Title page for ETD etd-03242009-040545
|Type of Document
||Analogical representation in temporal, spatial, and mnemonic reasoning
||Master of Science
||Computer Science and Applications
|Roach, John W.
|Ehrich, Roger W.
|Nutter, Jane Terry
|Date of Defense
The traditional Euclidean approach to problem solving in AI has always designed
representations for a domain and then spent considerable effort on the methods of
efficiently searching the representation in order to extract the desired information. We feel
that the emphasis in problem solving should be on the automated construction of the
knowledge representation and not on the searching of the representation.
This thesis proposes and implements an alternative approach: that of analogical
representation. Analogical representation differs from the Euclidean methodology in that it
creates a representation for the data from which the acquisition of information is done by
simple 'observation.' It is not our goal to propose a system that reduces the NP-hard
problem of temporal reasoning to a lower complexity. Our approach simply minimizes the
number of times that we must pay the exponential expense. Furthermore, the
representation can encode uncertainty and unknownness in an efficient manner. This
allows for 'intelligent' creation of a representation and removes the 'mindless' mechanical
search techniques from information retrieval, placing the computational effort where it
should be: on representation construction.
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