Generating rules with predicates, terms and variables from the pruned neural networks.
Artificial neural networks (ANN) have demonstrated good predictive performance in a wide range of applications. They are, however, not considered sufficient for knowledge representation because of their inability to represent the reasoning process succinctly. This paper proposes a novel methodology Gyan that represents the knowledge of a trained network in the form of restricted first-order predicate rules. The empirical results demonstrate that an equivalent symbolic interpretation in the form of rules with predicates, terms and variables can be derived describing the overall behaviour of the trained ANN with improved comprehensibility while maintaining the accuracy and fidelity of the propositional rules.
Nayak
Richi
R
Faculty of Information Technology, Queensland University of Technology, S 537 Gardens Point, GPO Box 2434, Brisbane, QLD 4001, Australia. r.nayak@qut.edu.au
D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D003658:Decision Making, Computer-Assisted; D055641:Mathematical Concepts; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated; D013561:Symbolism