# A New Direction in AI Toward a Computational Theory of Perceptions

• Lotfi A. Zadeh
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 89)

## Abstract

Humans have a remarkable capability to perform a wide variety of physical and mental tasks without any measurements and any computations. Familiar examples are parking a car, driving in city traffic, playing golf, cooking a meal, and summarizing a story. In performing such tasks, humans use perceptions of time, direction, speed, shape, possibility, likelihood, truth, and other attributes of physical and mental objects. Reflecting the bounded ability of the human brain to resolve detail, perceptions are intrinsically imprecise. In more concrete terms, perceptions are f-granular, meaning that (1) the boundaries of perceived classes are unsharp and (2) the values of attributes are granulated, with a granule being a clump of values (points, objects) drawn together by indistinguishability, similarity, proximity, and function. For example, the granules of age might be labeled very young, young, middle aged, old, very old, and so on.

F-granularity of perceptions puts them well beyond the reach of traditional methods of analysis based on predicate logic or probability theory. The computational theory of perceptions (CTP), which is outlined in this article, adds to the armamentarium of AI a capability to compute and reason with perception-based information. The point of departure in CTP is the assumption that perceptions are described by propositions drawn from a natural language; for example, it is unlikely that there will be a significant increase in the price of oil in the near future.

In CTP, a proposition, p, is viewed as an answer to a question, and the meaning of p is represented as a generalized constraint. To compute with perceptions, their descriptors are translated into what is called the generalized constraint language (GCL). Then, goal-directed constraint propagation is utilized to answer a given query. A concept that plays a key role in CTP is that of precisiated natural language (PNL).

The computational theory of perceptions suggests a new direction in AI—a direction that might enhance the ability of AI to deal with real-world problems in which decision-relevant information is a mixture of measurements and perceptions. What is not widely recognized is that many important problems in AI fall into this category.

## Keywords

Membership Function Computational Theory Possibility Distribution Information Granulation Fuzzy Graph
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

## Preview

Unable to display preview. Download preview PDF.

## Bibliography

1. Barsalou, L. W. 1999. Perceptual Symbol Systems. Behavioral and Brain Sciences 22: 577–660Google Scholar
2. Davis, E. 1990 Representations of Commonsense Knowledge. San Francisco, Calif.: Morgan KaufmannGoogle Scholar
3. Davis, E. 1987. Constraint Propagation with Interval Labels. Artificial Intelligence 32 (3): 281–331.
4. de Kleer, J., and Bobrow, D. G. 1984. Qualitative Reasoning with Higher-Order Derivatives. In Proceedings of the Fourth National Conference on Artificial Intelligence. Menlo Park, Calif.: American Association for Artificial IntelligenceGoogle Scholar
5. Dubois, D., and Prade, H. 1996. Approximate andCommonsense Reasoning: From Theory to Practice. In Proceedings of the Foundations of Intelligent Systems, Ninth International Symposium,19–33. Berlin: Springer-VerlagGoogle Scholar
6. Dubois, D.; Fargier, H.; and Prade, H. 1994. Propagation and Satisfaction of Flexible Constraints. In Fuzzy Sets, Neural Networks, and Soft Computing, eds. R. R. Yager and L. A. Zadeh, 166–187. New York: Von Nostrand Reinhold.Google Scholar
7. Forbus, K. D. 1984. Qualitative Process Theory. Artificial Intelligence 24 (1): 85–168.
8. Geng, J. Z. 1995. Fuzzy CMAC Neural Networks. Journal of Intelligent and Fuzzy Systems 3 (1): 87–102.Google Scholar
9. Kaufmann A., and Gupta, M. M. 1985. Introduction to Fuzzy Arithmetic: Theory and Applications. New York: Von Nostrand.
10. Kuipers, B. J. 1984. Qualitative Reasoning. Cambridge, Mass.: MIT Press.Google Scholar
11. Lano, K. 1991. A Constraint-Based Fuzzy Inference System. In Proceedings of EPIA 91, Fifth Portuguese Conference on Artificial Intelligence, eds. P. Barahona, L. M. Pereira, and A. Porto, 45–59. Berlin: Springer-Verlag.Google Scholar
12. Lenat, D. B. 1995. cyc: A Large-Scale Investment in Knowledge Infrastructure Communications of the ACM 38(11): 32–38Google Scholar
13. McCarthy, J. 1990. Formalizing Common Sense, eds. V. Lifschitz and J. McCarthy. Norwood, N.J.: Ablex.Google Scholar
14. McCarthy, J., and Hayes, P. J. 1969. Some Philosophical Problems from the Standpoint of Artificial Intelligence. In Machine Intelligence 4, eds. B. Meltzer and D. Michie, 463–502. Edinburgh: Edinburgh University Press.Google Scholar
15. Mani, I., and Maybury, M. T., eds. 1999. Advances in Automatic Text Summarization. Cambridge, Mass.: MIT Press.Google Scholar
16. Mavrovouniotis, M. L., and Stephanopoulos, G. 1987. Reasoning with Orders of Magnitude and Approximate Relations. In Proceedings of the Sixth National Conference on Artificial Intelligence, 626–630. Menlo Park, Calif.: American Association for Artificial Intelligence.Google Scholar
17. Novak, V. 1991. Fuzzy Logic, Fuzzy Sets, and Natural Languages. International Journal of General Systems 20 (1): 83–97.
18. Pedrycz, W., and Gomide, F. 1998. Introduction to Fuzzy Sets. Cambridge, Mass.: MIT Press.
19. Raiman, 0. 1991. Order of Magnitude Reasoning. Artificial Intelligence 51 (1): 11–38.
20. Sandewall, E. 1989. Combining Logic and Differential Equations for Describing Real-World Systems. In Proceedings of the First International Conference on Principles of Knowledge Representation and Reasoning, 412–420. San Francisco, Calif.: Morgan KaufmannGoogle Scholar
21. Shafer, G. 1976. A Mathematical Theory of Evidence. Princeton, N.J.: Princeton University PressGoogle Scholar
22. Struss, P. 1990. Problems of Interval-Based Qualitative Reasoning. In Qualitative Reasoning about Physical Systems, eds. D. Weld and J. de Kleer, 288–305. San Francisco, Calif.: Morgan KaufmannGoogle Scholar
23. Sun, R. 1994. Integrating Rules and Connectionism for Robust Commonsense Reasoning. New York: Wiley.
24. Vallee, R. 1995. Cognition et Systeme (Cognition and Systems). Paris: l’Interdisciplinaire Systeme(s)Google Scholar
25. Zadeh, L. A. 1999. From Computing with Numbers to Computing with Words-From Manipulation of Measurements to Manipulation of PerceptionsIEEE Transactions on Circuits and Systems 45(1): 105–119
26. Zadeh, L. A. 1997. Toward a Theory of Fuzzy Information Granulation and Its Centrality in Human Reasoning and Fuzzy Logic. Fuzzy Sets and Systems 90: 111–127.
27. Zadeh, L. A. 1986. Outline of a Computational Approach to Meaning and Knowledge Representation Based on the Concept of a Generalized Assignment Statement. In Proceedings of the International Seminar on Artificial Intelligence and Man-Machine Systems, eds. M. Thoma and A. Wyner, 198–211. Heidelberg: Springer-VerlagGoogle Scholar
28. Zadeh, L. A. 1973. Outline of a New Approach to the Analysis of Complex System and Decision Processes. IEEE Transactions on Systems, Man, and Cybernetics SMC-3(1): 28–44.

## Copyright information

© Springer-Verlag Berlin Heidelberg 2002

## Authors and Affiliations

• Lotfi A. Zadeh

There are no affiliations available