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...

In this paper, we show how to fully automatically infer semantic interfaces of data structures on the basis of systematic testing. Our semantic interfaces are a generalized form of Register Automata (RA), comprising parameterized input and output, allowing to model controland data-flow in component interfaces concisely. Algorithmic key to the automated synthesis of these semantic interfaces is ...

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...

A Learning Automaton is an automaton that interacts with a random environment, having as its goal the task of learning the optimal action based on its acquired experience. Many learning automata have been proposed, with the class of Estimator Algorithms being among the fastest ones. Thathachar and Sastry [24], through the Pursuit Algorithm, introduced the concept of learning algorithms. Their a...

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...

We present a generalized disambiguation algorithm of finite state automata, and show a proof of its correctness. This algorithm can remove ambiguities of finite state and tree automata. Our proposed algorithm can make finite state and tree automata more efficient to use in many applications.