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Juan Miguel Santos ;Inteligencia Artificial. Revista Iberoamericana de Inteligencia Artificial 2003, 7 (21)

Author: Diego Ariel Bendersky

Source: http://www.redalyc.org/


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Inteligencia Artificial.
Revista Iberoamericana de Inteligencia Artificial ISSN: 1137-3601 revista@aepia.org Asociación Española para la Inteligencia Artificial España Bendersky, Diego Ariel; Santos, Juan Miguel Robot formations as an emergent collective task using target-following behavior Inteligencia Artificial.
Revista Iberoamericana de Inteligencia Artificial, vol.
7, núm.
21, 2003, pp.
9-18 Asociación Española para la Inteligencia Artificial Valencia, España Available in: http:--www.redalyc.org-articulo.oa?id=92572103 How to cite Complete issue More information about this article Journals homepage in redalyc.org Scientific Information System Network of Scientific Journals from Latin America, the Caribbean, Spain and Portugal Non-profit academic project, developed under the open access initiative Robot Formations as an Emergent Collective Task using Target-following Behavior Diego Ariel Bendersky and Juan Miguel Santos Departamento de Computación Facultad de Ciencias Exactas y Naturales Universidad de Buenos Aires Cdad.
Universitaria, Pabellón I (1428) Cdad.
de Buenos Aires, Argentina {dbenders,jmsantos}@dc.uba.ar Abstract Robot formations imply the establishment and the maintenance of a predetermined geometric shape by a group of robots.
In this work, we achieve formations using robots with proximity sensors of short detection range and no inter-robot communication equipment.
This fact turns the synthesis problem into a hard one compared to those cases where the robot has information about the absolute location of the other robots, or has sensors with a higher range limit.
The robot formations emerge from an individual behavior called target-following which we synthesize using Reinforcement Learning (RL). We propose a task decomposition technique based on an action space transformation that allows us to reduce considerably the time needed for learning and overcomes some difficulties that arise with the use of RL for this particular problem.
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