We define a Markovian Agent Model (MAM) as an analytical model formed by a spatial collection of interacting Markovian Agents (MAs), whose properties and behavior can be evaluated by numerical techniques. MAMs have been introduced with the aim of providing a flexible and scalable framework for distributed systems of interacting objects, where both the local properties and the interactions may depend on the geographical position. MAMs can be proposed to model biologically inspired systems since they are suited to cope with the four common principles that govern swarm intelligence: positive feedback, negative feedback, randomness, multiple interactions. In the present work, we report some results of a MAM for a Wireless Sensor Network (WSN) routing protocol based on swarm intelligence, and some preliminary results in utilizing MAs for very basic Ant Colony Optimization (ACO) benchmarks.

An intelligent swarm of Markovian Agents

BRUNEO, Dario;SCARPA, Marco Lucio;
2015

Abstract

We define a Markovian Agent Model (MAM) as an analytical model formed by a spatial collection of interacting Markovian Agents (MAs), whose properties and behavior can be evaluated by numerical techniques. MAMs have been introduced with the aim of providing a flexible and scalable framework for distributed systems of interacting objects, where both the local properties and the interactions may depend on the geographical position. MAMs can be proposed to model biologically inspired systems since they are suited to cope with the four common principles that govern swarm intelligence: positive feedback, negative feedback, randomness, multiple interactions. In the present work, we report some results of a MAM for a Wireless Sensor Network (WSN) routing protocol based on swarm intelligence, and some preliminary results in utilizing MAs for very basic Ant Colony Optimization (ACO) benchmarks.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11570/2431638
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