A Static Model-Based Engine for
Model-Based Reasoning
Peter Fröhlich and Wolfgang Nejdl
Abstract
Most systems for model-based reasoning record
justifications during the search for solutions. In the popular ATMS
systems this can lead to an explosion of recorded information when
solving complicated problems. We propose an engine for model-based
reasoning which works directly on logical models, uses static
precompiled information on the structure of the underlying theory and
does no further recording during search. This engine (DRUM-II) solves
large complicated problems with attractive time and space complexity.
To demonstrate the efficiency of the engine we give a new
characterization of a popular benchmark suite, solve it with our
engine and compare the performance to previous results.
Keywords: Diagnosis, Non-monotonic Reasoning
The full paper is available as a postscript file