[mod.ai] Seminar - Learning Arguments of Functional Descriptions

PRASAD@RED.RUTGERS.EDU (03/18/86)

                    Machine Learning Colloquium

       	
         LEARNING ARGUMENTS OF INVARIANT FUNCTIONAL DESCRIPTIONS

                        Mieczyslaw M. Kokar
	              Northeastern University
                       360 Huntington Avenue
                         Boston, MA 02115

                     11 AM, March 25, Tuesday
                         #423, Hill Center


The main subject of this presentation is discovery of concepts from
observation. The focus is on a special kind of concepts - arguments of
functional descriptions. The functions considered here are to be
meaningful, i.e., computable functions expressed in terms of the operations
defining the representation language in which the concepts are described.
Such functions are invariant under transformations of the representation 
language into equivalent representations.

It will be shown that the feature of invariance can be utilized in 
formulating and testing hypotheses about relevance of arguments of functional
descriptions. The main point is that the arguments do not need to be changed
to test the relevance. This is very important to the discovery process as the
arguments to be discovered are not known, therefore, how could they be
controlled?

Simple examples of discovering concepts of physical parameters (arguments
of physical laws) will be discussed.

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