A stochastic automaton can perform a finite number of actions in a random environment. When a specific action is performed, the environment responds by producing an environment output that is stochastically related to the action. The aim is to design an automaton, using an evolutionary reinforcement scheme (the basis of the learning process), that can determine the best action guided by past ac...

This paper presents the theoretical underpinning of a model for symbolically representing probabilistic transition systems, an extension of labelled transition systems for the modelling of general (discrete as well as continuous or singular) probability spaces. These transition systems are particularly suited for modelling softly timed systems, real-time systems in which the time constraints ar...

A stochastic automaton can perform a finite number of actions in a random environment. When a specific action is performed, the environment responds by producing an environment output that is stochastically related to the action. This response may be favourable or unfavourable. The aim is to design an automaton that can determine the best action guided by past actions and responses. Using Stoch...

L EARNING automata [1] have attracted a considerable interest in the last three decades. They are adaptive decision making devices that operate in unknown stochastic environments and progressively improve their performance via a learning process. They have been initially used by psychologists and biologists to describe the human behavior from both psychological and biological viewpoints. Learni...