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This book—Mind, Body, and Digital Brains—focuses on both theoretical and empirical issues and joins contributions from different disciplines, concepts, and sensibilities, bringing together scholars from fields that at first glance may appear different—Neuroscience and Cognitive Neuroscience; Robotics, Computer Science, Deep Learning, and Information Processing Systems; Education, Philosophy, Law, and Psychology. All these research fields are held together by the very object to be discussed: a broad, articulate, and polyphonic reflection on the status of theories and fields of application of Digital Technologies and Artificial Intelligence, seen from the perspective of the digital mind, digital body, and digital brain. Scientific and humanistic issues will be considered through an interdisciplinary point of view, with the purpose of deepening emerging trends about various disciplines. This book offers a framework for different perspectives and, at the same time, a platform for discussion aimed not only at experts, but also at a non-specialist public interested in the digital revolution. The digital revolution is emerging from the intertwining of ethical, philosophical, and technological aspects, which concern several general issues as cooperation, law, and environment, but also specialized as cybersecurity or algorithmic citizenship. More questions arise, concerning which opportunities and risks are associated with the new scenarios, what idea of humanity is emerging from the increasingly widespread use of Artificial Intelligence technologies, and what idea of integrated science should we promote to accompany the ongoing transformations.

Artificial Psychology (AP) is a highly multidisciplinary field of study in psychology. AP tries to solve problems which occur when psychologists do research and need a robust analysis method. Conventional statistical approaches have deep rooted limitations. These approaches are excellent on paper but often fail to model the real world. Mind researchers have been trying to overcome this by simplifying the models being studied. This stance has not received much practical attention recently. Promoting and improving artificial intelligence helps mind researchers to find a holistic model of mental models. This development achieves this goal by using multiple perspectives and multiple data sets together with interactive, and realistic models. In this book, the methodology of approximate inference in psychological research from a theoretical and practical perspective has been considered. Quantitative variable-oriented methodology and qualitative case-oriented methods are both used to explainthe set-oriented methodology and this book combines the precision of quantitative methods with information from qualitative methods. This is a book that many researchers can use to expand and deepen their psychological research and is a book which can be useful to postgraduate students. The reader does not need an in-depth knowledge of mathematics or statistics because statistical and mathematical intuitions are key here and they will be learned through practice. What is important is to understand and use the new application of the methods for finding new, dynamic and realistic interpretations. This book incorporates theoretical fuzzy inference and deep machine learning algorithms in practice. This is the kind of book that we wished we had had when we were students. This book covers at least some of the most important issues in mind research including uncertainty, fuzziness, continuity, complexity and high dimensionality which are inherent to mind data. These are elements of artificialpsychology. This book implements models using R software.

This Handbook offers the first comprehensive reference guide to the interdisciplinary field of abductive cognition, providing readers with extensive information on the process of reasoning to hypotheses in humans, animals, and in computational machines. It highlights the role of abduction in both theory practice: in generating and testing hypotheses and explanatory functions for various purposes and as an educational device. It merges logical, cognitive, epistemological and philosophical perspectives with more practical needs relating to the application of abduction across various disciplines and practices, such as in diagnosis, creative reasoning, scientific discovery, diagrammatic and ignorance-based cognition, and adversarial strategies. It also discusses the inferential role of models in hypothetical reasoning, abduction and creativity, including the process of development, implementation and manipulation for different scientific and technological purposes. Written by a group ofinternationally renowned experts in philosophy, logic, general epistemology, mathematics, cognitive, and computer science, as well as life sciences, engineering, architecture, and economics, the Handbook of Abductive Cognition offers a unique reference guide for readers approaching the process of reasoning to hypotheses from different perspectives and for various theoretical and practical purposes. Numerous diagrams, schemes and other visual representations are included to promote a better understanding of the relevant concepts and to make concepts highly accessible to an audience of scholars and students with different scientific backgrounds.

