Programming expert systems in OPS5: an introduction to rule-based programming
Programming expert systems in OPS5: an introduction to rule-based programming
Parallel algorithms and architectures for rule-based systems
ISCA '86 Proceedings of the 13th annual international symposium on Computer architecture
Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Switching and Finite Automata Theory: Computer Science Series
Switching and Finite Automata Theory: Computer Science Series
The Design and Analysis of Computer Algorithms
The Design and Analysis of Computer Algorithms
On the efficient implementation of production systems.
On the efficient implementation of production systems.
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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.