A Baysean Approach to Extracting Meaning from System Behavior
By William Dress
Instrumentation and Controls Division
Oak Ridge National Lab
Here's the abstract of Bill's paper;
his address is:
W. B. Dress MS 6011
Oak Ridge National Lab.
P.O. Box 2008
Oak Ridge TN 37831
The paper will appear in: IEEE SMC '98 "Semantics and Semiotics of Compex Computing Systems
La Jolla, CA October 12-15, 1998
The modeling relation and its reformulation to include the semiotic hierarchy is essential for the understanding, control, and successful re-creation of natural systems. This presentation will argue for a careful application of Rosen's modeling relationship to the problems of intelligence and autonomy in natural and artificial systems. To this end, I discuss the essential need for a correct theory of induction, learning and probability; and suggest that modern Baysian probability theory, developed by Cox, Jaynes, and others, can adequately meet such demands, especially on the operational level of extracting meaning from observations.
The methods of Bayseian and maximum Entropy parameter estimation have been applied to measurements of system observables to directly infer the underlying differential equations generating system behavior. This approach bypasses the usual method of parameter estimation based on assuming a functional form for the observable and then estimating the parameters that would lead to the particular observed behavior. The computational savings is great since only location parameters enter into the maximum- entropy calculations; this innovation finesses the need for non-linear parameters altogether. Such an approach more directly extracts the semantics inherent in a given system by going the root of the system meaning as expressed by abstract form or shape, rather than in syntactic particulars, such as signal amplitude and phase. Examples will be shown how the form of a system can be followed while ignoring unneccesary details.in this sense, we are observing the meaning of the "words" rather than being concerned with their particular expression or "language". For the present discussion, empirical models are embodied by the differential equations underlying, producing, or describing the behavior of a process as measured or tracked by a particular variable set- the observables. (................I skip a bit)