This book explores the cognitive plausibility of computational language models and why it's an important factor in their development and evaluation. The authors present the idea that more can be learned about cognitive plausibility of computational language models by linking signals of cognitive processing load in humans to interpretability methods that allow for exploration of the hidden mechanisms of neural models. The book identifies limitations when applying the existing methodology for representational analyses to contextualized settings and critiques the current emphasis on form over more grounded approaches to modeling language. The authors discuss how novel techniques for transfer and curriculum learning could lead to cognitively more plausible generalization capabilities in models. The book also highlights the importance of instance-level evaluation and includes thorough discussion of the ethical considerations that may arise throughout the various stages of cognitive plausibility research.

Menschen schreiben sich ein Selbst zu. Die Wissenschaften haben Mühe zu sagen, was das ist und wie es zustande kommt. Sie sind sich aber sicher, dass wir es haben und brauchen. Für Forscher ist es jedoch kaum greifbar und deshalb ein verflixtes Problem. Daran wird die Entwicklung einer starken KI scheitern, Menschen bleiben etwas anderes.

AI (Artificial Intelligence) is predicted to radically change teaching and learning in both schools and industry causing radical disruption of work. AI can support well-being initiatives and lifelong learning but educational institutions and companies need to take the changing technology into account. Moving towards AI supported by digital tools requires a dramatic shift in the concept of learning, expertise and the businesses built off of it. Based on the latest research on AI and how it is changing learning and education, this book will focus on the enormous opportunities to expand educational settings with AI for learning in and beyond the traditional classroom. This open access book also introduces ethical challenges related to learning and education, while connecting human learning and machine learning. This book will be of use to a variety of readers, including researchers, AI users, companies and policy makers.

This book illustrates the design, development, and evaluation of personalized intelligent tutoring systems that emulate human cognitive intelligence by incorporating artificial intelligence. Artificial intelligence is an advanced field of research. It is particularly used in the field of education to increase the effectiveness of teaching and learning techniques. With the advancement of internet technology, there is a rapid growth in web based distance learning modality. This mode of learning is better known as the e-learning system. These systems present low intelligence because they offer a pre-identified learning frame to their learners. The advantage of these systems is to offer to learn anytime and anyplace without putting emphasis on a learner's needs, competency level, and previous knowledge. Every learner has different grasping levels, previous knowledge, and preferred mode of learning, and hence, the learning process of one individual may significantly vary from other individuals. This book provides a complete reference for students, researchers, and industry practitioners interested in keeping abreast of recent advancements in this field. It encompasses cognitive intelligence and artificial intelligence which are very important for deriving a roadmap for future research on intelligent systems.

人と自然にコミュニケーションするAI，ロボットを設計するための入門書． コグニティブインタラクションは，従来の認知科学や情報科学だけではなしえていない，「状況に応じて，人と自然に，かつ持続的にインタラクションが可能な人工物を設計するための基礎理論」です．本書は，その入門書です． AIやロボットが人の社会生活にとけこむためには，人と協調行動をとれなければいけません．それには，コミュニケーション，つまり，意思疎通の能力が欠かせません．しかし，言語によるコミュニケーションよりも，非言語情報によるインタラクションのほうが重要になることがよくあります．相手が人であれ，動物であれ，AIやロボットなどの人工物であれ，人は相手の意図や欲求などの心的状況を読み取り，それに適応した行動をとるという，コグニティブ（認知的）インタラクションを繰り返すことで，円滑に対話を行っていると考えられるからです． 第1章では，人とAIのインタラクションについて，人どうしのインタラクションや人と動物のインタラクションをベースに考える枠組を説明しています．第2章では，インタラクションを分析していくための概念や方法を説明しています．続く第3章では，取得したデータをモデルベースで分析するために必要な，データの表現方法について説明しています．最後の第4章では，第3章までに学んだ基礎的な概念や方法を用いて，実際にどのようなインタラクションの分析が可能なのかを，これまでの事例の中から特に興味深いものに絞って説明しています． 本書を読むことで，人どうし，あるいは人と動物の間のインタラクションで起きていることを理解するためだけでなく，人と自然にコミュニケーションするAI，ロボット，そのほかの人工物を設計するための基本がわかります．

