Polling policies have been introduced to simplify the accessing process in client/server systems by a centralized control access scheme. This paper considers a client/server model which employs a polling policy as its access strategy. We propose a learning-automata-based approach for polling in order to improve the throughput-delay performance of the system. Each client has an associated queue ...

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...

The optimization of reconstruction algorithms has become a key aspect in LHCb as it is currently undergoing major upgrade that will considerably increase the data processing rate. Aiming to accelerate second most time consuming process trigger, we propose an alternative algorithm for Electromagnetic Calorimeter LHCb. Together with use deep learning techniques and understanding current algorithm...

Symbolic reasoning is a key component for enabling practical use of data-driven planners in autonomous driving. In that context, deterministic finite state automata (DFA) are often used to formalize the underlying high-level decision-making process. Manual design an effective DFA can be tedious. combination with deep learning pipelines, serve as representation learn and process complex behavior...

We present an exhaustive analysis of the problem of computing the relative entropy of two probabilistic automata. We show that the problem of computing the relative entropy of unambiguous probabilistic automata can be formulated as a shortest-distance problem over an appropriate semiring, give efficient exact and approximate algorithms for its computation in that case, and report the results of...

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...

he purpose of this work is to introduce and study the concept of minimal deterministic automaton with IF-outputs which realizes the given IF-language. Among two methods for construction of such automaton presented here, one is based on Myhill-Nerode's theory while the other is based on derivatives of the given IF-language. Meanwhile, the categories of deterministic automata with IF-outputs and ...

We introduce a novel framework of reservoir computing, that is capable of both connectionist machine intelligence and symbolic computation. Cellular automaton is used as the reservoir of dynamical systems. Input is randomly projected onto the initial conditions of automaton cells and nonlinear computation is performed on the input via application of a rule in the automaton for a period of time....