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Journal of Artificial Intelligence Research
1 (1999),
pp. 1-38.  Submitted 4/98; published 1/99.
© 1999 AI Access Foundation
and Morgan Kaufmann Publishers.
All rights reserved.
Order of Magnitude Comparisons of Distance
Ernest Davis
Courant Institute
New York University 
New York, NY 10012
Email:  davise@cs.nyu.edu
Abstract:
Order of magnitude reasoning -- reasoning by rough comparisons 
of the sizes of quantities -- is often called ``back of the envelope 
calculation", with the implication that the calculations are quick though 
approximate.  
This paper exhibits an interesting class of constraint sets in which 
order of magnitude reasoning is demonstrably fast. 
Specifically, we present a polynomial-time algorithm 
that can solve a set of constraints of the 
form ``Points a and b are much closer together than points c and d.'' 
We prove that this algorithm can be applied if 
``much closer together'' is interpreted either as referring to an infinite 
difference in scale or as referring to a finite 
difference in scale, as long as the difference in scale 
is greater than the number of variables in the constraint set. 
We also prove that the first-order theory over such constraints is 
decidable.