This volume constitutes the proceedings of the 28th International Conference on Collaboration Technologies and Social Computing, CollabTech 2022, held in Santiago, Chile during November 8–11, 2022. The 18 full and 4 work-in-process papers presented in this volume were selected from 37 submissions and underwent careful double-blind peer review. The papers focus on innovative technical, human and organizational approaches to expand collaboration support including computer science, management science, design science, cognitive and social science.

Im Kern dieser Arbeit geht es um das Begreifen von Kognition. Der Kognitionsbegriff wird zur Schlüsselkategorie in den basalen Gedanken- bzw. Modellgebäuden und den daraus entwickelten Algorithmen. Es ist eine Arbeit, die unter anderem die philosophischen Positionen des Reduktionismus, Funktionalismus und Konstruktivismus mit einer kognitiven Theorie so in Verbindung bringt, um diese erkenntnistheoretischen Ismen mit den Erkenntnissen einer technologisierten Kognitionswissenschaft zu synchronisieren und als algorithmisierte Theorie im Rahmen eines Entwicklungsprojekt als artifizielle Kognition zu realisieren. Die Arbeit ist somit theoretisch fundiert und praktisch orientiert.

In recent years there has been increasing excitement concerning the potential of Artificial Intelligence to transform human society. This book addresses the leading edge of research in this area. The research described aims to address present incompatibilities of Human and Machine reasoning and learning approaches. According to the influential US funding agency DARPA (originator of the Internet and Self-Driving Cars) this new area represents the Third Wave of Artificial Intelligence (3AI, 2020s-2030s), and is being actively investigated in the US, Europe and China. The chapters of this book have been authored by a mixture of UK and other international specialists. Some of the key questions addressed by the Human-Like Computing programme include how AI systems might 1) explain their decisions effectively, 2) interact with human beings in natural language, 3) learn from small numbers of examples and 4) learn with minimal supervision. Solving such fundamental problems involves new foundational research in both the Psychology of perception and interaction as well as the development of novel algorithmic approaches in Artificial Intelligence.

Das Handbuch gibt einen umfassenden Überblick über grundlegende und moderne Methoden und Anwendungen der Künstlichen Intelligenz. Die Themen reichen von Kognition, Neuronale Netze, über Maschinelles Lernen, Sprach-und Bildverarbeitung bis hin zu Robotik und Software-Agenten und Anwendungen in der Industrie 4.0. Herausgeber und Autoren sind Spezialisten auf den jeweiligen Teilgebieten. Ein Muss für jeden, der sich mit dem Thema KI befasst.

An expert on mind considers how animals and smart machines measure up to human intelligence.Octopuses can open jars to get food, and chimpanzees can plan for the future. An IBM computer named Watson won on Jeopardy! and Alexa knows our favorite songs. But do animals and smart machines really have intelligence comparable to that of humans? In Bots and Beasts, Paul Thagard looks at how computers ('bots') and animals measure up to the minds of people, offering the first systematic comparison of intelligence across machines, animals, and humans. Thagard explains that human intelligence is more than IQ and encompasses such features as problem solving, decision making, and creativity. He uses a checklist of twenty characteristics of human intelligence to evaluate the smartest machines--including Watson, AlphaZero, virtual assistants, and self-driving cars--and the most intelligent animals--including octopuses, dogs, dolphins, bees, and chimpanzees. Neither a romantic enthusiast for nonhuman intelligence nor a skeptical killjoy, Thagard offers a clear assessment. He discusses hotly debated issues about animal intelligence concerning bacterial consciousness, fish pain, and dog jealousy. He evaluates the plausibility of achieving human-level artificial intelligence and considers ethical and policy issues. A full appreciation of human minds reveals that current bots and beasts fall far short of human capabilities.

