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The fusion of multiple sources of information in the organization of goal-oriented behavior: Spatial attention versus integration


Ringwald, M; Verschure, P F M J (2007). The fusion of multiple sources of information in the organization of goal-oriented behavior: Spatial attention versus integration. In: European Conference on Mobile Robots (ECMR) 2007, Freiburg, Germany, 19 September 2007 - 21 September 2007.

Abstract

In the fields of neuroscience, psychology and robotics, an important question is how to establish a unified system that will autonomously acquire both its input state space and optimal goal-oriented action policies in unknown environments. An important requirement for a such a system is to understand how multiple sources of sensory information can be integrated to support autonomous behavior. So far, Distributed Adaptive Control (DAC), a self-contained neuronal system, used only egocentric cues to achieve goal-directed behavior in a foraging task. However, implicitly acquired navigation strategies are not well exploited. In this paper, we evaluate the hypothesis that learned ego-centrically defined behavioral strategies can be improved by the integration of allocentric spatial information. Using an extension of the DAC architecture in the context of random foraging, we show that this integration can be rather seen as an instance of Bayesian inference as opposed to selective attention. We provide an extensive analysis of the architecture and compare its performance in the broader context using a known robotics algorithm. Our results further support the belief that a Bayesian framework can provide for a unified view on the organization of goal-oriented behavior.

In the fields of neuroscience, psychology and robotics, an important question is how to establish a unified system that will autonomously acquire both its input state space and optimal goal-oriented action policies in unknown environments. An important requirement for a such a system is to understand how multiple sources of sensory information can be integrated to support autonomous behavior. So far, Distributed Adaptive Control (DAC), a self-contained neuronal system, used only egocentric cues to achieve goal-directed behavior in a foraging task. However, implicitly acquired navigation strategies are not well exploited. In this paper, we evaluate the hypothesis that learned ego-centrically defined behavioral strategies can be improved by the integration of allocentric spatial information. Using an extension of the DAC architecture in the context of random foraging, we show that this integration can be rather seen as an instance of Bayesian inference as opposed to selective attention. We provide an extensive analysis of the architecture and compare its performance in the broader context using a known robotics algorithm. Our results further support the belief that a Bayesian framework can provide for a unified view on the organization of goal-oriented behavior.

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Additional indexing

Item Type:Conference or Workshop Item (Speech), not refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Neuroinformatics
Dewey Decimal Classification:570 Life sciences; biology
Language:English
Event End Date:21 September 2007
Deposited On:21 Mar 2014 13:39
Last Modified:05 Apr 2016 17:42
Publisher:European Conference on Mobile Robots
Official URL:http://specs.upf.edu/publication/2709
Permanent URL: https://doi.org/10.5167/uzh-93221

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