The ability to predict linkages among data objects is central to many data mining tasks, such as product recommendation and social network analysis. Substantial literature has been devoted to the link prediction problem either as an implicitly embedded problem in specific applications or as a generic data mining task. This literature has mostly adopted a static graph representation where a snap...

We propose to harness Angluin’s L∗ algorithm for learning a deterministic finite automaton that describes the possible scenarios under which a given program error occurs. The alphabet of this automaton is given by the user (for instance, a subset of the function call sites or branches), and hence the automaton describes a user-defined abstraction of those scenarios. More generally, the same tec...

we introduce a two generated weakly branch contracting automaton group $g$ which is generated by a two state automaton on a three letter alphabet. using its branch structure and the finiteness nature of a sequence of its factor groups we compute the order of some of these factors. furthermore some algebraic properties of $g$ are detected .

Reinforcement schemes represent the basis of the learning process for stochastic learning automata, generating their learning behavior. An automaton using a reinforcement scheme can decide the best action, based on past actions and environment responses. The aim of this paper is to introduce a new reinforcement scheme for stochastic learning automata. We test our schema and compare with other n...

Learning regular languages is a branch of machine learning, which has been proved useful in many areas, including artificial intelligence, neural networks, data mining, verification, etc. On the other hand, interest in languages defined over large and infinite alphabets has increased in recent years. Although many theories and properties generalize well from the finite case, learning such langu...

This paper presents a modification of Particle Swarm Optimization (PSO) technique based on cooperative behavior of swarms and learning ability of an automaton. The approach is called Cooperative Particle Swarm Optimization based on Learning Automata (CPSOLA). The CPSOLA algorithm utilizes three layers of cooperation which are intra swarm, inter swarm and inter population. There are two active p...

We present the STack ARchitecture (STAR) automaton. It is a fixed structure, multiaction, reward-penalty learning automaton, characterized by a star-shaped state transition diagram. Each branch of the star contains D states associated with a particular action. The branches are connected to a central "neutral" state. The most general version of STAR involves probabilistic state transitions in re...

QoS in the area of wireless sensor networks (WSNs) can be measured based on different criteria: Network lifetime, network coverage, number of active nodes and energy consumption. In this paper, we propose a scheduling solution for activating and deactivating sensor nodes in the network in such a way that all of the above criteria are considered. In the proposed solution, which is based on cellu...

We have trained networks of E II units with short-range connections to simulate simple cellular automata that exhibit complex or chaotic behaviour. Three levels of learning are possible (in decreasing order of difficulty): learning the underlying automaton rule, learning asymptotic dynamical behaviour, and learning to extrapolate the training history. The levels of learning achieved with and wi...

Designing an intelligent search method in peer-to-peer networks will significantly affect efficiency of the network taking into account sending a search query to nodes which have more probably stored the desired object. Machine learning techniques such as learning automaton can be used as an appropriate tool for this purpose. This paper tries to present a search method based on the learning aut...