With the purpose of unifying a number of approaches to energy problems found in the literature, we introduce generalized energy automata. These are finite automata whose edges are labeled with energy functions that define how energy levels evolve during transitions. Uncovering a close connection between energy problems and reachability and Büchi acceptance for semiring-weighted automata, we sho...

The application of statistical methods to natural language processing has been remarkably successful over the past two decades. But, to deal with recent problems arising in this field, machine learning techniques must be generalized to deal with uncertain data, or datasets whose elements are distributions over sequences, such as weighted automata. This paper reviews a number of recent results r...

We present a tool that transforms nondeterministic ω-automata to semi-deterministic ω-automata. The tool Seminator accepts transition-based generalized Büchi automata (TGBA) as an input and produces automata with two kinds of semi-determinism. The implemented procedure performs degeneralization and semi-determinization simultaneously and employs several other optimizations. We experimentally ev...

The complementation problem for nondeterministic automata on infinite words has numerous applications in formal verification. In particular, the language-containment problem, to which many verification problems are reduced, involves complementation. Traditional optimal complementation constructions are quite complicated and have not been implemented. Recently, we have developed an analysis tech...

Received 17 Revised Cellular learning automata is a combination of learning automata and cellular automata. 19 This model is superior to cellular learning automata because of its ability to learn and also is superior to single learning automaton because it is a collection of learning 21 automata which can interact together. In some applications such as image processing, a type of cellular learn...