Knowledge representation and inference in intelligent decision systems
Decision making in complex domains typically consists of a series of related decisions, each of which requires a different level of support and richness of representation. Rule-based expert systems for decision support have been successful for well-structured, well-understood decision situations. As uncertainty increases and the preferred solution depends on the specific beliefs and preferences of an individual decision maker, more-powerful techniques based on single-person decision theory can be brought to bear. This research focuses on alternative means of representing and using knowledge regarding decision situations in a computer-based decision aid. A unified characterization of knowledge and inference for logical, probabilistic, and decision-theoretic reasoning is developed for intelligent decision support over a wide spectrum of decision situations. A representation of a decision domain consists of structures for representing the decision choices, alternative possible states or outcomes which might occur, the relationships between choices made and outcomes realized, and preferences of the decision maker for the various outcomes. The components are captured by an extension to first-order predicate logic in which propositions are used to represent states, alternatives, and objectives.
- Research Organization:
- Stanford Univ., CA (USA)
- OSTI ID:
- 7245063
- Resource Relation:
- Other Information: Thesis (Ph. D.)
- Country of Publication:
- United States
- Language:
- English
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