Abstract Automata learning is a technique to infer an automaton model of black-box system via queries the system. In recent years it has found widespread use both in industry and academia, as enables formal verification when no available or too complex create one manually. this paper we consider problem individual components synchronous system, assuming can only query whole We introduce composi...

—Due to its importance as a model for several software and hardware applications, automata theory is a core topic in computer science and engineering education. But because of its abstract mathematical nature automata is used to be taught by traditional lecture-driven style. Virtual reality and web-based learning can provide automata learners with a unique experience such as interactive learnin...

Quantitative languages are extension of Boolean languages that assign to each word a real number. With quantitative languages, systems and specifications can be formalized more accurately. For example, a system may use a varying amount of some resource (e.g., memory consumption, or power consumption) depending on its behavior, and a specification may assign a maximal amount of available resourc...

In this paper a data miner based on the learning automata is proposed and is called LA-miner. The LA-miner extracts classification rules from data sets automatically. The proposed algorithm is established based on the function optimization using learning automata. The experimental results on three benchmarks indicate that the performance of the proposed LA-miner is comparable with (sometimes be...

Cellular learning automata (CLA) is a distributed computational model which was introduced in the last decade. This model combines the computational power of the cellular automata with the learning power of the learning automata. Cellular learning automata is composed from a lattice of cells working together to accomplish their computational task; in which each cell is equipped with some learni...

Learning automata (LA) were recently shown to be valuable tools for designing Multi-Agent Reinforcement Learning algorithms and are able to control the stochastic games. In this paper, the concepts of stigmergy and entropy are imported into learning automata based multi-agent systems with the purpose of providing a simple framework for interaction and coordination in multi-agent systems and spe...

In this paper, we establish the learnability of simple deterministic finitememory automata via membership and equivalence queries. Sim ple deterministic fmitememory automata are a subclass of finitememory automata introduced by Kaminski and Francez (1994) as a model gen eralizing finite automata to infinite alphabets. Continuously, Sakamoto and Ikeda investigated several decision problems for f...