6 papers
Synthesizing POMDP Policies: Sampling Meets Model-checking via Learning
Debraj Chakraborty, Anirban Majumdar, Prince Mathew +2
Partially Observable Markov Decision Processes (POMDPs) are the standard framework for decision-making under uncertainty. While sampling-based methods scale well, they lack formal…
Edit Distance of Finite-Valued Transducers
Prince Mathew, Saina Sunny
Transducers generalise automata by producing output word(s) for each input word, thereby defining a relation over words. A transducer is said to be finite-valued if, for every inpu…
Scalable Learning of One-Counter Automata via State-Merging Algorithms
Shibashis Guha, Anirban Majumdar, Prince Mathew +1
We propose One-counter Positive Negative Inference (OPNI), a passive learning algorithm for deterministic real-time one-counter automata (DROCA). Inspired by the RPNI algorithm for…
Learning real-time one-counter automata using polynomially many queries
Prince Mathew, Vincent Penelle, A. V. Sreejith
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…
Learning Deterministic One-Counter Automata in Polynomial Time
Prince Mathew, Vincent Penelle, A. V. Sreejith
We give an active learning algorithm for deterministic one-counter automata (DOCAs) where the learner can ask the teacher membership and minimal equivalence queries. The algorithm…
Equivalence of Deterministic Weighted Real-time One-Counter Automata
Prince Mathew, Vincent Penelle, Prakash Saivasan +1
This paper introduces deterministic weighted real-time one-counter automaton (DWROCA). A DWROCA is a deterministic real-time one-counter automaton whose transitions are assigned a…