Perceptions play a central role in human cognition. There are close links between perceptions, consciousness and our ability to reason and make decisions on both conscious and subconscious levels. There is an enormous literature dealing with perceptions from every conceivable point of view. And yet, what is not in existence is a theory which makes it possible to compute with perceptions. A framework for such a theory—the computational theory of perceptions (CTP)—is described.
In CTP, the point of departure is the human ability to describe perceptions in a natural language. Thus, in CTP a perception is equated to its description, e.g., “It was very warm during much of the early part of summer.”
Perceptions are intrinsically imprecise, reflecting the bounded ability of sensory organs and ultimately the brain, to resolve detail and store information. More specifically, perceptions are f-granular in the sense that (a) the boundaries of perceived classes are imprecise; and (b) the values of attributes are granular, with a granule being a clump of values drawn together by indistinguishability, similarity, proximity or functionality. F-granularity of perceptions puts them well beyond the reach of existing meaning-representation systems based on bivalent logic.
In CTP, the meaning of a perception, p, is represented through the use of what is referred to as precisiated natural language (PNL). In PNL, the meaning of p is expressed as a generalized constraint on a variable, X, which, in general, is implicit on p. A collection of rules governing generalized constraint propagation is employed for reasoning and computing with perceptions.
CTP adds to existing theories the capability to operate on perception-based information exemplified by “It is very unlikely that there will be a significant increase in the price of oil in the near future.” This capability provides a basis for extending the ability of existing theories—and especially probability theory—to deal with real-world problems.
* Professor in the Graduate School and Director, Berkeley initiative in Soft Computing (BISC), Computer Science Division and the Electronics Resear ch Laboratory, Department of EECS, University of California, Berkeley, CA 94720- 1776; Telephone: 510-642-4959; Fax: 510-642-1712; E-Mail: firstname.lastname@example.org.