NDLI: General imprecise causal reasoning representations

Content Provider	IEEE Xplore Digital Library
Author	Mazlack, L.J.
Copyright Year	2007
Description	Author affiliation: Univ. of Cincinnati, Cincinnati (Mazlack, L.J.)
Abstract	Recognizing and developing causal relationships is essential for reasoning; it forms the basis for acting intelligently in the world. Causal knowledge provides a deep understanding of a system; and, the potential control over a system that comes from being able to predict action's consequences. Knowledge of at least some relationships is inherently imprecise. Causal complexes are groupings of smaller causal relations that can make up a larger-grained causal object. Usually, commonsense reasoning is more successful in reasoning about a few large-grained events than many finer-grained events. However, larger-grained causal objects are necessarily more imprecise. Often, a network represents a causal relationship with conditioned edges (probability, possibility, randomness, etc.). Various representational graphs and models can be used. Needed necessary descriptions include cycles, including mutual dependencies, both with non-cumulative effects and cumulative effects. Without cyclic descriptions, there would be an incomplete representation of the variety and wealth of causal constructions used in science as well as in everyday life. Directed Bayesian causal networks have received significant attention; they have a significant weakness in that they do not allow cycles; they have other significant restrictions, including Markoff independence conditions. This paper discusses: causality, complexes, granularity, imprecision, general and Bayesian causal models; the perspective reflects data mining goals.
Starting Page	480
Ending Page	485
File Size	551032
Page Count	6
File Format	PDF
ISBN	9781424409907
DOI	10.1109/ICSMC.2007.4413750
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-10-07
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Switches Bayesian methods Uncertainty Graphics Grain size Turning Control systems Data mining
Content Type	Text
Resource Type	Article

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