KSL-88-63 + redirect page
An Analysis of Categorical and Quantitative Methods for Planning under Uncertainty + Has identifier
An Analysis of Categorical and Quantitative Methods for Planning under Uncertainty + Ksl tr id
An Analysis of Categorical and Quantitative Methods for Planning under Uncertainty + Number
| An Analysis of Categorical and Quantitative Methods for Planning under Uncertainty |
Bibtype
techreport
Has publishing details
1988
Has title
An Analysis of Categorical and Quantitative Methods for Planning under Uncertainty
Has where published
KSL-88-63
Has year
1988
Title
An Analysis of Categorical and Quantitative Methods for Planning under Uncertainty
Year
1988
Abstract
Decision theory and logical reasoning are … Decision theory and logical reasoning are both methods for representing and solving medical decision problems. We analyze the usefulness of these two approaches to medical therapy planning by establishing a simple correspondence between decision theory and non-monotonic logic, a formalization of categorical logical reasoning. The analysis indicates that categorical approaches to planning can be viewed as comprising two decision-theoretic concepts: probabilities (degrees of belief in planning hypotheses) and utilities (degrees of desirability of planning outcomes). We present and discuss examples of the following lessons from this decision-theoretic view of categorical (nonmonotonic) reasoning: (1) Decision theory and artificial intelligence techniques are intended to solve different components of the planning problem. (2) When considered in the context of planning under uncertainty, nonmonotonic logics do not retain the domain-independent characteristics of classical logical reasoning for planning under certainty. (3) Because certain nonmonotonic programming paradigms (e.g., frame-based inheritance, rule-based planning, protocol-based reminders) are inherently problem-specific, they may be inappropriate to employ in the solution of certain types of planning problems. We discuss how these conclusions affect several current medical informatics research issues, including the construction of "very large" medical knowledge bases. n of "very large" medical knowledge bases.
Author
Curtis Langlotz and Edward H. Shortliffe +
Has author
Curtis Langlotz and Edward H. Shortliffe +
Has identifier
An Analysis of Categorical and Quantitative Methods for Planning under Uncertainty +
Institution
Knowledge Systems, AI Laboratory +
Ksl tr id
An Analysis of Categorical and Quantitative Methods for Planning under Uncertainty +
Number
An Analysis of Categorical and Quantitative Methods for Planning under Uncertainty +
Process note
YES +
Categories KSL Technical Report +, Publication +, Technical Report +
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