Literature DB >> 21227083

Behavior-based robotics as a tool for synthesis of artificial behavior and analysis of natural behavior.

M J Matarić1.   

Abstract

Work in behavior-based systems focuses on functional modeling, that is, the synthesis of life-like and/or biologically inspired behavior that is robust, repeatable and adaptive. Inspiration from cognitive science, neuroscience and biology drives the development of new methods and models in behavior-based robotics, and the results tie together several related fields including artificial life, evolutionary computation, and multi-agent systems. Ideas from artificial intelligence and engineering continue to be explored actively and applied to behavior-based robots as their role in animal modeling and practical applications is being developed.

Year:  1998        PMID: 21227083     DOI: 10.1016/s1364-6613(98)01141-3

Source DB:  PubMed          Journal:  Trends Cogn Sci        ISSN: 1364-6613            Impact factor:   20.229


  6 in total

1.  The New Robotics-towards human-centered machines.

Authors:  Stefan Schaal
Journal:  HFSP J       Date:  2007-07-16

2.  Combined Computational Systems Biology and Computational Neuroscience Approaches Help Develop of Future "Cognitive Developmental Robotics".

Authors:  Faramarz Faghihi; Ahmed A Moustafa
Journal:  Front Neurorobot       Date:  2017-11-15       Impact factor: 2.650

3.  Autonomous Sequence Generation for a Neural Dynamic Robot: Scene Perception, Serial Order, and Object-Oriented Movement.

Authors:  Jan Tekülve; Adrien Fois; Yulia Sandamirskaya; Gregor Schöner
Journal:  Front Neurorobot       Date:  2019-11-15       Impact factor: 2.650

4.  Predictive coding strategies for developmental neurorobotics.

Authors:  Jun-Cheol Park; Jae Hyun Lim; Hansol Choi; Dae-Shik Kim
Journal:  Front Psychol       Date:  2012-05-07

5.  Social Interaction with an "Unidentified Moving Object" Elicits A-Not-B Error in Domestic Dogs.

Authors:  Anna Gergely; Anna B Compton; Ruth C Newberry; Ádám Miklósi
Journal:  PLoS One       Date:  2016-04-13       Impact factor: 3.240

6.  A Bayesian Developmental Approach to Robotic Goal-Based Imitation Learning.

Authors:  Michael Jae-Yoon Chung; Abram L Friesen; Dieter Fox; Andrew N Meltzoff; Rajesh P N Rao
Journal:  PLoS One       Date:  2015-11-04       Impact factor: 3.240

  6 in total

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