Implementation and Evaluation of a Gradient Descent-Trained Defensible Blackboard Architecture System
- Resource Type
- Working Paper
- Authors
- Milbrath, Jordan; Rivard, Jonathan; Straub, Jeremy
- Source
- Subject
- Computer Science - Artificial Intelligence
- Language
A variety of forms of artificial intelligence systems have been developed. Two well-known techniques are neural networks and rule-fact expert systems. The former can be trained from presented data while the latter is typically developed by human domain experts. A combined implementation that uses gradient descent to train a rule-fact expert system has been previously proposed. A related system type, the Blackboard Architecture, adds an actualization capability to expert systems. This paper proposes and evaluates the incorporation of a defensible-style gradient descent training capability into the Blackboard Architecture. It also introduces the use of activation functions for defensible artificial intelligence systems and implements and evaluates a new best path-based training algorithm.