Neural-network-based blackboard demon subsystems

摘要

We propose two neural network architectures involving feedforward and probabilistic neural network models to simulate the blackboard demon subsystem, which is responsible for triggering knowledge sources in accordance with the complex monitoring conditions on the hypotheses in the blackboard. Both architectures involve a preprocessor module to perform the transformation between symbolic hypotheses and numerical ones. They learn and record the triggering relationships between hypotheses and knowledge sources in the network links and perform better blackboard monitoring function than the traditional symbolic demon subsystems. In comparison, the probabilistic neural network-like architecture performs better when there is a possibility of using a centralized knowledge representation and when it only involves one-to-one or one-to-many mapping between hypotheses and triggering patterns. The feedforward architecture may be useful when a distributed knowledge representation is possible and when time requirements for training the architecture to learn the complex mappings are not too strict.