Core Concepts
Angluin’s PAC learning algorithm behaves well with random noise but poorly with structured noise, showing robustness against unstructured noise.
Abstract
The article analyzes the robustness of Angluin's L$^*$ algorithm in the presence of noise. It explores different types of noise introduced to DFAs and evaluates how the algorithm performs under these conditions. The study includes noisy outputs, noisy inputs, counter DFAs, and DFA with pathological behaviors. Experimental evaluations are conducted to determine the impact of word distribution on algorithm performance and the reduction of DFA size for efficiency. The results suggest that the algorithm is robust against random noise but struggles with structured noise.
Structure:
Introduction to Discrete-event Systems and Languages
Angluin’s L$^*$ Algorithm Overview
Types of Noise Introduced to DFAs:
Noisy Outputs
Noisy Inputs
Counter DFAs
DFA with Pathological Behaviors
Experimental Evaluation:
Impact of Word Distribution
Reduction of DFA Size
Random Languages vs Structured Languages
Stats
AngluinのL∗アルゴリズムはランダムノイズに対してうまく機能しますが、構造化されたノイズに対してはうまくいかないことを示しています。