Abstract
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.
Abstract (translated)
人工智能系统发展了许多形式。两种著名的技术是神经网络和规则专家系统。前一种可以从给出的数据中进行训练，而后者通常是由人类领域专家开发的。 previously proposed 是一种使用梯度下降来训练规则专家系统的联合实现。 一个相关的系统类型，Blackboard Architecture，增加了专家系统的实现能力。 本文提出了并评估将可防御的梯度下降训练功能融入Blackboard架构中的效果。 它还介绍了使用激活函数作为可防御的人工智能系统的实现，并实现了和评估了一种基于新最佳路径的新颖训练算法。
URL
https://arxiv.org/abs/2404.11714