The use of cognitive science in creating stories, languages, visuals, and characters is known as narrative generation, and it has become a trending area of study. Applying artificial intelligence (AI) techniques to story development has caught the attention of professionals and researchers; however, few studies have inherited techniques used in previous literary methods and related research in social sciences. Implementing previous narratology theories to current narrative generation systems is a research area that remains unexplored. Bridging the Gap Between AI, Cognitive Science, and Narratology With Narrative Generation is a collection of innovative research on the analysis of current practices in narrative generation systems by combining previous theories in narratology and literature with current methods of AI. The book bridges the gap between AI, cognitive science, and narratology with narrative generation in a broad sense, including other content generation, such as a novels, poems, movies, computer games, and advertisements. The book emphasizes that an important method for bridging the gap is based on designing and implementing computer programs using knowledge and methods of narratology and literary theories. In order to present an organic, systematic, and integrated combination of both the fields to develop a new research area, namely post-narratology, this book has an important place in the creation of a new research area and has an impact on both narrative generation studies, including AI and cognitive science, and narrative studies, including narratology and literary theories. It is ideally designed for academicians, researchers, and students, as well as enterprise practitioners, engineers, and creators of diverse content generation fields such as advertising production, computer game creation, comic and manga writing, and movie production.

This book presents a theoretical critical appraisal of the Mechanistic Theory of Human Cognition (MTHC), which is one of the most popular major theories in the contemporary field of cognitive science. It analyses and evaluates whether MTHC provides a unifying account of human cognition and its explanation. The book presents a systematic investigation of the internal and external consistency of the theory, as well as a systematic comparison with other contemporary major theories in the field. In this sense, it provides a fresh look at more recent major theoretical debates in this area of scientific research and a rigorous analysis of one of its most central major theories. Rigorous theoretical work is integrated with objective consideration of relevant empirical evidence, making the discussions robust and clear. As a result, the book shows that MTHC provides a significant theoretical contribution for the field of cognitive science. The content is useful for those interested in theoretical and empirical issues concerning major theories in the contemporary field of cognitive science.

This book provides a systematic and comprehensive overview of machine learning with cognitive science methods and technologies which have played an important role at the core of practical solutions for a wide scope of tasks between handheld apps, industrial process control, autonomous vehicles, environmental policies, life sciences, playing computer games, computational theory, and engineering development. The chapters in this book focus on readers interested in machine learning, cognitive and neuro-inspired computational systems – theories, mechanisms, and architecture, which underline human and animal behaviour, and their application to conscious and intelligent systems. In the current version, it focuses on the successful implementation and step-by-step explanation of practical applications of the domain. It also offers a wide range of inspiring and interesting cutting-edge contributions to applications of machine learning and cognitive science such as healthcare products, medical electronics, and gaming. Overall, this book provides valuable information on effective, cutting-edge techniques and approaches for students, researchers, practitioners, and academicians working in the field of AI, neural network, machine learning, and cognitive science. Furthermore, the purpose of this book is to address the interests of a broad spectrum of practitioners, students, and researchers, who are interested in applying machine learning and cognitive science methods in their respective domains.

This book addresses the issue of cognitive semantics'aspects that cannot be represented by traditional digital and logical means. The problem of creating cognitive semantics can be resolved in an indirect way. The electromagnetic waves, quantum fields, beam of light, chaos control, relativistic theory, cosmic string recognition, category theory, group theory, and so on can be used for this aim. Since the term artificial intelligence (AI) appeared, various versions of logic have been created; many heuristics for neural networks deep learning have been made; new nature-like algorithms have been suggested. At the same time, the initial digital, logical, and neural network principles of representation of knowledge in AI systems have not changed a lot. The researches of these aspects of cognitive semantics of AI are based on the author's convergent methodology, which provides the necessary conditions for purposeful and sustainable convergence of decision-making.

