Understanding intelligence
Neural Networks as Cybernetic Systems
Neural Networks as Cybernetic Systems
Artificial Evolution: A Continuing SAGA
ER '01 Proceedings of the International Symposium on Evolutionary Robotics From Intelligent Robotics to Artificial Life
Genetic Programming and Evolvable Machines
Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)
Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)
How the Body Shapes the Way We Think: A New View of Intelligence (Bradford Books)
How the Body Shapes the Way We Think: A New View of Intelligence (Bradford Books)
Steps to a Cyber-Physical Model of Networked Embodied Anticipatory Behavior
Anticipatory Behavior in Adaptive Learning Systems
Preliminary considerations for a quantitative theory of networked embodied intelligence
50 years of artificial intelligence
Using Lie group symmetries for fast corrective motion planning
International Journal of Robotics Research
Evolving spatiotemporal coordination in a modular robotic system
SAB'06 Proceedings of the 9th international conference on From Animals to Animats: simulation of Adaptive Behavior
IEEE Transactions on Robotics
Error propagation on the Euclidean group with applications to manipulator kinematics
IEEE Transactions on Robotics
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We outline a possible theoretical framework for the quantitative modeling of networked embodied cognitive systems. We note that: 1 information self-structuring through sensory-motor coordination does not deterministically occur in â聞聺n vector space, a generic multivariable space, but in SE3, the group structure of the possible motions of a body in space; 2 it happens in a stochastic open-ended environment. These observations may simplify, at the price of a certain abstraction, the modeling and the design of self-organization processes based on the maximization of some informational measures, such as mutual information. Furthermore, by providing closed form or computationally lighter algorithms, it may significantly reduce the computational burden of their implementation. We propose a modeling framework that aims to give new tools for the design of networks of new artificial self-organizing, embodied, and intelligent agents and for the reverse engineering of natural networks. At this point, it represents largely a theoretical conjecture, and must still to be experimentally verified whether this model will be useful in practice.