Computational Models of Tacit Knowledge

When an expert designer uses a term such as “interference fit” or “H7-r6”, they effortlessly invoke a rich set of associations across a wide range of experience. While at one level, the meaning of a term such as H7 is formally specified, many of these associations are implicit and hard to characterize formally. The explicit concepts build on layers of implicit abstraction; e.g. the concept of fit would be difficult to achieve without the commonsense notion of “tight”, discriminated by human infants from five month onwards. We propose that such ubiquitous expertise may be acquired as functionally relevant low-dimensional chunks in an experiential space, which are then stabilized through language. The technical terms of design build on these everyday concepts by mechanisms such as extension or narrowing of their semantics. We suggest a two-stage computational analog of this process: (a) the baby designer stage learns elementary concepts as tacit patterns on an input space; and (b) the novice designer stage relates these early concepts to explicitly defined design terms to arrive at a grounded semantics for the new symbols. We illustrate the process through the development of concepts such as interference fit.