bobgian@psuvax.UUCP (Bob Giansiracusa) (03/26/84)
ADAPTIVE COGNITIVE MODEL FORMATION
The goal of this work is the automatic construction of models which can
predict and characterize the behavior of dynamical systems at multiple
levels of abstraction.
Numeric models used in simulation studies can PREDICT system behavior
but cannot EXPLAIN those predictions. Traditional expert systems can
explain certain predictions, but their accuracy is usually limited to
qualitative ("symbolic") statements. This research effort attempts to
couple the explanatory power of symbolic representations with the
precision and testability of numeric models.
Additionally, the computational burden implicit in the use of numeric
simulation models rapidly becomes astronomical when accurate performance
is needed over large domains (fine sampling density).
The solution my work explores consists of developing AUTOMATICALLY
a hierarchical sequence of SYMBOLIC models which convey QUALITATIVE
information of the sort that a human analyst generates when interpreting
numeric simulations. These symbolic models portray system behavior at
multiple levels of abstraction, allowing symbolic simulation and inference
procedures to optimize the "run time" versus "accuracy" tradeoff.
I profess the philosophical bias that the study of learning and modeling
mechanisms can proceed productively in a relatively domain-independent
manner. Obviously, domain-specific knowledge will speed the solution search
process. Such constraints can be regarded as "seeds" for search in a process
whose algorithm is largely domain-independent. Anecdotal support for this
hypothesis comes from the observation that HUMANS can become expert at theory
and model formation in a wide variety of different domains.
--
Bob Giansiracusa
Computer Science Dept, Penn State U, 814-865-9507 (ofc), 814-234-4375 (home)
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