Abstract
This article discusses MUM, a knowledge-based consultation system designed to manage the uncertainty inherent in medical diagnosis. The primary task of the system is to plan which questions, tests, and treatments to order at each point in a consultation, given current uncertain knowledge about the patient's disease. Managing uncertainty means planning what to do when uncertain; the authors suggest that this ability must be designed in, not added on, to the architectures of knowledge-based systems. MUM is based on one such architecture, implemented as a generalized inference network and planner. The network facilitates local combination of evidence; the planner "reads" the state of the network after each piece of evidence integrated, then decides which evidence to seek on the basis of its several goals.
Original language | English (US) |
---|---|
Pages (from-to) | 103-116 |
Number of pages | 14 |
Journal | International Journal of Approximate Reasoning |
Volume | 1 |
Issue number | 1 |
DOIs | |
State | Published - Jan 1987 |
Keywords
- automated reasoning
- control
- diagnosis
- planning
- reasoning about uncertainty
ASJC Scopus subject areas
- Software
- Theoretical Computer Science
- Artificial Intelligence
- Applied Mathematics