ビッグデータやIoTなども巻き込んで発展し続ける人工知能研究は今後、社会にどう受け入れられていくか？　その鍵について著者は、社会システムを構成する人工物に「心的機能」が備わることと説き、それを「ココロの創成課題」と呼ぶ。 本書は、その課題の実現を目指して研究を行ってきた著者の人工知能研究の足跡（動画像処理・強化学習・身体性・情動・共感）をたどりながら、ロボカップ研究（認知発達ロボティクス）から導き出された認知の問題意識などの重要性と成果、そして課題を解説する。各分野のキーパーソンとの対話やその知見なども紹介。

While cognitive informatics and natural intelligence are receiving greater attention by researchers, multidisciplinary approaches still struggle with fundamental problems involving psychology and neurobiological processes of the brain. Examining the difficulties of certain approaches using the tools already available is vital for propelling knowledge forward and making further strides. Innovations, Algorithms, and Applications in Cognitive Informatics and Natural Intelligence is a collection of innovative research that examines the enhancement of human cognitive performance using emerging technologies. Featuring research on topics such as parallel computing, neuroscience, and signal processing, this book is ideally designed for engineers, computer scientists, programmers, academicians, researchers, and students.

This book introduces three key issues: (i) development of a gradient-free method to enable multi-objective self-optimization; (ii) development of a reinforcement learning strategy to carry out self-learning and finally, (iii) experimental evaluation and validation in two micromachining processes (i.e., micro-milling and micro-drilling). The computational architecture (modular, network and reconfigurable for real-time monitoring and control) takes into account the analysis of different types of sensors, processing strategies and methodologies for extracting behavior patterns from representative process'signals. The reconfiguration capability and portability of this architecture are supported by two major levels: the cognitive level (core) and the executive level (direct data exchange with the process). At the same time, the architecture includes different operating modes that interact with the process to be monitored and/or controlled. The cognitive level includes three fundamentalmodes such as modeling, optimization and learning, which are necessary for decision-making (in the form of control signals) and for the real-time experimental characterization of complex processes. In the specific case of the micromachining processes, a series of models based on linear regression, nonlinear regression and artificial intelligence techniques were obtained. On the other hand, the executive level has a constant interaction with the process to be monitored and/or controlled. This level receives the configuration and parameterization from the cognitive level to perform the desired monitoring and control tasks.

Is Apple conscious? Could a cyber–human system sense a potential terrorist attack? Or make diagnosing a rare and little-known disease routine? Computers are not replacing us: they are enhancing us. Different intelligences are joining together to do things we thought were impossible. Whether it's devising innovations to tackle climate change, helping job seekers and employers find one another, or identifying the outbreak of a serious disease, groups of humans and machines are already working together to solve all sorts of problems. And they will do a lot more. The future will be like another world – a place where we'll think differently. In many ways, we are already there.

In order to bridge the gap between artificial and synthetic intelligence, we must first understand our own intelligence.'What is intelligence?'might appear as a simple question, but many great minds have agreed that there is no singular answer. Unlocking Consciousness attempts to examine this central question through exploring the convergence of computing, philosophy, cognitive neuroscience and biogenetics.The book is the first of its kind to compare comprehensive definitions of both information and intelligence, an essential component to the advancement of computing into the realms of artificial intelligence. In examining explanations for intelligence, consciousness, memory and meaning from the perspective of a computer scientist, it offers routes that can be taken to augment natural and artificial intelligence, improving our own individual abilities, and even considering the potential for creating a prosthetic brain.Unlocking Consciousness demonstrates that understanding intelligence is not just for the benefit of computer scientists, it is also of great value to those working in evolutionary, molecular and systems biology, cognitive neuroscience, genetics and biotechnology. In unlocking the secrets of intelligence and laying out the methods of which information is structured and processed, we can unlock a completely new theory of consciousness.For additional published articles and appendices referenced in this title, readers can visit www.brainmindforum.org/ for further information.

