Minimum vertex cover problem is an NP-Hard problem with the aim of finding minimum number of vertices to cover graph. In this paper, a learning automaton based algorithm is proposed to find minimum vertex cover in graph. In the proposed algorithm, each vertex of graph is equipped with a learning automaton that has two actions in the candidate or noncandidate of the corresponding vertex cover se...

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...

The term "Grid" has become common parlance among parallel and distributed computer scientists to denote a middleware infrastructure for wide-area scientific and engineering computing. Information services are a vital part of any Grid software infrastructure, providing fundamental mechanisms for discovery and monitoring and thus for planning and adapting application behavior. Grid Information Se...

In this paper, we introduce a Monte Carlo simulation method based on distributed learning automata for solving the stochastic shortest path problem. We give an iterative stochastic algorithm that find the minimum expected value of set of random variables representing cost of paths in a stochastic graph by taking sufficient samples from them. In the given algorithm, the sample size is determined...

Cellular learning automata is a combination of cellular automata and learning automata. The synchronous version of cellular learning automata in which all learning automata in different cells are activated synchronously, has found many applications. In some applications a type of cellular learning automata in which learning automata in different cells are activated asynchronously (asynchronous ...

A new model for distributed decision making, &tributed game automata, focuses on how communication affects the quality of decisions. The goal is to limit communication between decision makers such that overhead costs are reduced but good decisions still result. Learning automata play repeated games with payas quadifying the performance in a distributed application. Each automaton’s view of the ...

Highly dynamic and heterogeneous distributed systems are challenging today’s middleware technologies. Existing middleware paradigms are unable to deliver on their most central promise, which is offering interoperability. In this paper, we argue for the need to dynamically synthesise distributed system infrastructures according to the current operating environment, thereby generating “Emergent M...

Abstract We extend the $$\mathtt {L}^{\!\star }$$ L⋆ algorithm to learn bimonoids recognising pomset languages. then identify a class of automata that accepts precisely languages recognised by and show how convert between automata.

The cellular learning automata, which is a combination of cellular automata, and learning automata, is a new recently introduced model. This model is superior to cellular automata because of its ability to learn and is also superior to a single learning automaton because it is a collection of learning automata which can interact with each other. The basic idea of cellular learning automata, whi...