Future Robots: Towards a robotic science of human beings

This book is for both robot builders and scientists who study human behaviour and human societies. Scientists do not only collect empirical data but they also formulate theories to explain the data. Theories of human behaviour and human societies are traditionally expressed in words but, today, with the advent of the computer they can also be expressed by constructing computer-based artefacts. If the artefacts do what human beings do, the theory/blueprint that has been used to construct the artefacts explains human behaviour and human societies. Since human beings are primarily bodies, the artefacts must be robots, and human robots must progressively reproduce all we know about human beings and their societies. And, although they are purely scientific tools, they can have one very important practical application: helping human beings to better understand the many difficult problems they face today and will face in the future - and, perhaps, to find solutions for these problems.

[1]  D. Parisi,et al.  Self-contained algorithms to detect communities in networks , 2004 .

[2]  Randall D. Beer,et al.  The brain has a body: adaptive behavior emerges from interactions of nervous system, body and environment , 1997, Trends in Neurosciences.

[3]  Leslie Pack Kaelbling,et al.  Ecological Robotics , 1998, Adapt. Behav..

[4]  Matthew Schlesinger,et al.  Evolving agents as a metaphor for the developing child. , 2004, Developmental science.

[5]  Domenico Parisi,et al.  The strategic level and the tactical level of behaviour , 2010 .

[6]  Domenico Parisi,et al.  Living in Enclaves , 2001, Complex..

[7]  Sinan Kalkan,et al.  The learning of adjectives and nouns from affordance and appearance features , 2013, Adapt. Behav..

[8]  Domenico Parisi,et al.  Neminem laedere. An evolutionary agent-based model of the interplay between punishment and damaging behaviours , 2013, Artificial Intelligence and Law.

[9]  Domenico Parisi,et al.  THE ROLE OF PERCEPTION AND ACTION IN OBJECT CATEGORISATION , 2002 .

[10]  Domenico Parisi,et al.  Simulating the Evolution of Artifacts , 1999, ECAL.

[11]  Stefano Nolfi,et al.  Evolving Artificial Neural Networks that Develop in Time , 1995, ECAL.

[12]  Domenico Parisi,et al.  Robots that have emotions , 2010, Adapt. Behav..

[13]  Luc Steels,et al.  Experiments in cultural language evolution , 2012 .

[14]  Dario Floreano,et al.  Using robots to understand social behaviour , 2013, Biological reviews of the Cambridge Philosophical Society.

[15]  D. Parisi.  Evolutionary Psychology and Artificial Life , 2003 .

[16]  Stefano Nolfi,et al.  Distributed Coordination of Simulated Robots Based on Self-Organization , 2006, Artificial Life.

[17]  Stefano Nolfi,et al.  Emergence of Protosentences in Artificial Communicating Systems , 2011, IEEE Transactions on Autonomous Mental Development.

[18]  L. Steels.  Modeling the cultural evolution of language. , 2011, Physics of life reviews.

[19]  Dario Floreano,et al.  Neuroevolution: from architectures to learning , 2008, Evol. Intell..

[20]  D. Floreano,et al.  Relatedness influences signal reliability in evolving robots , 2011, Proceedings of the Royal Society B: Biological Sciences.

[21]  Tom Ziemke,et al.  On the role of emotion in biological and robotic autonomy , 2008, Biosyst..

[22]  Domenico Parisi,et al.  What to Do With a Surplus , 1997 .

[23]  Magnus Enquist,et al.  Regulatory Traits: Cultural Influences on Cultural Evolution , 2014, Evolution, Complexity and Artificial Life.

[24]  Stefano Nolfi,et al.  Self-selection of Input Stimuli for Improving Performance , 1993 .

[25]  A. Cangelosi,et al.  The processing of verbs and nouns in neural networks: Insights from synthetic brain imaging , 2004, Brain and Language.

[26]  Filippo Menczer,et al.  From Complex Environments to Complex Behaviors , 1996, Adapt. Behav..

[27]  Stefano Nolfi,et al.  Evolving Mobile Robots Able to Display Collective Behaviors , 2003, Artificial Life.

[28]  Domenico Parisi,et al.  In What Kinds of Social Groups can “Altruistic” Behaviors Evolve? , 1997 .

[29]  Colm O'Riordan,et al.  Increasing Population Diversity Through Cultural Learning , 2006, Adapt. Behav..

[30]  D. Parisi,et al.  Phenotypic plasticity in evolving neural networks , 1994, Proceedings of PerAc '94. From Perception to Action.

[31]  D. Parisi,et al.  Towards a Vygotskyan cognitive robotics: The role of language as a cognitive tool , 2011 .

[32]  Domenico Parisi,et al.  Artificial Life and Piaget , 2002 .

[33]  Illah R. Nourbakhsh,et al.  A survey of socially interactive robots , 2003, Robotics Auton. Syst..

[34]  Inman Harvey,et al.  Explorations in Evolutionary Robotics , 1993, Adapt. Behav..

[35]  Peter M. Todd,et al.  Parental Guidance Suggested: How Parental Imprinting Evolves Through Sexual Selection as an Adaptive Learning Mechanism , 1993, Adapt. Behav..

[36]  Michael T. Turvey,et al.  Gibsonian Affordances for Roboticists , 2007, Adapt. Behav..

