Accession Number : ADA052212


Title :   Learning by Hypothesizing and Justifying Transfer Frames,


Corporate Author : MASSACHUSETTS INST OF TECH CAMBRIDGE ARTIFICIAL INTELLIGENCE LAB


Personal Author(s) : Winston,Patrick H


Full Text : https://apps.dtic.mil/dtic/tr/fulltext/u2/a052212.pdf


Report Date : Apr 1977


Pagination or Media Count : 35


Abstract : Learning is defined to be the computation done by a student when there is a transfer of information to him from a teacher. In the particular kind of learning discussed, the teacher names a source and a destination. In the sentence, 'Robbie is like a fox,' fox is the source and Robbie is the destination. The student, on analyzing the teacher's instruction, computes a kind of filter called a transfer frame. It stands between the source and the destination and determines what information is allowed to pass from one to the other. Computing the transfer frame requires two steps: hypothesis and evaluation. In the hypothesis step, potentially useful transfer frames are produced through an analysis of the information in the source and its immediate relatives. For Robbie, a robot, the way it compares with other robots would be noted. In the evaluation step, the better of the hypothesized frames are selected through a study of the destination frame, its relatives, and the general context. Some source-destination pairs may be generated by the student acting alone. There is also the possibility of making notes that are useful in deciding if conclusion makes sense. (Author)


Descriptors :   *HYPOTHESES , *COMPUTER AIDED INSTRUCTION , *LEARNING , COMPUTER PROGRAMS , INFORMATION TRANSFER , ARTIFICIAL INTELLIGENCE , FRAMES


Subject Categories : Humanities and History
      Computer Programming and Software
      Bionics


Distribution Statement : APPROVED FOR PUBLIC RELEASE