Skip to main content Accessibility help

We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings.

Close cookie message
×
Hostname: page-component-586b7cd67f-t7czq Total loading time: 0 Render date: 2024-11-22T23:02:45.828Z Has data issue: false hasContentIssue false

1 - History, motivations, and core themes

Published online by Cambridge University Press:  05 July 2014

By
Stan Franklin
Edited by
Keith Frankish  and
William M. Ramsey
Stan Franklin
Affiliation:
University of Memphis
Keith Frankish
Affiliation:
The Open University, Milton Keynes
William M. Ramsey
Affiliation:
University of Nevada, Las Vegas
Get access

Summary

Introduction

This chapter introduces the field of artificial intelligence (AI) through a review of its core themes, history, major research areas, and current trends. The goal is to provide the reader with sufficient background context for understanding and appreciating the subsequent chapters in the volume.

Overview of core themes

The history of artificial intelligence may be best understood in the context of its core themes and controversies. Below is a brief listing of such AI distinctions, issues, themes, and controversies. It would be well to keep these in mind during your reading of the rest of this chapter. Each of the themes will be expanded upon and clarified as the chapter progresses. Many of these result from there being, to this day, no agreed upon definition of intelligence within the AI community of researchers.

Smart software vs. cognitive modeling. AI has always been a part of computer science, an engineering discipline aimed at creating smart computer programs – that is, intelligent software products to meet human needs. We shall see a number of examples of such smart software. AI also has its science side, which is aimed at helping us understand human intelligence. This endeavor includes building software systems that “think” in human-like ways, as well as producing computational models of aspects of human cognition. These computational models provide hypotheses for cognitive scientists.

Keywords

natural language processingsoft computingdevelopmental roboticskiller applicationknowledge-based expert systemstheorem proverscognitive computingchess playersartificial intelligence
Type
Chapter
Information
The Cambridge Handbook of Artificial Intelligence , pp. 15 - 33
Publisher: Cambridge University Press
Print publication year: 2014

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

Franklin, S. (1995). Artificial Minds. Cambridge MA: MIT Press. In this book for the intelligent layperson, the reader will find expanded discussions of most of the major themes and ideas of this chapter.Google Scholar
Russell, S. and Norvig, P. (2010). Artificial Intelligence: A Modern Approach (3rd edn.). Upper Saddle River, NJ: Prentice Hall. This best-selling text offers the most comprehensive, state-of-the-art introduction to the theory and practice of artificial intelligence. It is both readable and monumentally complete, perfect for a general reference to the various topics in this chapter.Google Scholar
Timeline: A Brief History of Artificial Intelligence. An online chronology of significant events in the History of AI prepared by the Association for the Advancement of Artificial Intelligence.
Brooks, R. A. (1991). Intelligence without representation, Artificial Intelligence 47: 139–59.CrossRefGoogle Scholar
Chalmers, D. (1990). Why Fodor and Pylyshyn were wrong: The simplest refutation, in Proceedings of the 12th Annual Conference of the Cognitive Science Society (pp. 340–7). Hillsdale, NJ: Lawrence Erlbaum.Google Scholar
Church, A. (1936). An unsolvable problem of elementary number theory, American Journal of Mathematics 58: 345–63.CrossRefGoogle Scholar
Davis, R., Buchanan, B., and Shortliffe, E. (1977). Production rules as a representation for a knowledge-based consultation program, Artificial Intelligence 8: 15–45.CrossRefGoogle Scholar
Fodor, J. A. and Pylyshyn, Z. W. (1988). Connectionism and cognitive architecture, Cognition 28: 3–71.CrossRefGoogle ScholarPubMed
Franklin, S. and Graesser, A. (1997). Is it an agent, or just a program?: A taxonomy for autonomous agents, in Müller, J., Wooldridge, M. J., and Jennings, N. R. (eds.), Intelligent Agents III: Agent Theories, Architectures, and Languages (pp. 21–35). Berlin: Springer Verlag.CrossRefGoogle Scholar
Holland, J. H. (1975). Adaptation in Natural and Artificial Systems. Ann Arbor: University of Michigan Press.Google Scholar
Lindsay, R. K., Buchanan, B. G., Feigenbaum, E. A., and Lederberg, J. (1980). Applications of Artificial Intelligence for Organic Chemistry: The Dendral Project. Columbus, OH: McGraw-Hill.Google Scholar
McClelland, J. L., Rumelhart, D. E., and the 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
McCulloch, W. S. and Pitts, W. H. (1943). A logical calculus of the ideas immanent in nervous activity, Bulletin of Mathematical Biophysics 5: 115–33.CrossRefGoogle Scholar
McCune, W. (1997). Solution of the Robbins problem, Journal of Automated Reasoning 19: 263–76.CrossRefGoogle Scholar
McDermott, J. P. (1980). R1: The formative years, AI Magazine 2: 21–9.Google Scholar
Minsky, M. (1954). Neural Nets and the Brain Model Problem. PhD. Dissertation, Princeton University.Google Scholar
Minsky, M. and Papert, S. (1969). Perceptrons. Cambridge, MA: MIT Press.Google Scholar
Newell, A., Shaw, J. C., and Simon, H. A. (1959). Report on a general problem-solving program, Proceedings of the International Conference on Information Processing, Paris (pp. 256–64). Paris:UNESCO.Google Scholar
Post, E. L. (1943). Formal reductions of the general combinatorial decision problem, American Journal of Mathematics 65: 197–215.CrossRefGoogle Scholar
Rosenblatt, F. (1958). The perceptron: A probabilistic model for information storage and organization in the brain, Psychological Review 65: 386–408.CrossRefGoogle Scholar
Rumelhart, D. E., McClelland, J. L., and the PDP Research Group (1986). Parallel Distributed Processing: Explorations in the Microstructure of Cognition, vol. 1: Foundations. Cambridge, MA: MIT Press.Google Scholar
Russell, S. and Norvig, P. (2010). Artificial Intelligence: A Modern Approach (3rd edn.). Upper Saddle River, NJ: Prentice Hall.Google Scholar
Samuel, A. L. (1959). Some studies in machine learning using the game of checkers, IBM Journal of Research and Development 3: 211–29.CrossRefGoogle Scholar
Smolensky, P. (1987). The constituent structure of connectionist mental states: A reply to Fodor and Pylyshyn, Southern Journal of Philosophy, 26 (Supplement): 137–63. Reprinted in T. Horgan and J. Tienson (eds.), (1991), Connectionism and the Philosophy of Mind (pp. 281–308). Dordrecht: Kluwer Academic.CrossRefGoogle Scholar
Turing, A. M. (1936). On computable numbers, with an application to the Entscheidungsproblem, Proceedings of the London Mathematical Society 42: 230–65.Google Scholar
Smolensky, P. (1950). Computing machinery and intelligence, Mind 59: 433–60. Reprinted in E. A. Feigenbaum and J. Feldman (eds.) (1963), Computers and Thought (pp. 11–35). New York: McGraw-Hill.Google Scholar
Varela, F. J., Thompson, E., and Rosch, E. (1991). The Embodied Mind. Cambridge, MA: MIT Press.Google Scholar
Winograd, T. (1972). Understanding Natural Language. San Diego: Academic Press.Google Scholar
Zadeh, L. A., (1965). Fuzzy sets, Information and Control 8: 338–53.CrossRefGoogle Scholar

Save book to Kindle

To save this book to your Kindle, first ensure [email protected] 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
×