What is Gained from Past Learning
Abstract
We consider ways of enabling systems to apply previously learned information to novel situations so as to minimize the need for retraining. We show that theoretical limitations exist on the amount of information that can be transported from previous learning, and that robustness to changing environments depends on a delicate balance between the relations to be learned and the causal structure of the underlying model. We demonstrate by examples how this robustness can be quantified.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Mar 01, 2018
- Source ID
- 10.1515/jci-2018-0005
Entities
People
- Judea Pearl
Organizations
- Defense Advanced Research Projects Agency
- National Science Foundation
- Office of Naval Research
- University of California