The paper describes a technique to extract symbolic rules from a trained artificial neural network with functional dependency preprocessing. RULEX (R. Andrews and S. Geva, 1994; 1995), classified as a decompositional technique of rule extraction from trained neural network in a recent survey by R. Andrews et al. (1995), is used to extract symbolic rules from data that have been preprocessed by identification of functional dependency. The identification of functional dependency offers several advantages. It can lead to significant reductions in the computational load, to reduction in the number and complexity of derived rules and to the discovery of alternative solutions that would otherwise be ignored by some methods due to implicit or explicit procedural bias. Benchmark datasets from the UCI repository of machine learning databases are used in the testing. Experimental results indicate that by including functional dependency preprocessing performance of RULEX can be improved. Good rule quality is obtained by applying RULEX with functional dependency preprocessing when compared to symbolic rule extraction technique C4.5.

Certain facets of the gripping problem in robotics are discussed. A gripper with a single degree of freedom is considered, which consists of three equal-length fingers. The set of workpieces under consideration are modeled by prismatic and convex polyhedra. This leads to a planar-geometric formulation. The computational geometric problem of inscribing, in a given convex polygon, an equilateral triangle that is locally minimal, is addressed. Feasibility and the (computational) geometric construction of the solution are emphasized.>