Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/48145
Title: Action-planning and execution from multimodal cues: An integrated cognitive model for artificial autonomous systems
Authors: Mathews, Zenon
Badia, Sergi Bermúdez I 
Verschure, Paul F M J
UNESCO Clasification: 120304 Inteligencia artificial
Keywords: Mobile Robot
Selective Attention
Autonomous System
Humanoid Robot
Data Association
Issue Date: 2010
Journal: Studies in Computational Intelligence 
Abstract: Using multimodal sensors to perceive the environment and subsequently performing intelligent sensor/motor allocation is of crucial interest for building autonomous systems. Such a capability should allow autonomous entities to (re)allocate their resources for solving their most critical tasks depending on their current state, sensory input and knowledge about the world. Architectures of artificial real-world systems with internal representation of the world and such dynamic motor allocation capabilities are invaluable for systems with limited resources. Based upon recent advances in attention research and psychophysiology we propose a general purpose selective attention mechanism that supports the construction of a world model and subsequent intelligent motor control. We implement and test this architecture including its selective attention mechanism, to build a probabilistic world model. The constructed world-model is used to select actions by means of a Bayesian inference method. Our method is tested in a multi-robot task, both in simulation and in the real world, including a coordination mission involving aerial and ground vehicles.
URI: http://hdl.handle.net/10553/48145
ISBN: 9783642134272
ISSN: 1860-949X
DOI: 10.1007/978-3-642-13428-9_24
Source: Studies in Computational Intelligence[ISSN 1860-949X],v. 299, p. 479-497
Appears in Collections:Artículos
Show full item record

SCOPUSTM   
Citations

1
checked on May 22, 2022

Page view(s)

2
checked on Jan 9, 2022

Google ScholarTM

Check

Altmetric


Share



Export metadata



Items in accedaCRIS are protected by copyright, with all rights reserved, unless otherwise indicated.