Bayesian Network (BN) is a probabilistic graphical model which describes the joint probability distribution over a set of random variables. Finding an optimal network structure based on an available training dataset is one of the most important challenges in the field of BNs. Since the problem of searching the optimal BN structure belongs to the class of NP-hard problems, typically greedy algor...

Nearly all algorithms for learning an unknown regular language, in particular the popular L∗ algorithm, yield deterministic finite automata. It was recently shown that the ideas of L∗ can be extended to yield non-deterministic automata, and that the respective learning algorithm, NL∗, outperforms L∗ on randomly generated regular expressions. We conjectured that this is due to the existential na...

Ant colony algorithms are a group of heuristic optimization algorithms that have been inspired by ants foraging for food. In these algorithms there are some agents, the ants, that for finding the suitable solution, search the solution space. On the other hand, Learning Automata is an abstract model that can do finite actions. Each selected action is evaluated by a random environment and the env...

In this paper, a new structure for cooperative learning automata called extended learning automata (eDLA) is introduced. Based on the new structure, an iterative randomized heuristic algorithm using sampling is proposed for finding an optimal subgraph in a stochastic edge-weighted graph. Stochastic graphs are graphs in which the weights of edges have an unknown probability distribution. The pro...

Clustering sensor nodes is one of the effective ways which extends the life time of sensor networks. In this paper, a learning automata-based clustering algorithm (LACA) for a sensor network is proposed. The proposed algorithm is completely distributed and independent of network size and topology. The performance of the LACA via the computer simulation was evaluated and compared with other clus...