This paper proposes a distributed learning mechanism that learns patterns from distributed datasets. The complex and dynamic settings of grid environments requires supporting systems to be of a more sophisticated level. Contemporary tools lack the ability to relate and infer events. We developed an information system, based on collaborative agents, that supports system administrators in monitor...

In ad hoc networks, the performance is significantly degraded as the size of the network grows. The network clustering is a method by which the nodes are hierarchically organized on the basis of the proximity and thus the scalability problem is alleviated. Finding the weakly connected dominating set (WCDS) is a well-known approach, proposed for clustering the wireless ad hoc networks. Finding t...

Learning automata act in a stochastic environment and are able to update their action probabilities considering the inputs from their environment, so optimizing their functionality as a result. In this paper, the goal is to investigate and evaluate the application of learning automata to cooperation in multiagent systems, using soccer simulation server as a test bed. We have also evaluated our ...

Active learning of register automata infers extended finite state machines (EFSMs) with registers for storing values from a possibly infinite domain, and transition guards that compare data parameters to registers. In this paper, we present RALib, an extension to the LearnLib framework for automata learning. RALib provides an extensible implementation of active learning of register automata, to...

We apply the theory of asynchronous automata to the synthesis problem of closed distributed systems. We use safe asynchronous automata as implementation model, and characterise the languages they accept. We analyze the complexity of the synthesis problem in our framework. Theorems by Zielonka and Morin are then used to develop and implement a synthesis algorithm. Finally, we apply the developed...

In electronic commerce markets, agents often should acquire multiple resources to fulfil a high-level task. In order to attain such resources they need to compete with each other. In multi-agent environments, in which competition is involved, negotiation would be an interaction between agents in order to reach an agreement on resource allocation and to be coordinated with each other. In recent ...

Automata models of learning systems introduced in the 1960s were popularized as learning automata (LA) in a survey paper by Narendra and Thathachar (1974). Since then, there have been many fundamental advances in the theory as well as applications of these learning models. In the past few years, the structure of LA, has been modified in several directions to suit different applications. Concept...

We investigate the decidability of the emptiness problem for three classes of distributed automata. These devices operate on finite directed graphs, acting as networks of identical finite-state machines that communicate in an infinite sequence of synchronous rounds. The problem is shown to be decidable in LOGSPACE for a class of forgetful automata, where the nodes see the messages received from...

The problem of task scheduling in distributed systems is known as an NP-hard problem, and methods based on heuristic or metaheuristic search have been proposed to obtain optimal and suboptimal solutions. The scheduling problem is a key factor for distributed systems to gain better performance. In this paper, an efficient method based on memetic algorithm is developed to solve the problem of dis...