Review and assessment of prior work on and future directions for gradient descent-trained expert systems

This paper reviews prior work demonstrating the efficacy of a new artificial intelligence technique which is based on optimizing expert systems' rule-fact networks. Systems of this type can learn from presented data and operations; however, they cannot learn any changes that 'jump out of' the human-created or validated pathways, ensuring that they don't learn invalid or non-causal associations. This paper presents a review and assessment of the functionality provided by the base gradient descent-trained expert system, the functionality provided by an enhancement that facilitates automated network development, and several other enhancements. The benefits of each system variant are discussed.