Abstract
In the probabilistic causal model described in this book, as well as in some others, probabilistic inference is combined with AI symbol processing methods for diagnostic problem-solving. In these models disorders and manifestations (and perhaps intermediate states) are connected by causal links associated with probabilities representing the strength of causal association. A hypothesis, consisting of zero or more disorders, with the highest posterior probability under the given set of manifestations (findings) is typically taken as the optimal problem solution. Conventional sequential search approaches in AI for solving diagnostic problems formulated in this fashion, such as the ones presented in Chapter 5 of this book and the search algorithm in NESTOR [Cooper84], suffer from combinatorial explosion when the number of possible disorders is large. This is because they potentially must compare the posterior probabilities of all or a notable portion of possible combinations of disorders. Pearl’s belief network model adopts a parallel revision method to find global optimal solutions for the special case of singly-connected causal networks within polynomial time of the network diameter [Pearl87]. However, as discussed in Section 5.4, for a non-singly-connected causal network, which is the case for most diagnostic problems, Pearl’s approach requires separate computation for each instantiation of the set of “cycle-cut” nodes, and thus still leads to combinatorial difficulty when this set of cycle-cut nodes is large. All of these problems raise the issue of whether the probabilistic causal model described in this book might be formulated as a highly parallel computation, i.e., as a “connectionist model”, so that combinatorial explosion can be avoided.
“Information is represented in a long-term memory as a network of the associations among concepts. Information is retrieved by spreading activation from concepts in working memory through the network structure.”
John R. Anderson
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 1990 Springer Science+Business Media New York
About this chapter
Cite this chapter
Peng, Y., Reggia, J.A. (1990). Parallel Processing for Diagnostic Problem-Solving. In: Abductive Inference Models for Diagnostic Problem-Solving. Symbolic Computation. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-8682-5_7
Download citation
DOI: https://doi.org/10.1007/978-1-4419-8682-5_7
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4612-6450-7
Online ISBN: 978-1-4419-8682-5
eBook Packages: Springer Book Archive