Paper Info
Title
The generation of 'critical problems' by abstract interpretations of student models
Abstract
As the range of models which a tutoring system can capture is extended, efficient diagnosis becomes more difficult. We have implemented a partial solution to this problem, in the form of a 'Critical Problem' generator. We argue that great diagnostic power can be obtained by generating discriminating problem examples. In general, efficient diagnosis is just not possible without such a hypothesis-tes ting capability. We describe a program, PG, which given a pair of production rule models and a description of the class of problems which the student must solve, generates an abstract specification of the problems which discriminate between those two hypotheses. The key to this problem lies in the realisation that we are only interested in the abstract mapping between a models inputs and outputs; from the point of view of generating a Critical Problem, the intermediate processing of the model is irrelevant.
Year
Venue
Keywords
1989
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 1
critical problem,partial solution,abstract mapping,abstract specification,abstract interpretation,hypothesis-testing capability,problem example,Critical Problem,student model,intermediate processing,great diagnostic power,models input,efficient diagnosis
Field
DocType
Citations 
Computer science,Realisation,Artificial intelligence,Machine learning
Conference
1
PageRank 
References 
Authors
0.37
5
1
Name
Order
Citations
PageRank
Rick Evertsz1224.31