The necessity of studying sensor networks, rich internet applications, social networks and molecular biology have raised the need of being able to consider systems composed by very large population of similar objects. This lead to the development of new modelling paradigms, such as Fluid Process Algebra, Mean Field analysis and Markovian Agents. These methodologies produces exact results if the number of considered objects goes to the infinity, but provide reasonable approximations even for finite quantities. In this work Mean Field analysis and Markovian Agents models will be presented
abstract: The problem of modeling and controlling the distribution of a multi-agent system has recen...
A Markovian Agent Model (MAM) is an agent-based spatio-temporal analytical formalism aimed to model ...
In many domain areas the behaviour of a system can be described at two levels: the behaviour of indi...
The necessity of studying sensor networks, rich internet applications, social networks and molecular...
Modeling and analysing very large stochastic systems composed of interacting entities is a very chal...
The mean-field analysis technique is used to perform analysis of a system with a large number of com...
A Markovian Agent Model (MAM) is a stochastic model that provides a flexible, powerful and scalable...
Large systems of interacting objects are highly prevalent in today's world. Such system usually cons...
The mean-field analysis technique is used to perform analysis of a systems with a large number of co...
Markovian Agents (MAs) are stochastic entities introduced with the aim of providing a flexible, powe...
The statement of the mean field approximation theorem in the mean field theory of Markov processes p...
The paper discusses a family of Markov processes that represent many particle systems, and their lim...
Abstract Fluid or mean-field methods are approximate analytical techniques which have proven effecti...
This self-contained text develops a Markov chain approach that makes the rigorous analysis of a clas...
We study the limiting behaviour of stochastic models of populations of interacting agents, as the nu...
abstract: The problem of modeling and controlling the distribution of a multi-agent system has recen...
A Markovian Agent Model (MAM) is an agent-based spatio-temporal analytical formalism aimed to model ...
In many domain areas the behaviour of a system can be described at two levels: the behaviour of indi...
The necessity of studying sensor networks, rich internet applications, social networks and molecular...
Modeling and analysing very large stochastic systems composed of interacting entities is a very chal...
The mean-field analysis technique is used to perform analysis of a system with a large number of com...
A Markovian Agent Model (MAM) is a stochastic model that provides a flexible, powerful and scalable...
Large systems of interacting objects are highly prevalent in today's world. Such system usually cons...
The mean-field analysis technique is used to perform analysis of a systems with a large number of co...
Markovian Agents (MAs) are stochastic entities introduced with the aim of providing a flexible, powe...
The statement of the mean field approximation theorem in the mean field theory of Markov processes p...
The paper discusses a family of Markov processes that represent many particle systems, and their lim...
Abstract Fluid or mean-field methods are approximate analytical techniques which have proven effecti...
This self-contained text develops a Markov chain approach that makes the rigorous analysis of a clas...
We study the limiting behaviour of stochastic models of populations of interacting agents, as the nu...
abstract: The problem of modeling and controlling the distribution of a multi-agent system has recen...
A Markovian Agent Model (MAM) is an agent-based spatio-temporal analytical formalism aimed to model ...
In many domain areas the behaviour of a system can be described at two levels: the behaviour of indi...