Probabilistic Inference and Learning in a Connectionist Causal Network
نویسندگان
چکیده
The SHRUTI model demonstrates how a structured connectionist network can be used to encode relational causal knowledge and provide a basis for rapid inference. This paper explores the extent to which the evidential reasoning in SHRUTI can be viewed as probabilistic. An interpretation of the link weights is provided with which the results of spreading activation in the model accord well with probability theory. In addition, a simple local (Hebbian-like) learning mechanism is provided with which the described network structure and probabilistic link weights can be learned.
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تاریخ انتشار 2000