Perfect languages—a term coined by Esparza, Ganty, and Majumdar—are the classes of languages that are closed under Boolean operations and enjoy decidable emptiness problem. Perfect languages form the basis for decidable automata-theoretic model-checking for the respective class of models. Regular languages and visibly pushdown languages are paradigmatic examples of perfect languages. Alur and D...

L EARNING automata [1] have attracted a considerable interest in the last three decades. They are adaptive decision making devices that operate in unknown stochastic environments and progressively improve their performance via a learning process. They have been initially used by psychologists and biologists to describe the human behavior from both psychological and biological viewpoints. Learni...

This paper upgrades Regular Linear Temporal Logic (RLTL) with past operators and complementation. RLTL is a temporal logic that extends the expressive power of linear temporal logic (LTL) to all ω-regular languages. The syntax of RLTL consists of an algebraic signature from which expressions are built. In particular, RLTL does not need or expose fix-point binders (like linear time μ-calculus), ...

Learning algorithms are increasingly used in multiple disciplines of today's computer science reaching from robotics and formal verification to bioinformatics or natural language recognition. In this talk, we consider learning algorithms in the context of software development and verification. Two novel variants of learning algorithms will be presented. In the first part, we introduce NL* an al...

One of most attractive topics in grammatical inference is theoretically study on learnability of some classes of automata corresponding with defined formal languages. For last two decades a number of theoretical results have been reported and played as essential knowledge for applications in other fileds such as speech recognition and music-style recognition. In this paper, we consider the prob...

Markov games, as the generalization of Markov decision processes to the multi agent case, have long been used for modeling multi-agent systems. In this paper, several learning automata based multi-agent system algorithms for finding optimal policies in fully-cooperative Markov Games are proposed. In the proposed algorithms, Markov problem is described as a directed graph in which the nodes are ...

We are motivated by the following question: which data languages admit an active learning algorithm? This question was left open in previous work authors, and is particularly challenging for recognised nondeterministic automata. To answer it, we develop theory of residual nominal automata, a subclass prove that this class has canonical representatives, can always be constructed via finite numbe...