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

Humans have a remarkable capability to perform a wide variety of physical and mental tasks without any measurements and any computations. Familar examples are: parking a car, driving in city traffic, playing golf, cooking a meal, and summarizing a story. In performing such tasks, humans employ 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 (a) the boundaries of perceived classes are unsharp; and (b) the values of attributes are granulated, with a granule being a clump of values (points, objects) drawn together by indistinguishability, similarity, proximity, and functionality. For example, the granules of age might be labeled very young, young, middle-age, old, very old, etc.