Knowledge representation and inference in intelligent decision systems

Thesis/Dissertation · Thu Jan 01 04:00:00 EST 1987

OSTI ID:7245063

Breese, J S

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
• Country of Publication: United States
• Language: English

Related Subjects

99 GENERAL AND MISCELLANEOUS
990210* -- Supercomputers-- (1987-1989)
ARTIFICIAL INTELLIGENCE
COMPUTERS
DATA COVARIANCES
DECISION MAKING
EXPERT SYSTEMS
KNOWLEDGE BASE
PROBABILISTIC ESTIMATION