Evolving Robots Able To Integrate Sensory-Motor Information Over Time

Summary We will discuss in which conditions we can expect the emergence of agents able to integrate sensory-motor information over time and later use this information to modulate their behavior accordingly. In doing so we will illustrate the problems that these agents should be able to solve and the processes that might lead to a transition from simple agents that only rely on sensory information or on their internal dynamic to agents that are also able to integrate information over time. The analysis of evolved individuals revealed that: (1) individuals able to integrate information over time rely on mixed strategy in which basic sensory-motor mechanisms are complemented and enhanced with additional internal mechanisms; (2) evolved individuals tend to rely on partial, action-oriented, and action-mediated representations of the external environment.

[1]  Michael I. Jordan.  Serial Order: A Parallel Distributed Processing Approach , 1997 .

[2]  Rolf Pfeifer,et al.  Understanding intelligence , 2020, Inequality by Design.

[3]  Francesco Mondada,et al.  Mobile Robot Miniaturisation: A Tool for Investigation in Control Algorithms , 1993, ISER.

[4]  A. Clark.  Being There: Putting Brain, Body, and World Together Again , 1996 .

[5]  Horst Hendriks-Jansen,et al.  Catching ourselves in the act , 1996 .

[6]  Jeffrey L. Elman,et al.  Finding Structure in Time , 1990, Cogn. Sci..

[7]  C. Gallistel.  The organization of learning , 1990 .

[8]  Andrew H. Fagg,et al.  Genetic programming approach to the construction of a neural network for control of a walking robot , 1992, Proceedings 1992 IEEE International Conference on Robotics and Automation.

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

[10]  Randall D. Beer,et al.  A Dynamical Systems Perspective on Agent-Environment Interaction , 1995, Artif. Intell..

[11]  Giles Mayley,et al.  Landscapes, Learning Costs, and Genetic Assimilation , 1996, Evolutionary Computation.

[12]  Stefano Nolfi,et al.  Using Emergent Modularity to Develop Control Systems for Mobile Robots , 1997, Adapt. Behav..

[13]  Inman Harvey,et al.  Seeing the Light: Artiicial Evolution, Real Vision Seeing the Light: Artiicial Evolution, Real Vision , 1994 .

[14]  Stefano Nolfi,et al.  Evolving non-trivial behaviors on real robots: A garbage collecting robot , 1997, Robotics Auton. Syst..

[15]  Nick Jacobi,et al.  Running Across the Reality Gap: Octopod Locomotion Evolved in a Minimal Simulation , 1998, EvoRobot.

[16]  Randall D. Beer,et al.  Application of evolved locomotion controllers to a hexapod robot , 1996, Robotics Auton. Syst..

[17]  Stefano Nolfi,et al.  Power and the limits of reactive agents , 2002, Neurocomputing.

[18]  Rodney A. Brooks,et al.  Intelligence Without Reason , 1991, IJCAI.

[19]  Robert F. Port,et al.  MIND IN MOTION: , 2019, Dune.

[20]  Phil Husbands,et al.  Better Living Through Chemistry: Evolving GasNets for Robot Control , 1998, Connect. Sci..

[21]  Pattie Maes,et al.  Toward the Evolution of Dynamical Neural Networks for Minimally Cognitive Behavior , 1996 .

[22]  Randall D. Beer,et al.  Computational and dynamical languages for autonomous agents , 1996 .

[23]  Stefano Nolfi,et al.  Evolving robots able to integrate sensory-motor information over time , 2003 .

[24]  Randall D. Beer,et al.  Evolving Dynamical Neural Networks for Adaptive Behavior , 1992, Adapt. Behav..

[25]  E. Rosch,et al.  The Embodied Mind: Cognitive Science and Human Experience , 1993 .

[26]  Francesco Mondada,et al.  Evolution of homing navigation in a real mobile robot , 1996, IEEE Trans. Syst. Man Cybern. Part B.

[27]  Francesco Mondada,et al.  Evolution of Plastic Neurocontrollers for Situated Agents , 1996 .