Learning automata (LA) were recently shown to be valuable tools for designing Multi-Agent Reinforcement Learning algorithms and are able to control the stochastic games. In this paper, the concepts of stigmergy and entropy are imported into learning automata based multi-agent systems with the purpose of providing a simple framework for interaction and coordination in multi-agent systems and spe...

We investigate weighted asynchronous cellular automata with weights in valuation monoids. These automata form a distributed extension of weighted finite automata and allow us to model concurrency. Valuation monoids are abstract weight structures that include semirings and (non-distributive) bounded lattices but also offer the possibility to model average behaviors. We prove that weighted asynch...

In this paper, we establish the learnability of simple deterministic finitememory automata via membership and equivalence queries. Sim ple deterministic fmitememory automata are a subclass of finitememory automata introduced by Kaminski and Francez (1994) as a model gen eralizing finite automata to infinite alphabets. Continuously, Sakamoto and Ikeda investigated several decision problems for f...

The recent work on automata whose variables and parameters are real numbers (e.g., Blum, Shub, and Smale, 1989; Koiran, 1993; Bournez and Cosnard, 1996; Siegelmann, 1996; Moore, 1996) has focused largely on questions about computational complexity and tractability. It is also revealing to examine the metric relations that such systems induce on automata via the natural metrics on their paramete...

In this paper, the concept of fuzzy automata normed linear structure spaces is introduced and suitable examples are provided. ;The ;concepts of fuzzy automata $alpha$-open sphere, fuzzy automata $mathscr{N}$-locally compact spaces, fuzzy automata $mathscr{N}$-Hausdorff spaces are also discussed. Some properties related with to fuzzy automata normed linear structure spaces and fuzzy automata $ma...

This paper provides an Angluin-style learning algorithm for a class of register automata supporting the notion of fresh data values. More specifically, we introduce session automata which are well suited for modeling protocols in which sessions using fresh values are of major interest, like in security protocols or ad-hoc networks. We show that session automata (i) have an expressiveness partly...

The results of this dissertation are two-fold. On the one hand, inductive learning techniques are extended and two new inference algorithms for inferring nondeterministic, and universal, respectively, finite-state automata are presented. On the other hand, certain learning techniques are employed and enhanced to semi-automatically infer communicating automata (also called design models in the s...

New decision-making paradigms addressing the requirements of flexibility, adaptability and intelligence are needed for future wireless networks. Moreover, mutual interactions should be captured when all the devices are autonomous and smart. Game theory is a powerful tool to study such interactions. However, since it is a branch of applied mathematic and mainly studied in economic, some featured...

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...

The classification of recognizable ω-word languages into Borel levels is the basis of many specialized solutions in the fields of formal verification and algorithmic controller synthesis. Each of these levels is characterized by a class of deterministic ω-automata, namely deterministic weak (reachability and safety), deterministic Büchi, and deterministic Muller automata. This thesis analyses t...