Neural network based heuristics for transitive closure derivation

Summary form only given. A neural autoassociator is proposed for transitive closure derivation. A number of neural network based heuristics are identified which lend themselves to easy solutions in transitive closure derivations. Several parallel distributed processing algorithms are developed in a stepwise manner based on the heuristics and a few theorems are proved which support the correctness and time efficiency of different algorithms in different cases. It is shown that, in performance a closure derivation, the neural network model self-organizes its connectivity matrix into a transitive closure using the heuristics opportunistically at run time with little overhead. In addition to practical applications, the proposed approach identifies a new category of heuristics called neural network based heuristics. It is suggested and demonstrated that this category of heuristics may gain high efficiency, which has never been possible by sequential or traditional parallel processing.<>