & PDP Research Group (1986). Parallel distributed processing: Explorations in the microstructure of cognition, vol. 1, Foundations. Cambridge, MA: MIT Press.Google Scholar
Samsonovich, A. V. (2010). Toward a unified catalog of implemented cognitive architectures. In Samsonovich, A. V., Jóhannsdóttir, K. R., Chella, A., & Goertzel, B. (Eds.), Proceeding of the Conference on Biologically Inspired Cognitive Architectures (pp. 195244). New York: IOS Press.Google Scholar
Schank, R. C. (1975). Conceptual information processing. New York: Elsevier.Google Scholar
Schank, R. C. (1982). Dynamic memory (2nd ed.). New York: Cambridge University Press.Google Scholar
Schank, R. C., & Abelson, R. P. (1977). Scripts, plans, goals and understanding. Hillsdale, NJ: Erlbaum.Google Scholar
Silver, D., Huang, A., Maddison, C., Guez, A., Sifre, L., Driessche, G., et al. (2016). Mastering the game of Go with deep neural networks and tree search. Nature, 529(7587), 484489.CrossRefGoogle ScholarPubMed
Silver, D., Schrittwieser, J., Simonyan, K., Antonoglou, I., Huang, A., Guez, A., et al. (2017). Mastering the game of Go without human knowledge. Nature, 550(7676), 354359.CrossRefGoogle ScholarPubMed
Simon, H. A. (1996). Sciences of the artificial (3rd ed.). Cambridge, MA: MIT Press.Google Scholar
Singh, P., Lin, T., Mueller, E. T., Lim, G., Perkins, T., & Zhu, W. L. (2002). Open mind common sense: Knowledge acquisition from the general public. In Meersman, R. & Tari, Z. (Eds.), On the Move to Meaningful Internet Systems: OTM Confederated International Conferences (pp. 12231237). Berlin: Springer.Google Scholar
Sowa, J. (1987). Semantic networks. In Shapiro, S. (Ed.), Encylopedia of AI (pp. 10111024). New York: Wiley.Google Scholar
Stanovich, K. E. (2004). The robot’s rebellion. Chicago: University of Chicago Press.CrossRefGoogle Scholar
Stanovich, K. E., & West, R. F. (2000). Individual differences in reasoning: Implications for the rationality debate. Behavioral and Brain Sciences, 23, 645726.CrossRefGoogle ScholarPubMed
Stroulia, E., & Goel, A. K. (1999). Evaluating problem-solving methods in evolutionary design: The autognostic experiments. International Journal of Human-Computer Studies, 51, 825847.CrossRefGoogle Scholar
Sutton, R. S., & Barto, A. (1998). Reinforcement learning: An introduction. Cambridge, MA: MIT Press.Google Scholar
Tomasello, M. (1999). The cultural origins of human cognition. Cambridge, MA: Harvard University Press.Google Scholar
Turing, A. M. (1950). Computing machinery and intelligence. Mind, 59, 433460.CrossRefGoogle Scholar
Veale, T., & Cardoso, A. (2018). Computational creativity: The philosophy and engineering of autonomously creative systems. Berlin: Springer.Google Scholar
Von Anh, L., Liu, R., & Blum, M. (2006). Peekaboom: A game for locating objects in images. In Grinter, R., Rodden, T., Aoki, P., Cutrell, E., Jeffries, R., & Olson, G. (Eds.), Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (Montreal, April 22–27) (pp. 5564). New York: ACM Press.CrossRefGoogle Scholar
Wechsler, D. (1939). The measurement of adult intelligence. Baltimore, MD: Williams & Wilkins.CrossRefGoogle Scholar
Weiner, N. (1961). Cybernetics (2nd ed.). Cambridge, MA: MIT Press.Google Scholar
Weizenbaum, J. (1966). ELIZA – a computer program for the study of natural language communication between man and machine. Communications of the ACM, 9(1), 3645.CrossRefGoogle Scholar
Winograd, T. (1972). Understanding natural language. San Diego, CA: Academic Press.CrossRefGoogle Scholar
3
Cited by

Save book to Kindle

To save this book to your Kindle, first ensure coreplatform@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

Available formats
×