The present book is the product of conferences held in Bielefeld at the Center for interdisciplinary Sturlies (ZiF) in connection with a year-long ZiF Research Group with the theme "Prerational intelligence". The premise ex plored by the research group is that traditional notions of intelligent behav ior, which form the basis for much work in artificial intelligence and cog nitive science, presuppose many basic capabilities which are not trivial, as more recent work in robotics and neuroscience has shown, and that these capabilities may be best understood as ernerging from interaction and coop eration in systems of simple agents, elements that accept inputs from and act upon their surroundings. The main focus is on the way animals and artificial systems process in formation about their surroundings in order to move and act adaptively. The analysis of the collective properties of systems of interacting agents, how ever, is a problern that occurs repeatedly in many disciplines. Therefore, contributions from a wide variety of areas have been included in order to obtain a broad overview of phenomena that demoostrate complexity arising from simple interactions or can be described as adaptive behavior arising from the collective action of groups of agents. To this end we have invited contributions on topics ranging from the development of complex structures and functions in systems ranging from cellular automata, genetic codes, and neural connectivity to social behavior and evolution. Additional contribu tions discuss traditional concepts of intelligence and adaptive behavior. 1.

An introduction to how neuroethology can inform the development of robots controlled by synaptic networks instead of algorithms, from a pioneer in biorobotics. The trait most fundamental to the evolution of animals is the capability to adapt to novel circumstances in unpredictable environments. Recent advances in biomimetics have made it feasible to construct robots modeled on such unsupervised autonomous behavior, and animal models provide a library of existence proofs. Filling an important gap in the field, this introductory textbook illuminates how neurobiological principles can inform the development of robots that are controlled by synaptic networks, as opposed to algorithms. Joseph Ayers provides a comprehensive overview of the sensory and motor systems of a variety of model biological systems and shows how their behaviors may be implemented in artificial systems, such as biomimetic robots. Introduces the concept of biological intelligence as applied to robots, building a strategy for autonomy based on the neuroethology of simple animal models Provides a mechanistic physiological framework for the control of innate behavior Illustrates how biomimetic vehicles can be operated in the field persistently and adaptively Developed by a pioneer in biorobotics with decades of teaching experience Proven in the classroom Suitable for professionals and researchers as well as undergraduate and graduate students in cognitive science and computer science

This book presents Proceedings of the 2021 Intelligent Systems Conference which is a remarkable collection of chapters covering a wider range of topics in areas of intelligent systems and artificial intelligence and their applications to the real world. The conference attracted a total of 496 submissions from many academic pioneering researchers, scientists, industrial engineers, and students from all around the world. These submissions underwent a double-blind peer-review process. Of the total submissions, 180 submissions have been selected to be included in these proceedings. As we witness exponential growth of computational intelligence in several directions and use of intelligent systems in everyday applications, this book is an ideal resource for reporting latest innovations and future of AI. The chapters include theory and application on all aspects of artificial intelligence, from classical to intelligent scope. We hope that readers find the book interesting and valuable; it provides the state-of-the-art intelligent methods and techniques for solving real-world problems along with a vision of the future research.

Serious Games in Personalized Learning investigates game-based teaching and learning at a time when learning and training systems are increasingly integrating serious games, machine-learning artificial intelligence models, and adaptive technologies. Game-based education provides rare data for measuring, assessing, and evaluating not just a game’s effectiveness but the acquisition of information and knowledge that a student may gain through playing a learning game. This book synthesizes contemporary research, frameworks, and models centered on the design and delivery of serious games that truly personalize the learning experience. Scholars of educational technology, instructional design, human performance, and more will find a comprehensive guide to the history, practical implications, and data-collection potential inherent to these fast-evolving tools.

