Learnability of Probabilistic Automata via Oracles

Efficient learnability using the state merging algorithm is known for a subclass of probabilistic automata termed µ-distinguishable. In this paper, we prove that state merging algorithms can be extended to efficiently learn a larger class of automata. In particular, we show learnability of a subclass which we call µ2-distinguishable. Using an analog of the Myhill-Nerode theorem for probabilistic automata, we analyze µ-distinguishability and generalize it to µp-distinguishability. By combining new results from property testing with the state merging algorithm we obtain KL-PAC learnability of the new automata class. Our research hints at closer connections between property testing and probabilistic automata learning and leads to very interesting open problems.

• Type: Conference
• Year: 2005
• Where: ALT
• Authors: Omri Guttman, S. V. N. Vishwanathan, Robert C. Williamson
• Keywords: ALT 2005, Machine Learning, Probabilistic Automata, Probabilistic Automata Learning, State Merging Algorithm