Title :
Discovering Sensing Capability in Multi-agent Systems
Author :
Parpaglione, Cristina ; Santos, Juan Miguel
Author_Institution :
Dept. de Ingeniera Inf., Inst. Tecnol. de Buenos Aires (ITBA), Buenos Aires, Argentina
Abstract :
Which should the sensing capabilities of agents in a Multiagent System be to solve a problem efficiently, fast and with low cost? This question often appears when trying to solve a problem using Multiagent System. This paper introduces a method to find out these sensing capabilities in order to solve a given problem. To achieve this, the sensing capability of an agent is modeled by a parametrized function and then Genetic Algorithms are used to find the parameters values. The individual behavior of the agents are found with Reinforcement Learning.
Keywords :
genetic algorithms; learning (artificial intelligence); multi-agent systems; genetic algorithms; multiagent sensing capability; multiagent systems; parametrized function; reinforcement learning; Apertures; Biological cells; Sensor arrays; Shape; Testing; Training; Genetic Algorithms; Multiagent Systems; Reinforcement Learning; Sensing Capability; Sensing Parametrization;
Conference_Titel :
Chilean Computer Science Society (SCCC), 2010 XXIX International Conference of the
Conference_Location :
Antofagasta
Print_ISBN :
978-1-4577-0073-6
Electronic_ISBN :
1522-4902
DOI :
10.1109/SCCC.2010.22