Most of the modern machine learning models, based on deep learning techniques, depend on carefully curated and cleanly labelled training sets to be reliably trained and deployed. However, the expensive labelling process involved in the acquisition of such training sets limits the number and size of datasets available to build new models, slowing down progress in the field. Alternatively, many poorly curated training sets containing noisy labels are readily available to be used to build new models. However, the successful exploration of such noisy-label training sets depends on the development of algorithms and models that are robust to these noisy labels.Machine learning and Noisy Labels: Definitions, Theory, Techniques and Solutions defines different types of label noise, introduces the theory behind the problem, presents the main techniques that enable the effective use of noisy-label training sets, and explains the most accurate methods developed in the field.This book is an ideal introduction to machine learning with noisy labels suitable for senior undergraduates, post graduate students, researchers and practitioners using, and researching into, machine learning methods. - Shows how to design and reproduce regression, classification and segmentation models using large-scale noisy-label training sets - Gives an understanding of the theory of, and motivation for, noisy-label learning - Shows how to classify noisy-label learning methods into a set of core techniques

This is the first book-length presentation and defense of a new theory of human and machine cognition, according to which human persons are superminds. Superminds are capable of processing information not only at and below the level of Turing machines (standard computers), but above that level (the "Turing Limit"), as information processing devices that have not yet been (and perhaps can never be) built, but have been mathematically specified; these devices are known as super-Turing machines or hypercomputers. Superminds, as explained herein, also have properties no machine, whether above or below the Turing Limit, can have. The present book is the third and pivotal volume in Bringsjord's supermind quartet; the first two books were What Robots Can and Can't Be (Kluwer) and AI and Literary Creativity (Lawrence Erlbaum). The final chapter of this book offers eight prescriptions for the concrete practice of AI and cognitive science in light of the fact that we are superminds.

This book addresses two fundamental issues of motor control for both humans and robots: kinematic redundancy and the posture/movement problem. It blends traditional robotic constrained-optimal approaches with neuroscientific and evidence-based principles, proposing a “Task-space Separation Principle,” a novel scheme for planning both posture and movement in redundant manipulators. The proposed framework is first tested in simulation and then compared with experimental motor strategies displayed by humans during redundant pointing tasks. The book also shows how this model builds on and expands traditional formulations such as the Passive Motion Paradigm and the Equilibrium Point Theory. Lastly, breaking with the neuroscientific tradition of planar movements and linear(ized) kinematics, the theoretical formulation and experimental scenarios are set in the nonlinear space of 3D rotations which are essential for wrist motions, a somewhat neglected area despite its importance in daily tasks.

This edited volume provides a biosemiotic analysis of the ecological relationship between food and medicine. Drawing on the origins of semiotics in medicine, this collection proposes innovative ways of considering aliments and treatments. Considering the ever-evolving character of our understanding of meaning-making in biology, and considering the keen popular interest in issues relating to food and medicines - fueled by an increasing body of interdisciplinary knowledge - the contributions here provide diverse insights and arguments into the larger ecology of organisms’ engagement with and transformation through taking in matter. Bodies interpret molecules, enzymes, and alkaloids they intentionally and unintentionally come in contact with according to their pre-existing receptors. But their receptors are also changed by the experience. Once the body has identified a particular substance, it responds by initiating semiotic sequences and negotiations that fulfill vital functions for the organism at macro-, meso-, and micro-scales. Human abilities to distill and extract the living world into highly refined foods and medicines, however, have created substances far more potent than their counterparts in our historical evolution. Many of these substances also lack certain accompanying proteins, enzymes, and alkaloids that otherwise aid digestion or protect against side-effects in active extracted chemicals. Human biology has yet to catch up with human inventions such as supernormal foods and medicines that may flood receptors, overwhelming the body’s normal satiation mechanisms. This volume discusses how biosemioticians can come to terms with these networks of meaning, providing a valuable and provocative compendium for semioticians, medical researchers and practitioners, sociologists, cultural theorists, bioethicists and scholars investigating the interdisciplinary questions stemming from food and medicine.

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.