Weizmann Logo
Electronic Colloquium on Computational Complexity

Under the auspices of the Computational Complexity Foundation (CCF)

Login | Register | Classic Style



TR17-046 | 8th March 2017 17:45

Learning Residual Alternating Automata



Residuality plays an essential role for learning finite automata.
While residual deterministic and nondeterministic
automata have been understood quite well, fundamental
questions concerning alternating automata (AFA) remain open.
Recently, Angluin, Eisenstat, and Fisman have initiated
a systematic study of residual AFAs and proposed an algorithm called AL*
-an extension of the popular L* algorithm - to learn AFAs.
Based on computer experiments they conjectured
that AL* produces residual AFAs, but have not been able to give a proof.
In this paper we disprove this conjecture by constructing a counterexample.
As our main positive result we design an efficient
learning algorithm, named AL**, and give a proof
that it outputs residual AFAs only.
In addition, we investigate the succinctness of these different FA types in more detail.

ISSN 1433-8092 | Imprint