| | |
Summary: 290 lEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, VOL. 4, NO. 3, JUNE 1992
Concise Papers
A Multilayer Perceptron Solution to the Match Phase
Problem in Rule-Based Artificial Intelligence Systems
Michael A. Sartori, Kevin M. Passino, and Panos J. Antsaklis,
Abstruct- 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"). The traditional
approach to solve such a "match phase problem" for production systems
is to use the Rete Match Algorithm. Here, a new technique using a
multilayer perceptron, a particular artificial neural network model, is
presented to solve the match phase problem for rule-based AI systems.
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 complexityof
the constructedmultilayerperceptmnis derivedin terms of the maximum
number of nodes and the required number of layers. A method for
|
