There has been a lot of hype on Artificial Intelligence (AI) recently. Most people identify AI with GOFAI (Good Old Fashioned Artificial Intelligence, broadly speaking ‘functionalist’ and ‘symbolic’ AI), or Machine Learning, especially, Deep Learning which represents comparatively a subset of AI research itself. This identification is favored by the recent remarkable successes in particular of Deep Learning in object and speech recognition and other tasks. Deep (Reinforcement) Learning has also been applied to robotics to some problems with remarkable success.
GOFAI is affected by the Frame-of-Reference issue as already well explained among others by the American philosopher Daniel Dennett decades ago. Deep Learning has been initially applied for the indexing of large data sets of images, sound samples and the profiling of online customers of marketplace platforms and social networks users. Its application to physical systems and in particular robotic systems raises some issues as usually the data coming from robot sensors are in comparatively limited amounts and the robots interact and affect their environment making for example real time object recognition more problematic. Both paradigms are implicitly based on Descartes’ idea of mind body separation. The very fact that we have two distinct disciplines one for the body (Robotics) and one for the mind (AI) is difficult to justify from a philosophical and epistemological standpoint. Ideas like those of the XX century philosopher Merlau-Ponty (“the body understands”) seem more in line with what we know of perception in humans, animals and even plants. Moreover, the principles of organization of natural intelligent and cognitive agents are rather different from the mainstream design principles of intelligent robots. In nature cognition and intelligence are usually embedded in a physical system (a body), emerging bottom-up from the interaction of large numbers of loosely coupled components and is usually associated to Life, while the ‘mechatronics paradigm’ used to build mainstream robots, implements top-down controls, keeping well divided the body (usually a complex mechanical structure, made of rigid parts actuated by electric motors with sophisticated sensors and actuators) from the mind (a set of complex algorithms running on microprocessors arrays). The Fukushima accident and the recent Covid-19 pandemics have shown how current robotics solutions are still insufficiently developed to cope with real world challenges. Together with the results of the DARPA Robotics Challenge and the delayed adoption of self-driving cars this suggests that the philosophical and epistemological concerns may actually have quite practical implications.
Modeling and control of embodied and enactive intelligent systems capable to enable the design of complex physical intelligent systems still pose hard research challenges.
Coping with those challenges is mandatory if we aim to develop artificial intelligent agents comparable to the natural ones and gain a better understanding of natural intelligence, cognition and life itself.
The article collection aims to gather the many researchers working on those issues, foster the exchange of different perspectives and to create a common research agenda with shared problems and shared language.
The time is ripe to take to the center stage the 'embodied and enactive paradigm’ in AI, Cognition and Robotics.
Topics include but are not limited to the following:
• Embodiment in Embodied and Enactive Ai/Cognition
• Evo Devo Methods in Embodied and Enactive AI/Cognition
• Morphological computation
• Orchestration control
• Biomimetics and Biomimicry
• Natural Human Robot/Agent Interaction
• Emergence and Self-organization of Behaviors
• Self-structuring of Sensory Motor Information
• Foundational Approaches
• Foundational Approaches to Soft Robotics
• Evolutionary approaches to the emergence of AI in embodied behavioral agents
• Evolutionary Robotics and AI
• Information Lens on Embodied/Enactive AI and Cognition
• Machine Learning and Deep Learning approaches to Physical Systems
• Modeling of Human and Natural Agents Motion
• Limits of ML and DL
• Limits of GOFAI
• Relation between Life and Cognition
• Epistemological Issues
• Research Reproducibility and Objective Operational Measures of Performance of Natural and Artificial agents
• Empathy and Moral Agents
• Quantum AI and Embodiment
• Xenobots
There has been a lot of hype on Artificial Intelligence (AI) recently. Most people identify AI with GOFAI (Good Old Fashioned Artificial Intelligence, broadly speaking ‘functionalist’ and ‘symbolic’ AI), or Machine Learning, especially, Deep Learning which represents comparatively a subset of AI research itself. This identification is favored by the recent remarkable successes in particular of Deep Learning in object and speech recognition and other tasks. Deep (Reinforcement) Learning has also been applied to robotics to some problems with remarkable success.
GOFAI is affected by the Frame-of-Reference issue as already well explained among others by the American philosopher Daniel Dennett decades ago. Deep Learning has been initially applied for the indexing of large data sets of images, sound samples and the profiling of online customers of marketplace platforms and social networks users. Its application to physical systems and in particular robotic systems raises some issues as usually the data coming from robot sensors are in comparatively limited amounts and the robots interact and affect their environment making for example real time object recognition more problematic. Both paradigms are implicitly based on Descartes’ idea of mind body separation. The very fact that we have two distinct disciplines one for the body (Robotics) and one for the mind (AI) is difficult to justify from a philosophical and epistemological standpoint. Ideas like those of the XX century philosopher Merlau-Ponty (“the body understands”) seem more in line with what we know of perception in humans, animals and even plants. Moreover, the principles of organization of natural intelligent and cognitive agents are rather different from the mainstream design principles of intelligent robots. In nature cognition and intelligence are usually embedded in a physical system (a body), emerging bottom-up from the interaction of large numbers of loosely coupled components and is usually associated to Life, while the ‘mechatronics paradigm’ used to build mainstream robots, implements top-down controls, keeping well divided the body (usually a complex mechanical structure, made of rigid parts actuated by electric motors with sophisticated sensors and actuators) from the mind (a set of complex algorithms running on microprocessors arrays). The Fukushima accident and the recent Covid-19 pandemics have shown how current robotics solutions are still insufficiently developed to cope with real world challenges. Together with the results of the DARPA Robotics Challenge and the delayed adoption of self-driving cars this suggests that the philosophical and epistemological concerns may actually have quite practical implications.
Modeling and control of embodied and enactive intelligent systems capable to enable the design of complex physical intelligent systems still pose hard research challenges.
Coping with those challenges is mandatory if we aim to develop artificial intelligent agents comparable to the natural ones and gain a better understanding of natural intelligence, cognition and life itself.
The article collection aims to gather the many researchers working on those issues, foster the exchange of different perspectives and to create a common research agenda with shared problems and shared language.
The time is ripe to take to the center stage the 'embodied and enactive paradigm’ in AI, Cognition and Robotics.
Topics include but are not limited to the following:
• Embodiment in Embodied and Enactive Ai/Cognition
• Evo Devo Methods in Embodied and Enactive AI/Cognition
• Morphological computation
• Orchestration control
• Biomimetics and Biomimicry
• Natural Human Robot/Agent Interaction
• Emergence and Self-organization of Behaviors
• Self-structuring of Sensory Motor Information
• Foundational Approaches
• Foundational Approaches to Soft Robotics
• Evolutionary approaches to the emergence of AI in embodied behavioral agents
• Evolutionary Robotics and AI
• Information Lens on Embodied/Enactive AI and Cognition
• Machine Learning and Deep Learning approaches to Physical Systems
• Modeling of Human and Natural Agents Motion
• Limits of ML and DL
• Limits of GOFAI
• Relation between Life and Cognition
• Epistemological Issues
• Research Reproducibility and Objective Operational Measures of Performance of Natural and Artificial agents
• Empathy and Moral Agents
• Quantum AI and Embodiment
• Xenobots
