Adaptive search space scaling in digital image registration

An image registration technique for application in X-ray, gamma-ray, and magnetic resonance imaging is described. The technique involves searching a real-valued, multidimensional, rectangular, symmetric space of bilinear geometrical transformations for a globally optimal transformation. Physical considerations provide theoretical limits on the search space, but the theoretically maximum allowable space is still often much larger than the smallest rectangular symmetric subspace that contains the optimal transformation. To reduce the search time, the current practice is to guess an optimal subspace from the maximum allowable space. This reduced space is then discretized and searched. An automatic technique to estimate adaptively a subspace from the maximum space during the search process itself is described. This adaptive technique is tested with two quite different types of search algorithms, namely, genetic algorithms and simulated annealing.

Theory formation in postulating enzyme kinetic mechanisms: reasoning with constraints

This paper reports on a prototype system for modeling and analyzing the expert reasoning involved in postulating enzyme kinetic mechanisms. It involves data-driven, theory-driven, and analogical components of reasoning within a generate-and-test cycle. Its central component is a set of domain-specific "filters" for matching experimentally and theoretically derived constraints. The input to the system consists of an abstracted qualitative description of an experiment and prior knowledge reported in the literature. Its output shows how the results match those expected for a set of postulated reaction mechanism models and also provides a trace of which features do or do not match each of the candidate topological models. Results, constraints, and models are all analyzed and compared to those from other, similar experiments. We deduced rules for interpreting the qualitative features of enzyme kinetic experiments from natural language descriptions in the literature and verified that the rules were correct by predicting the results for typical mechanisms. We obtained the correct behavior for all 37 states of a complex enzyme mechanism involving three substrates and three products. We tested our system on data from several published reports dealing with the enzyme hexokinase and obtained detailed listings of the differences in conclusions and interpretation reported in several journal articles. This system, which provides qualitative representations of enzyme kinetic results, should facilitate further experimentation on theory formation in enzyme kinetics and lead to more efficient experimental designs.

Some comments on rule induction

Computer induction is discussed in the light of concerns recently expressed by Bloomfield about the use of such algorithms in expert systems construction.

Gradualness facilitates knowledge refinement

To facilitate knowledge refinement, a system should be designed so that small changes in the knowledge correspond to small changes in the function or performance of the system. Two sets of experiments show the value of small, heuristically guided changes in a weighted rule base. In the first set, the ordering among numbers (reflecting certainties) makes their manipulation more straightforward than the manipulation of relationships. A simple credit assignment and weight adjustment strategy for improving numbers in a weighted, rule-based expert system is presented. In the second set, the rearrangement of predicates benefits from additional knowledge about the ``ordering'' among predicates. A third set of experiments indicates the importance of the proper level of granularity when augmenting a knowledge base. Augmentation of one knowledge base by analogical reasoning from another knowledge base did not work with only binary relationships, but did succeed with ternary relationships. To obtain a small improvement in the knowledge base, a substantial amount of structure had to be treated as a unit.