Getting Rid of Derivational Redundancy or How to Solve Kuhns Problem
This paper deals with the problem of derivational redundancy in scientific explanation, i.e. the problem that there can be extremely many different explanatory derivations for a natural phenomenon while students and experts mostly come up with one and the same derivation for a phenomenon (modulo the order of applying laws). Given this agreement among humans, we need to have a story of how to select from the space of possible derivations of a phenomenon the derivation that humans come up with. In this paper we argue that the problem of derivational redundancy can be solved by a new notion of shortest derivation, by which we mean the derivation that can be constructed by the fewest (and therefore largest) partial derivations of previously derived phenomena that function as exemplars. We show how the exemplar-based framework known as Data-Oriented Parsing or DOP can be employed to select the shortest derivation in scientific explanation. DOPs shortest derivation of a phenomenon maximizes what is called the derivational similarity between a phenomenon and a corpus of exemplars. A preliminary investigation with exemplars from classical and fluid mechanics shows that the shortest derivation closely corresponds to the derivations that humans construct. Our approach also proposes a concrete solution to Kuhns problem of how we know on which exemplar a phenomenon can be modeled. We argue that humans model a phenomenon on the exemplar that is derivationally most similar to the phenomenon, i.e. the exemplar from which the largest subtree(s) can be used to derive the phenomenon.
derivational redundancy kuhns problem scientific explanation shortest derivation data-oriented parsing derivational similarity exemplar-based reasoning