le to compute with perceptions it is necessary to have a means of representing their meaning in a way that lends itself to computation. Conventional approaches to meaning representation cannot serve this purpose because the intrinsic imprecision of perceptions puts them well beyond the expressive power of predicate logic and related systems.
In the computational theory of perceptions, representation of meaning is a preliminary to reasoning with perceptions--a process that starts with a collection of perceptions that constitute the initial data set (IDS) and terminates in a proposition or a collection of propositions that play the role of an answer to a query, that is, the terminal data set (TDS).
The principal aim of the computational theory of perceptions is the development of an automated capability to reason with perception-based information. Existing theories do not have this capability and rely instead on conversion of perceptions into measurements--a process that in many cases is infeasible, unrealistic, or counterproductive. In this perspective, addition of the machinery of the computational theory of perceptions to existing theories may eventually lead to theories that have a superior capability to deal with real-world problems and make it possible to conceive and design systems with a much higher MIQ (Machine IQ) than those we have today.