The classical approach to the acquisition of knowledge and reason in artificial intelligence is to program the facts and rules into the machine. Unfortunately, the amount of time required to program the equivalent of human intelligence is prohibitively large. An alternative approach allows an automaton to learn to solve problems through iterative trial-and-error interaction with its environment...

The problem of analyzing the finite time behavior of learning automata is considered. This problem involves the finite time analysis of the learning algorithm used by the learning automaton and is important in determining the rate of convergence of the automaton. In this paper, a general framework for analyzing the finite time behavior of the automaton learning algorithms is proposed. Using thi...

A model for discrete event systems (DES) can be learned from observations. We propose a simple type of timed automaton to model DES where the timing of the events is important. Learning such an automaton is proven to be NP-complete by a reduction from the problem of learning deterministic finite state automata (DFA) without time. Based on this reduction, we show how the currently best learning ...

A Learning Automaton is a learning entity that learns the optimal action to use from its set of possible actions. It does this by performing actions toward an environment and analyzes the resulting response. The response, being both good and bad, results in behaviour change to the automaton (the automaton will learn based on this response). This behaviour change is often called reinforcement al...

We present a model of learning cellular automata (LCA) as an emergent system having some collective behaviors. LCA is an extended version of the traditional cellular automaton. Especially, we adopt the LCA with some self-improving functions, called self-improving learning cellular automata (SILCA) and develop its optimization capability. Each self-improving learning cellular automaton, i.e. a m...