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<title>Evaluating model abstractions: a quantitative approach</title>
Author(s) -
Hessam S. Sarjoughian,
Bernard P. Zeigler,
François E. Cellier
Publication year - 1998
Publication title -
proceedings of spie, the international society for optical engineering/proceedings of spie
Language(s) - English
Resource type - Conference proceedings
SCImago Journal Rank - 0.192
H-Index - 176
eISSN - 1996-756X
pISSN - 0277-786X
DOI - 10.1117/12.319355
Subject(s) - semantic reasoner , computer science , correctness , focus (optics) , monotonic function , inductive reasoning , non monotonic logic , relation (database) , event (particle physics) , theoretical computer science , programming language , artificial intelligence , data mining , mathematics , mathematical analysis , physics , quantum mechanics , optics
An 'evaluation' approach devised for an inductive reasoning system called logic-based discrete-event inductive reasoner is the focus of this paper. The underlying inductive reasoning methodology utilizes abstractions as its primary means to deal with lack of knowledge. Based on abstractions and their treatments as assumptions, the logic-based discrete-event inductive reasoning system allows non- monotonic predictions. The evaluation approach takes into account explicitly the role of abstractions employed in non- monotonically derived multiple predictions. These predictions are ranked according to the type and number of abstractions used. The proposed evaluation approach is also discussed in relation to the dichotomy of model validation and simulation correctness.

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