In this paper, we introduce a novel method for active learning of deterministic real-time one-counter automata (DROCA). The existing techniques for learning DROCA rely on observing the behaviour of the DROCA up to exponentially large counter-values. Our algorithm eliminates this need and requires only a polynomial number of queries. Additionally, our method differs from existing techniques as we learn a minimal counter-synchronous DROCA, resulting in much smaller counter-examples on equivalence queries. Learning a minimal counter-synchronous automaton cannot be done in polynomial time unless $P = NP$, even in the case of visibly one-counter automata. We use a SAT solver to overcome this difficulty. The solver is used to compute a minimal separating DFA from a given set of positive and negative samples. We prove that the equivalence of two counter-synchronous DROCAs can be checked significantly faster than that of general DROCAs. For visibly one-counter automata, we have discovered an even faster algorithm for equivalence checking. We implemented the proposed learning algorithm and tested it on randomly generated DROCAs. Our evaluations show that the proposed method outperforms the existing techniques on the test set.
I am pursuing my PhD in Theoretical Computer Science under the guidance of Dr. Sreejith A V in the School of Mathematics and Computer Science at Indian Institute of Technology, Goa. Currently, research focused on finding better algorithms for active learning of one-counter automata. A prior research endeavour established that the problems of Equivalence, Regularity, and Covering of Weighted-one-counter automata (over fields) with counter determinacy can be solved in polynomial time.