[37]  M. Schlesinger,et al.  The Past, Present, and Future of Computational Models of Cognitive Development. , 2012 .

[38]  Angelo Cangelosi,et al.  The Emergence of a 'Language' in an Evolving Population of Neural Networks , 1998, Connect. Sci..

[39]  Domenico Parisi,et al.  Cultural evolution in neural networks , 1997 .

[40]  Active Sampling in Evolving Neural Networks , 1997 .

[41]  Domenico Parisi,et al.  Some Adaptive Advantages of the Ability to Make Predictions , 2006, SAB.

[42]  J. Fellous,et al.  Emotions: from brain to robot , 2004, Trends in Cognitive Sciences.

[43]  Domenico Parisi,et al.  Groups of Agents with a Leader , 2022 .

[44]  D. Parisi,et al.  TRoPICALS: a computational embodied neuroscience model of compatibility effects. , 2010, Psychological review.

[45]  Filippo Menczer,et al.  Maturation and the Evolution of Imitative Learning in Artificial Organisms , 1995, Adapt. Behav..

[46]  Stefano Nolfi,et al.  Duplication of Modules Facilitates the Evolution of Functional Specialization , 1999, Artificial Life.

[47]  Domenico Parisi,et al.  How can we explain the emergence of a language that benefits the hearer but not the speaker? , 2005, Connect. Sci..

[48]  Stefano Nolfi,et al.  The Facilitatory Role of Linguistic Instructions on Developing Manipulation Skills , 2010, IEEE Computational Intelligence Magazine.

[49]  Domenico Parisi,et al.  Male and female robots , 2011, Adapt. Behav..

[50]  D. Floreano,et al.  Evolutionary Conditions for the Emergence of Communication in Robots , 2007, Current Biology.

[51]  Stefano Nolfi,et al.  Exploiting the Power of Sensory-Motor Coordination , 1999, ECAL.

[52]  Domenico Parisi,et al.  Chapter 11 – Computational Models of Developmental Mechanisms , 1996 .

[53]  Domenico Parisi,et al.  Learning in the Active Mode , 1995, ECAL.

[54]  Stefano Nolfi,et al.  Co-evolving predator and prey robots , 2012, Adapt. Behav..

[55]  Stefano Nolfi,et al.  Selective attention enables action selection: evidence from evolutionary robotics experiments , 2013, Adapt. Behav..

[56]  Angelo Cangelosi,et al.  A unified simulation scenario for language development, evolution, and historical change , 2002 .

[57]  Lola Cañamero,et al.  Hedonic value: enhancing adaptation for motivated agents , 2013, Adapt. Behav..

[58]  D. Parisi.  An Artificial Life Approach to Language , 1997, Brain and Language.

[59]  Domenico Parisi,et al.  How Producer Biases Can Favor the Evolution of Communication: An Analysis of Evolutionary Dynamics , 2008, Adapt. Behav..

[60]  Domenico Parisi,et al.  Language as a Cognitive Tool , 2009, Minds and Machines.

[61]  Angelo Cangelosi,et al.  The Adaptive Advantage of Symbolic Theft Over Sensorimotor Toil: Grounding Language in Perceptual Categories , 2001 .

[62]  Takashi Ikegami,et al.  Rehabilitating Biology as a Natural History , 2009, Adapt. Behav..

[63]  Giorgio Metta,et al.  Better Vision through Manipulation , 2003, Adapt. Behav..

[64]  A. Cangelosi,et al.  Cell division and migration in a 'genotype' for neural networks (Cell division and migration in neural networks) , 1993 .

[65]  D. Parisi,et al.  Learning to Reach by Constraining the Movement Search Space , 2000 .

[66]  D. Parisi,et al.  The Agent-Based Approach: A New Direction for Computational Models of Development , 2001 .

[67]  Domenico Parisi,et al.  Social Aggregations in Evolving Neural Networks , 1992, MAAMAW.

[68]  D. Parisi,et al.  Information and Cooperation in a Simulated Labor Market: A Computational Model for the Evolution of Workers and Firms , 2007 .

[69]  Domenico Parisi,et al.  Input from the External Environment and Input from within the Body , 2009, ECAL.

[70]  Inman Harvey,et al.  Evolutionary Robotics: A New Scientific Tool for Studying Cognition , 2005, Artificial Life.

[71]  Jeffrey L. Elman,et al.  Learning and Evolution in Neural Networks , 1994, Adapt. Behav..

[72]  Matthew Schlesinger,et al.  A Lesson from Robotics: Modeling Infants as Autonomous Agents , 2003, Adapt. Behav..

[73]  Claudio Castellano,et al.  Defining and identifying communities in networks. , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[74]  Stefano Nolfi,et al.  Learning to Adapt to Changing Environments in Evolving Neural Networks , 1996, Adapt. Behav..

[76]  Lola Cañamero,et al.  Learning Affordances of Consummatory Behaviors: Motivation-Driven Adaptive Perception , 2010, Adapt. Behav..

[77]  Domenico Parisi,et al.  Cultural Change in Spatial Environments , 2003 .

[78]  Stefano Nolfi,et al.  18 – Artificial life models of neural development , 2003 .

[79]  Stefano Nolfi.  How Learning and Evolution Interact: The Case of a Learning Task which Differs from the Evolutionary Task , 1999, Adapt. Behav..