Title :
A multilayer perceptron solution to the match phase problem in rule-based artificial intelligence systems
Author :
Sartori, Michael A. ; Passino, Kevin M. ; Antsaklis, Panos J.
Author_Institution :
David Taylor Res. Center, Bethesda, MD, USA
fDate :
6/1/1992 12:00:00 AM
Abstract :
In rule-based artificial intelligence (AI) planning, expert, and learning systems, it is often the case that the left-hand-sides of the rules must be repeatedly compared to the contents of some working memory. Normally, the intent is to determine which rules are relevant to the current situation (i.e., to find the conflict set). A technique using a multilayer perceptron to solve the match phase problem for rule-based AI systems is presented. A syntax for premise formulas (i.e., the left-hand-sides of the rules) is defined, and working memory is specified. From this, it is shown how to construct a multilayer perceptron that finds all of the rules which can be executed for the current situation in working memory. The complexity of the constructed multilayer perceptron is derived in terms of the maximum number of nodes and the required number of layers. A method for reducing the number of layers to at most three is presented
Keywords :
artificial intelligence; expert systems; learning systems; neural nets; expert systems; learning systems; match phase problem; multilayer perceptron solution; planning; rule-based artificial intelligence systems; working memory; Artificial intelligence; Artificial neural networks; Engines; Expert systems; Knowledge based systems; Learning systems; Multi-layer neural network; Multilayer perceptrons; Neural networks; Production systems;
Journal_Title :
Knowledge and Data Engineering, IEEE Transactions on
