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Comments on Peter Cheeseman's An inquiry into computer understanding
Author(s) -
Aleliunas Romas
Publication year - 1988
Publication title -
computational intelligence
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.353
H-Index - 52
eISSN - 1467-8640
pISSN - 0824-7935
DOI - 10.1111/j.1467-8640.1988.tb00092.x
Subject(s) - set (abstract data type) , normative , computer science , artificial intelligence , inference , component (thermodynamics) , machine learning , management science , epistemology , philosophy , physics , economics , thermodynamics , programming language
I agree that probability in some, possibly disguised, form is a necessary component of practical inference. Our goal, therefore, must be to find how to make probability practical. Normatively motivated thinking (which includes textbook Bayesianism) is, however, mute on two topics that are crucial to practicality: the choice of a limited but useful set of initial hypotheses and the choice of decision rules. The best way of reducing the computational demands of probability is to use the smallest and simplest set of hypotheses, and the crudest decision rules that are compatible with your application's goals. Learning how to make these choices is an empirical, not a normative, endeavour.