The language of business is the language of dreams, but the language of war is the language of nightmare made real. Yet business dreams of driverless cars on intelligent roads, and of other real-time critical systems under the control of algorithmic entities, have much of war about them. Such systems, including military institutions at the tactical, operational and strategic scales, act on rapidly-shifting roadway topologies whose ‘traffic rules'can rapidly change. War is never without both casualty and collateral damage, and realtime critical systems of any nature will inevitably partake of fog-of-war and frictional challenges almost exactly similar to those that have made warfare intractable for modern states. Into the world of Carl von Clausewitz, John Boyd, Mao Tse-Tung, Vo Nguyen Giap and Genghis Khan, come the brash, bright-eyed techies of Alphabet, Microsoft, Amazon, and Uber who forthrightly step in where a phalanx of angels has not feared to tread, but treaded badly indeed. In this book we use cutting-edge tools from information and control theories to examine canonical and idiosyncratic failure modes of real-time cognitive systems facing fog-of-war and frictional constraints. In sum, nobody ever navigates, or can navigate, the landscapes of Carl von Clausewitz unscathed.

This book highlights the interdisciplinary study of cognition, mind and behavior from an information processing perspective, and describes related applications to health informatics. The respective chapters address health problem-solving and education, decision support systems, user-centered interfaces, and the design and use of controlled medical terminologies. Reflecting cutting-edge research on computational methods – including theory, algorithms, numerical simulation, error and uncertainty analysis, and their applications – the book offers a valuable resource for doctoral students and researchers in the fields of Computer Science and Engineering.

There are many different approaches to understanding human consciousness. By conducting research to better understand various biological mechanisms, these can be redefined and utilized for technological purposes. Advanced Research on Biologically Inspired Cognitive Architectures is an essential reference source for the latest scholarly research on the biological elements of human cognition and examines the applications of consciousness within computing environments. Featuring exhaustive coverage on a broad range of innovative topics and perspectives, such as artificial intelligence, bio-robotics, and human-computer interaction, this publication is ideally designed for academics, researchers, professionals, graduate students, and practitioners seeking current research on the exploration of the intricacies of consciousness and different approaches of perception.

It is well known that every animal species obeys Darwin's law of evolution, which requires permanent adaptation of animals to their environment. To be precise, every species except man, who behaves exactly contrariwise, adapting the workplace to himself in order to survive. For that he generally enjoys a particular gift of nature: intelligence.That reverse adaptation, which accumulated over centuries, led to what we call'progress'. This was enhanced by the development of machines which began to be also intelligent and now compete fiercely with humans through the development of an'artificial intelligence'.Some famous people in the world of science and technology recently sounded the alarm about the threats which these improvements are posing. They invoked a possible domination by the machines due to their uncontrolled superior intelligence, potentially leading us into a certain kind of slavery.In this book we take a look at this new challenge of the human brain versus the computer. The brain is a very complex organ and we are just beginning to understand how it works; many things remain mysterious and can lead to surprises. We will see how current investigations bring new information about this strange organ.We will also see how the'artificial challenger'plans to win the battle, how computers are getting more and more powerful and subtle as the AI advances. Would a transfer of minds in a machine be possible? Would the computer be capable of a self, nonneuromorphic intelligence? These questions are now open.Who will win? We do not know yet. But it is certain that many things are going to change in our lives in the very near future.

