2 research outputs found
Integrating Logical and Probabilistic Reasoning for Decision Making
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.Comment: Appears in Proceedings of the Third Conference on Uncertainty in
Artificial Intelligence (UAI1987
Representation Requirements for Supporting Decision Model Formulation
This paper outlines a methodology for analyzing the representational support
for knowledge-based decision-modeling in a broad domain. A relevant set of
inference patterns and knowledge types are identified. By comparing the
analysis results to existing representations, some insights are gained into a
design approach for integrating categorical and uncertain knowledge in a
context sensitive manner.Comment: Appears in Proceedings of the Seventh Conference on Uncertainty in
Artificial Intelligence (UAI1991