- S. Fahlman. 2008. Working Paper 57 a Hypothesis-frame System for Recognition Problems. Abstract: This paper proposes a new approach to a broad class of recognition problems ranging from medical diagnosis to vision. The features of this approach include a top-down hypothesize-and-test style and the use of a great deal of high-level knowledge about the subject. This knowledge is packaged into small groups of related facts and procedures called frames. Work reported herein was conducted at the Artificial Intelligence Laboratory, a Massachusetts Institute of Technology research program supported in part by the Advanced Research Projects Agency of the Department of Defense and monitored by the Office of Naval Research under Contract Number N00014-70-A-0362-0005. Working Papers are informal papers intended for internal use. A Hypothesis-Frame System for Recognition Problems Ph.D. Thesis Proposal Scott Fahlman <<The ideas expressed in this paper are still in their formative stages. I would welcome any thoughts readers might have about them, about the proposed research, or about this presentation.>> A pattern of light and darkness strikes a person's retina and he sees a familiar face, a cow, or a cloud shaped like Texas; vibrations strike his eardrum and he hears words and sentences, a Russian accent, or the call of the Arctic Loon; a chess master looks at pieces on a board and sees a vulnerable king, an impenetrable queen-side, a likely pawn for promotion; a doctor listens to symptoms, reads lab reports, thumps the patient, and arrives at his diagnosis. These examples differ in the extent to which they are conscious acts, but they all are acts of recognition the process of fitting some specific set of observable data (called the sample) into some loosely defined conceptual category, chosen from a large number of such categories. Because it lies at the heart of so many of our mental processes, recognition has fascinated philosophers and psychologists for centuries. Gestalt psychology, in particular, owes its existence to the cloud of mystery that surrounds the seemingly instantaneous nature of the recognition act. This thesis will be an attempt to dispel some of that mystery. Most current computer recognition programs employ a bottom-up
