...for learning transition dynamics...in the KWIK framework.
The above is part of a paper's conclusion - and I don't really seem to understand what the KWIK framework is. In the details of the paper, is a brief highlight of the KWIK conditions for a learning algorithm, which go as follows (I paraphrase):
- All predictions must be accurate (assuming a valid hypotheses class)
- However the learning algorithm may also return $\perp$, which indicates that it cannot yet predict the output for this input.
A quick Google search brought me to this paper from ICML 2008, but it is a little difficult to comprehend without a detailed read.
Could someone please help me understand what the KWIK framework is, and what implication does it have for a learning algorithm to satisfy KWIK conditions? An explanation that starts at simple and goes to fairly advanced discussions is appreciated.