Uncertainty in Artificial Intelligence
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Combining Symbolic and Numeric Approaches to Uncertainty Management
Bruce D'Ambrosio
A complete approach to reasoning under uncertainty requires support for incremental and interactive formulation and revision of, as well as reasoning with, models of the problem domain capable of representing our uncertainty. We present a hybrid reasoning scheme which combines symbolic and numeric methods for uncertainty management to provide efficient and effective support for each of these tasks. The hybrid is based on symbolic techniques adapted from Assumption-based Truth Maintenance systems (ATMS), combined with numeric methods adapted from the Dempster/Shafer theory of evidence, as extended in Baldwin's Support Logic Programming system. The hybridization is achieved by viewing an ATMS as a symbolic algebra system for uncertainty calculations. This technique has several major advantages over conventional methods for performing inference with numeric certainty estimates in addition to the ability to dynamically determine hypothesis spaces, including improved management of dependent and partially independent evidence, faster run-time evaluation of propositional certainties, the ability to query the certainty value of a proposition from multiple perspectives, and the ability to incrementally extend or revise domain models.
Keywords: ATMS, Hybrid Reasoning, Inference
Pages: 386-393
PS Link:
PDF Link: /papers/87/p386-d_ambrosio.pdf
AUTHOR = "Bruce D'Ambrosio ",
TITLE = "Combining Symbolic and Numeric Approaches to Uncertainty Management",
BOOKTITLE = "Proceedings of the Third Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-87)",
ADDRESS = "Corvallis, Oregon",
YEAR = "1987",
PAGES = "386--393"

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