In this paper we report some of the research endeavors we are embarking on as part of the Doctoral research of the first author. We have already completed an investigation of some of the existing algorithms in the areas of Network Routing and Traffic Engineering, and we propose superior algorithms that would adapt to the changes in the environment in which they operate. In this attempt, we inte...

Cellular Learning Automata (CLA) has been used in many fields of image processing such as noise elimination, smoothing, retrieval, fractionated and extraction of the content Characteristics of the images. The edge detection in images and methods if edge detection, have a great role in machine vision and cognizance systems. This method uses operands for analyzing images and digital image process...

We introduce session automata, an automata model to process data words, i.e., words over an infinite alphabet. Session automata support the notion of fresh data values, which are well suited for modeling protocols in which sessions using fresh values are of major interest, like in security protocols or ad-hoc networks. Session automata have an expressiveness partly extending, partly reducing th...

Markov games, as the generalization of Markov decision processes to the multi-agent case, have long been used for modeling multi-agent systems (MAS). The Markov game view of MAS is considered as a sequence of games having to be played by multiple players while each game belongs to a different state of the environment. In this paper, several learning automata based multiagent system algorithms f...

This paper describes an approach used in the 2012 Probabilistic Automata Learning Competition. The main goal of the competition was to obtain insights about which techniques and approaches work best for sequence learning based on different kinds of automata generating machines. This paper proposes the usage of n-gram models with variable length. Experiments show that, using the test sets provid...

This paper introduces a new algorithm for the induction of decision trees, based on adaptive techniques. One of the main feature of this algorithm is the application of automata theory to formalize the problem of decision tree induction and the use of a hybrid approach, which integrates both syntactical and statistical strategies. Some experimental results are also presented indicating that the...

Agents are software entities that act continuously and autonomously in a special environment. It is very essential for the agents to have the ability to learn how to act in the special environment for which they are designed to act in, to show reflexes to their environment actions, to choose their way and pursue it autonomously, and to be able to adapt and learn. In multi-agent systems, many in...

In this paper, we present a theoretical approach for the problem of learning multiplicity tree automata. These automata allows one to define functions which compute a number for each tree. They can be seen as a strict generalization of stochastic tree automata since they allow to define functions over any field K. A multiplicity automaton admits a support which is a non deterministic automaton....

One of the data mining techniques on the web is web usage mining. The purpose of this mining, extracting useful information from the user interaction data that can be obtained when using the web. By extracting this information, we can determine the relationship between the web pages and then we can do operations such as clustering and ranking web pages. The purpose of this article is to determi...

The Capacity Assignment problem focuses on finding the best possible set of capacities for the links that satisfy the traffic requirements in a prioritized network while minimizing the cost. Apart from the traditional methods for solving this NP-Hard problem, one new method that uses Learning Automata (LA) strategies has been recently reported. This method uses discretized learning automata [1]...