Model-based learning of interaction strategies in repeated-games has received a lot of attention in the game-theory literature. Gilboa & Samet [10] deal with bounded regular players. They describe a model-based learning strategy for repeated games that learns the best response against any regular strategy. Their procedure enumerates the set of all automata and chooses the current opponent model...

Social networks are streaming, diverse and include a wide range of edges so that continuously evolves over time and formed by the activities among users (such as tweets, emails, etc.), where each activity among its users, adds an edge to the network graph. Despite their popularities, the dynamicity and large size of most social networks make it difficult or impossible to study the entire networ...

In this paper, we propose an efficient approach to design optimization of analog circuits that is based on the reinforcement learning method. In this work, Multi-Objective Learning Automata (MOLA) is used to design a two-stage CMOS operational amplifier (op-amp) in 0.25μm technology. The aim is optimizing power consumption and area so as to achieve minimum Total Optimality Index (TOI), as a new...

We present here a construction for the derived term automaton (aka partial derivative, or Antimirov, automaton) of rational (or regular) expression based on sole induction depth and without making reference to an operation derivation expression. It is particularly well-suited case weighted expressions.

The cellular learning automata, which is a combination of cellular automata, and learning automata, is a new recently introduced model. This model is superior to cellular automata because of its ability to learn and is also superior to a single learning automaton because it is a collection of learning automata which can interact with each other. The basic idea of cellular learning automata, whi...

we present some connections between the max-min general fuzzy automaton theory and the hyper structure theory. first, we introduce a hyper bck-algebra induced by a max-min general fuzzy automaton. then, we study the properties of this hyper bck-algebra. particularly, some theorems and results for hyper bck-algebra are proved. for example, it is shown that this structure consists of different ty...

in this paper we proposed a cellular automaton based local algorithm to solve the autonomously sensor gathering problem in mobile wireless sensor networks (mwsn). in this problem initially the connected mobile sensors deployed in the network and goal is gather all sensors into one location. the sensors decide to move only based on their local information. cellular automaton (ca) as dynamical sy...

Cellular learning automata is a combination of learning automata and cellular automata. 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 automata which can interact together. In some applications such as image processing, a type of cellular learning automata in which the ...

This paper proposes DeepSynth, a method for effective training of deep Reinforcement Learning (RL) agents when the reward is sparse and non-Markovian, but at same time progress towards requires achieving an unknown sequence high-level objectives. Our employs novel algorithm synthesis compact automata to uncover this sequential structure automatically. We synthesise human-interpretable automaton...

Systems that perform \generalization-to-N" in explanation-based learning generalize a proof tree by generalizing the shape of the tree, rather than simply changing constants to variables. This paper introduces a formal framework which can be used either to characterize or to specify the outputs of an algorithm for generalizing number. The framework consists of two desiderata, or desired propert...