Il contributo metodologico della Developmental Robotics alla psicologia

2018 ◽  
pp. 221-239
Author(s):  
Daniela Conti ◽  
Santo Di Nuovo ◽  
Angelo Cangelosi
Keyword(s):  
Developmental Robotics

Robots as cognitive tools

2002 ◽  
Vol 1 (1) ◽  
pp. 125-143 ◽  
Author(s):  
Rolf Pfeifer
Keyword(s):  
Artificial Intelligence ◽  
Problem Solving ◽  
Main Tool ◽  
Human Cognition ◽  
Abstract Thinking ◽  
Cognitive Tools ◽  
Developmental Robotics ◽  
Information Theoretic ◽  
Natural Forms ◽  
Over Time

Artificial intelligence is by its very nature synthetic, its motto is “Understanding by building”. In the early days of artificial intelligence the focus was on abstract thinking and problem solving. These phenomena could be naturally mapped onto algorithms, which is why originally AI was considered to be part of computer science and the tool was computer programming. Over time, it turned out that this view was too limited to understand natural forms of intelligence and that embodiment must be taken into account. As a consequence the focus changed to systems that are able to autonomously interact with their environment and the main tool became the robot. The “developmental robotics” approach incorporates the major implications of embodiment with regard to what has been and can potentially be learned about human cognition by employing robots as cognitive tools. The use of “robots as cognitive tools” is illustrated in a number of case studies by discussing the major implications of embodiment, which are of a dynamical and information theoretic nature.

Can cognitive developmental robotics cause a paradigm shift?

2012 ◽  
pp. 251-273 ◽  
Author(s):  
Minoru Asada ◽  
Jeffrey Krichmar ◽  
Hiroaki Wagatsuma
Keyword(s):  
Paradigm Shift ◽  
Developmental Robotics

scholarly journals Guest Editorial Behavior Understanding and Developmental Robotics

2014 ◽  
Vol 6 (2) ◽  
pp. 77-79 ◽  
Author(s):  
Albert Ali Salah ◽  
Pierre-Yves Oudeyer ◽  
Cetin Mericli ◽  
Javier Ruiz-del-Solar
Keyword(s):  
Guest Editorial ◽  
Developmental Robotics ◽  
Behavior Understanding

Computational Models in Developmental Psychology

2013 ◽  
pp. 476-504
Author(s):  
Thomas R. Shultz
Keyword(s):  
Neural Networks ◽  
Computational Modeling ◽  
Computational Models ◽  
Developmental Psychology ◽  
Probability Distributions ◽  
Network Models ◽  
Developmental Theory ◽  
Negative Evidence ◽  
Developmental Robotics ◽  
Neural Network Models

Computational modeling implements developmental theory in a precise manner, allowing generation, explanation, integration, and prediction. Several modeling techniques are applied to development: symbolic rules, neural networks, dynamic systems, Bayesian processing of probability distributions, developmental robotics, and mathematical analysis. The relative strengths and weaknesses of each approach are identified and examples of each technique are described. Ways in which computational modeling contributes to developmental issues are documented. A probabilistic model of the vocabulary spurt shows that various psychological explanations for it are unnecessary. Constructive neural networks clarify the distinction between learning and development and show how it is possible to escape Fodor’s paradox. Connectionist modeling reveals different versions of innateness and how learning and evolution might interact. Agent-based models analyze the basic principles of evolution in a testable, experimental fashion that generates complete evolutionary records. Challenges posed by stimulus poverty and lack of negative examples are explored in neural-network models that learn morphology or syntax probabilistically from indirect negative evidence.

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