Morpho-functional Machines are a set of tools for investigating the design of embodied intelligence in autonomous bio-artifact systems. The focus in Morpho-functional Machines is on the balance of morphology, materials, and control; intelligent behavior emerges from the interaction of an autonomous system with a real-world environment. How, then, should body morphology, body materials, and sensory systems be designed to achieve a certain set of tasks or desired behaviors in a particular environment? This and other questions were addressed at the International Workshop on Morpho-functional Machines held in Tokyo in 2001. Collected here are the revised papers from the workshop, providing a new perspective for understanding embodied intelligence. Presenting the innovative concept of Morpho-functional Machines, this book is a valuable source for scientists and engineers working in ethnology, cognitive sciences, robotic engineering, and artificial intelligence.

• Motivation It is our dream to understand the principles of animals’ remarkable ability for adaptive motion and to transfer such abilities to a robot. Up to now, mechanisms for generation and control of stereotyped motions and adaptive motions in well-known simple environments have been formulated to some extentandsuccessfullyappliedtorobots.However,principlesofadaptationto variousenvironmentshavenotyetbeenclari?ed,andautonomousadaptation remains unsolved as a seriously di?cult problem in robotics. Apparently, the ability of animals and robots to adapt in a real world cannot be explained or realized by one single function in a control system and mechanism. That is, adaptation in motion is induced at every level from thecentralnervoussystemtothemusculoskeletalsystem.Thus,weorganized the International Symposium on Adaptive Motion in Animals and Machines(AMAM)forscientistsandengineersconcernedwithadaptation onvariouslevelstobebroughttogethertodiscussprinciplesateachleveland to investigate principles governing total systems. • History AMAM started in Montreal (Canada) in August 2000. It was organized by H. Kimura (Japan), H. Witte (Germany), G. Taga (Japan), and K. Osuka (Japan), who had agreed that having a small symposium on motion control, with people from several ?elds coming together to discuss speci?c issues, was worthwhile. Those four organizing committee members determined the scope of AMAM as follows.

Originating from a Dagstuhl seminar, the collection of papers presented in this book constitutes on the one hand a representative state-of-the-art survey of embodied artificial intelligence, and on the other hand the papers identify the important research trends and directions in the field. Following an introductory overview, the 23 papers are organized into topical sections on - philosophical and conceptual issues - information, dynamics, and morphology - principles of embodiment for real-world applications - developmental approaches - artificial evolution and self-reconfiguration

TheArti?cialLifetermappearedmorethan20yearsagoinasmallcornerofNew Mexico, USA. Since then the area has developed dramatically, many researchers joining enthusiastically and research groups sprouting everywhere. This frenetic activity led to the emergence of several strands that are now established ?elds in themselves. We are now reaching a stage that one may describe as maturer: with more rigour, more benchmarks, more results, more stringent acceptance criteria, more applications, in brief, more sound science. This, which is the n- ural path of all new areas, comes at a price, however. A certain enthusiasm, a certain adventurousness from the early years is fading and may have been lost on the way. The ?eld has become more reasonable. To counterbalance this and to encourage lively discussions, a conceptual track, where papers were judged on criteria like importance and/or novelty of the concepts proposed rather than the experimental/theoretical results, has been introduced this year. A conference on a theme as broad as Arti?cial Life is bound to be very - verse,but a few tendencies emerged. First, ?elds like ‘Robotics and Autonomous Agents’ or ‘Evolutionary Computation’ are still extremely active and keep on bringing a wealth of results to the A-Life community. Even there, however, new tendencies appear, like collective robotics, and more speci?cally self-assembling robotics, which represent now a large subsection. Second, new areas appear.

An exploration of embodied intelligence and its implications points toward a theory of intelligence in general; with case studies of intelligent systems in ubiquitous computing, business and management, human memory, and robotics. How could the body influence our thinking when it seems obvious that the brain controls the body? In How the Body Shapes the Way We Think, Rolf Pfeifer and Josh Bongard demonstrate that thought is not independent of the body but is tightly constrained, and at the same time enabled, by it. They argue that the kinds of thoughts we are capable of have their foundation in our embodiment—in our morphology and the material properties of our bodies. This crucial notion of embodiment underlies fundamental changes in the field of artificial intelligence over the past two decades, and Pfeifer and Bongard use the basic methodology of artificial intelligence—"understanding by building"—to describe their insights. If we understand how to design and build intelligent systems, they reason, we will better understand intelligence in general. In accessible, nontechnical language, and using many examples, they introduce the basic concepts by building on recent developments in robotics, biology, neuroscience, and psychology to outline a possible theory of intelligence. They illustrate applications of such a theory in ubiquitous computing, business and management, and the psychology of human memory. Embodied intelligence, as described by Pfeifer and Bongard, has important implications for our understanding of both natural and artificial intelligence.

Nature is the world's foremost designer. With billions of years of experience and boasting the most extensive laboratory available, it conducts research in every branch of engineering and science. Nature's designs and capabilities have always inspired technology, from the use of tongs and tweezers to genetic algorithms and autonomous legged robots.

This book, written by leading international researchers of evolutionary approaches to machine learning, explores various ways evolution can address machine learning problems and improve current methods of machine learning. Topics in this book are organized into five parts. The first part introduces some fundamental concepts and overviews of evolutionary approaches to the three different classes of learning employed in machine learning. The second addresses the use of evolutionary computation as a machine learning technique describing methodologic improvements for evolutionary clustering, classification, regression, and ensemble learning. The third part explores the connection between evolution and neural networks, in particular the connection to deep learning, generative and adversarial models as well as the exciting potential of evolution with large language models. The fourth part focuses on the use of evolutionary computation for supporting machine learning methods. This includes methodological developments for evolutionary data preparation, model parametrization, design, and validation. The final part covers several chapters on applications in medicine, robotics, science, finance, and other disciplines. Readers find reviews of application areas and can discover large-scale, real-world applications of evolutionary machine learning to a variety of problem domains. This book will serve as an essential reference for researchers, postgraduate students, practitioners in industry and all those interested in evolutionary approaches to machine learning.