Induction, of and by Probability
This work addresses noise management and incremental learning in heuristic search, with emerging principles that are generally applicable, though it appears incremental in nature.
The paper tackles the problem of generalization learning or induction by introducing probabilistic learning systems (PLS) that guide both task performance and learning through probability, achieving unique effectiveness and efficiency.
This paper examines some methods and ideas underlying the author's successful probabilistic learning systems(PLS), which have proven uniquely effective and efficient in generalization learning or induction. While the emerging principles are generally applicable, this paper illustrates them in heuristic search, which demands noise management and incremental learning. In our approach, both task performance and learning are guided by probability. Probabilities are incrementally normalized and revised, and their errors are located and corrected.