Opportunistic reasoning
Opportunistic reasoning is a method of selecting a suitable logical inference strategy within artificial intelligence applications.
Specific reasoning methods may be used to draw conclusions from a set of given facts in a knowledge base, e.g. forward chaining versus backward chaining. However, in opportunistic reasoning, pieces of knowledge may be applied either forward or backward, at the "most opportune time".[1]
An opportunistic reasoning system may combine elements of both forward and backward reasoning. It is useful when the number of possible inferences is very large and the reasoning system must be responsive to new data that may become known.[2]
Opportunistic reasoning has been used in applications such as blackboard systems and medical applications.[3]
References
[edit]- Marin D. Simina et al. "Opportunistic Reasoning: A Design Perspective" in Proceedings of the Seventeenth Annual Conference of Cognitive Science edited by Johanna D. Moore, 1995 ISBN 0-8058-2159-7, page 78
Notes
[edit]- ^ Blackboard systems by I. Craig, 1995 ISBN 1-56750-029-3 page 84
- ^ Fundamentals of expert systems technology by Samuel J. Biondo 1990 ISBN 0-89391-701-X page 69
- ^ Roy Turner, "Opportunistic use of schemata" in Tenth Annual Conference of the Cognitive Science Society 1988 ISBN 0-8058-0436-6 page 165
 | This artificial intelligence-related article is a stub. You can help Wikipedia by expanding it. |