Uncertainty in Artificial Intelligence
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Integrating Logical and Probabilistic Reasoning for Decision Making
John Breese, Edison Tse
Abstract:
We describe a representation and a set of inference methods that combine logic programming techniques with probabilistic network representations for uncertainty (influence diagrams). The techniques emphasize the dynamic construction and solution of probabilistic and decision-theoretic models for complex and uncertain domains. Given a query, a logical proof is produced if possible; if not, an influence diagram based on the query and the knowledge of the decision domain is produced and subsequently solved. A uniform declarative, first-order, knowledge representation is combined with a set of integrated inference procedures for logical, probabilistic, and decision-theoretic reasoning.
Keywords:
Pages: 355-362
PS Link:
PDF Link: /papers/87/p355-breese.pdf
BibTex:
@INPROCEEDINGS{Breese87,
AUTHOR = "John Breese and Edison Tse",
TITLE = "Integrating Logical and Probabilistic Reasoning for Decision Making",
BOOKTITLE = "Proceedings of the Third Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-87)",
PUBLISHER = "AUAI Press",
ADDRESS = "Corvallis, Oregon",
YEAR = "1987",
PAGES = "355--362"
}


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