話題の人工知能の粋を集めた学会誌 話題の人工知能の粋を集めた学会誌人工知能に関する専門家からのさまざまな研究結果、レポートを載せ、この分野における最新の情報を掲載しています。 ★このような方におすすめ 人工知能に関心のある一般の方、人工知能の研究者 ●主要目次● 巻頭言 特集：「認知的インタラクションデザイン学」 特集：「認知科学と記号創発ロボティクス：実世界情報に基づく知覚的シンボルシステムの構成論的理解に向けて」 研究会報告 レクチャーシリーズ：「つながりが創発するイノベーション」〔第5回〕 SFショート・ショート〔第19回〕 特集：「人工知能分野における博士論文」 論文特集：「知的対話システム」 論文特集：「実践Linked Open Data」 論文特集：「2014年度近未来チャレンジ」 私のブックマーク 学生フォーラム 会議報告 会告

The human brain has some capabilities that the brains of other animals lack. It is to these distinctive capabilities that our species owes its dominant position. Other animals have stronger muscles or sharper claws, but we have cleverer brains. If machine brains one day come to surpass human brains in general intelligence, then this new superintelligence could become very powerful. As the fate of the gorillas now depends more on us humans than on the gorillas themselves, so the fate of our species then would come to depend on the actions of the machine superintelligence. But we have one advantage: we get to make the first move. Will it be possible to construct a seed AI or otherwise to engineer initial conditions so as to make an intelligence explosion survivable? How could one achieve a controlled detonation? To get closer to an answer to this question, we must make our way through a fascinating landscape of topics and considerations. Read the book and learn about oracles, genies, singletons; about boxing methods, tripwires, and mind crime; about humanity's cosmic endowment and differential technological development; indirect normativity, instrumental convergence, whole brain emulation and technology couplings; Malthusian economics and dystopian evolution; artificial intelligence, and biological cognitive enhancement, and collective intelligence. This profoundly ambitious and original book picks its way carefully through a vast tract of forbiddingly difficult intellectual terrain. Yet the writing is so lucid that it somehow makes it all seem easy. After an utterly engrossing journey that takes us to the frontiers of thinking about the human condition and the future of intelligent life, we find in Nick Bostrom's work nothing less than a reconceptualization of the essential task of our time.

The work outlines a detailed blueprint for the creation of an Artificial General Intelligence system with capability at the human level and ultimately beyond, according to the Cog Prime AGI design and the Open Cog software architecture.

The work outlines a novel conceptual and theoretical framework for understanding Artificial General Intelligence and based on this framework outlines a practical roadmap for the development of AGI with capability at the human level and ultimately beyond.

An important collection of studies providing a fresh and original perspective on the nature of mind, including thoughtful and detailed arguments that explain why the prevailing paradigm - the computational conception of language and mentality - can no longer be sustained. An alternative approach is advanced, inspired by the work of Charles S. Peirce, according to which minds are sign-using (or `semiotic') systems, which in turn generates distinctions between different kinds of minds and overcomes problems that burden more familiar alternatives. Unlike conceptions of minds as machines, this novel approach has obvious evolutionary implications, where differences in semiotic abilities tend to distinguish the species. From this point of view, the scope and limits of computer and AI systems can be more adequately appraised and alternative accounts of consciousness and cognition can be more thoroughly criticised. Readership: Intermediate and advanced students of computer science, AI, cognitive science, and all students of the philosophy of the mind.

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.

`Intellectics'seeks to understand the functions, structure and operation of the human intellect and to test artificial systems to see the extent to which they can substitute or complement such functions. The word itself was introduced in the early 1980s by Wolfgang Bibel to describe the united fields of artificial intelligence and cognitive science. The book collects papers by distinguished researchers, colleagues and former students of Bibel's, all of whom have worked together with him, and who present their work to him here to mark his 60th birthday. The papers discuss significant issues in intellectics and computational logic, ranging across automated deduction, logic programming, the logic-based approach to intellectics, cognitive robotics, knowledge representation and reasoning. Each paper contains new, previously unpublished, reviewed results. The collection is a state of the art account of the current capabilities and limitations of a computational-logic-based approach to intellectics. Readership: Researchers who are convinced that the intelligent behaviour of machines should be based on a rigid formal treatment of knowledge representation and reasoning.

