Implementation and Evaluation of a Gradient Descent-Trained Defensible Blackboard Architecture System
<|reference_start|>Implementation and Evaluation of a Gradient Descent-Trained Defensible Blackboard Architecture System: 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.<|reference_end|>
@article{milbrath2024implementation,
    title={Implementation and Evaluation of a Gradient Descent-Trained Defensible
  Blackboard Architecture System},
    author={Jordan Milbrath, Jonathan Rivard, Jeremy Straub},
    journal={arXiv preprint arXiv:2404.11714},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.11714},
    primaryClass={cs.AI}
}