  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.
