Tuesday, May 16, 2006
- exploit regularities in the environments
- generalizations selective, pragmatic, and subject to exceptions
- learning must be incremental and coupled to performance
- rules as tentative hypotheses, not consistent assertions
Classifiers evolved from those early days with a strong emphasis on cognition. Reinforcement learning came along (with roots out of UofM) and gave important formalism.
Lashon wonders if we are solving problems that take advantage of what classifier systems do well and how to describe the architecture of classifier systems that will. He contrasted LCSs with the success of SOAR in solving problems and becoming more widely accepted.
After break Martin Butz is up next.