Enhanced qualitative probabilistic networks for resolving trade-offs

• Published in: Artificial intelligence Vol. 172; no. 12; pp. 1470 - 1494
• Main Authors: Renooij, Silja, van der Gaag, Linda C.
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier B.V 01-08-2008; Elsevier
• Subjects: Applied sciences; Artificial intelligence; Computer science; control theory; systems; Exact sciences and technology; Learning and adaptive systems; Probabilistic reasoning; Qualitative reasoning; Trade-off resolution; Probabilistic reasoning; Qualitative reasoning; Trade-off resolution; Probabilistic approach; Information system; Inference; Probabilistic logic; Distributed system; Abstraction; Modeling; Detail level; Stochastic programming; Probability learning; Artificial intelligence
• ISSN: 0004-3702; 1872-7921
• DOI: 10.1016/j.artint.2008.04.001

Qualitative probabilistic networks were designed to overcome, to at least some extent, the quantification problem known to probabilistic networks. Qualitative networks abstract from the numerical probabilities of their quantitative counterparts by using signs to summarise the probabilistic influences between their variables. One of the major drawbacks of these qualitative abstractions, however, is the coarse level of representation detail that does not provide for indicating strengths of influences. As a result, the trade-offs modelled in a network remain unresolved upon inference. We present an enhanced formalism of qualitative probabilistic networks to provide for a finer level of representation detail. An enhanced qualitative probabilistic network differs from a basic qualitative network in that it distinguishes between strong and weak influences. Now, if a strong influence is combined, upon inference, with a conflicting weak influence, the sign of the net influence may be readily determined. Enhanced qualitative networks are purely qualitative in nature, as basic qualitative networks are, yet allow for resolving some trade-offs upon inference.