Source selection for analogical reasoning an empirical approach
Conference
·
OSTI ID:430730
- Sandia National Labs., Albuquerque, NM (United States)
- Univ. of New Mexico, Albuquerque, NM (United States)
The effectiveness of an analogical reasoner depends upon its ability to select a relevant analogical source. In many problem domains, however, too little is known about target problems to support effective source selection. This paper describes the design and evaluation of SCAVENGER, an analogical reasoner that applies two techniques to this problem: (1) An assumption-based approach to matching that allows properties of candidate sources to match unknown target properties in the absence of evidence to the contrary. (2) The use of empirical learning to improve memory organization based on problem solving experience.
- OSTI ID:
- 430730
- Report Number(s):
- CONF-960876-; TRN: 96:006521-0105
- Resource Relation:
- Conference: 13. National conference on artifical intelligence and the 8. Innovative applications of artificial intelligence conference, Portland, OR (United States), 4-8 Aug 1996; Other Information: PBD: 1996; Related Information: Is Part Of Proceedings of the thirteenth national conference on artificial intelligence and the eighth innovative applications of artificial intelligence conference. Volume 1 and 2; PB: 1626 p.
- Country of Publication:
- United States
- Language:
- English
Similar Records
A Mixed-Method Design Approach for Empirically Based Selection of Unbiased Data Annotators
Parameterized Micro-benchmarking: An Auto-tuning Approach for Complex Applications
Empirical approaches to extrapolating among wildlife species
Conference
·
Sun Aug 01 00:00:00 EDT 2021
·
OSTI ID:430730
+2 more
Parameterized Micro-benchmarking: An Auto-tuning Approach for Complex Applications
Conference
·
Tue May 15 00:00:00 EDT 2012
·
OSTI ID:430730
Empirical approaches to extrapolating among wildlife species
Conference
·
Sun Dec 31 00:00:00 EST 1995
·
OSTI ID:430730