0. 0 Psychology versus Complex Systems Science Over the last century, psychology has become much less of an art and much more of a science. Philosophical speculation is out; data collection is in. In many ways this has been a very positive trend. Cognitive science (Mandler, 1985) has given us scientific analyses of a variety of intelligent behaviors: short-term memory, language processing, vision processing, etc. And thanks to molecular psychology (Franklin, 1985), we now have a rudimentary understanding of the chemical processes underlying personality and mental illness. However, there is a growing feeling-particularly among non-psychologists (see e. g. Sommerhoff, 1990) - that, with the new emphasis on data collection, something important has been lost. Very little attention is paid to the question of how it all fits together. The early psychologists, and the classical philosophers of mind, were concerned with the general nature of mentality as much as with the mechanisms underlying specific phenomena. But the new, scientific psychology has made disappointingly little progress toward the resolution of these more general questions. One way to deal with this complaint is to dismiss the questions themselves. After all, one might argue, a scientific psychology cannot be expected to deal with fuzzy philosophical questions that probably have little empirical signifi­ cance. It is interesting that behaviorists and cognitive scientists tend to be in agreement regarding the question of the overall structure of the mind.

This book contains a selection of papers presented at the International Workshop Machine Learning, Meta-Reasoning and Logics held in Hotel de Mar in Sesimbra, Portugal, 15-17 February 1988. All the papers were edited afterwards. The Workshop encompassed several fields of Artificial Intelligence: Machine Learning, Belief Revision, Meta-Reasoning and Logics. The objective of this Workshop was not only to address the common issues in these areas, but also to examine how to elaborate cognitive architectures for systems capable of learning from experience, revising their beliefs and reasoning about what they know. Acknowledgements The editing of this book has been supported by COST-13 Project Machine Learning and Knowledge Acquisition funded by the Commission o/the European Communities which has covered a substantial part of the costs. Other sponsors who have supported this work were Junta Nacional de lnvestiga~ao Cientlfica (JNICT), lnstituto Nacional de lnvestiga~ao Cientlfica (INIC), Funda~ao Calouste Gulbenkian. I wish to express my gratitude to all these institutions. Finally my special thanks to Paula Pereira and AnaN ogueira for their help in preparing this volume. This work included retyping all the texts and preparing the camera-ready copy. Introduction 1 1. Meta-Reasoning and Machine Learning The first chapter is concerned with the role meta-reasoning plays in intelligent systems capable of learning. As we can see from the papers that appear in this chapter, there are basically two different schools of thought.

One of the most enjoyable experiences in science is hearing a simple but novel idea which instantly rings true, and whose consequences then begin to unfold in unforeseen directions. For me, this book presents such an idea and several of its ramifications. This book is concerned with machine learning. It focuses on a ques­ tion that is central to understanding how computers might learn:'how can a computer acquire the definition of some general concept by abstracting from specific training instances of the concept?'Although this question of how to automatically generalize from examples has been considered by many researchers over several decades, it remains only partly answered. The approach developed in this book, based on Haym Hirsh's Ph.D. dis­ sertation, leads to an algorithm which efficiently and exhaustively searches a space of hypotheses (possible generalizations of the data) to find all maxi­ mally consistent hypotheses, even in the presence of certain types of incon­ sistencies in the data. More generally, it provides a framework for integrat­ ing different types of constraints (e.g., training examples, prior knowledge) which allow the learner to reduce the set of hypotheses under consideration.

The book brings into relief the variety of approaches and disciplines that have informed the quest for a theory of cognition. The center of interest are the historical, geographical, and theoretical peripheries of classic AI's mainstream research program. The twelve chapters bring back into focus the variety of strategies and theoretical questions that researchers explored while working toward a scientific theory of cognition and pre-cognition.The volume is organized in four parts, each one including three essays. The first one deals with cybernetics, the approach that may be considered as the most important periphery of classic AI research. The second part focuses on the geographical periphery of AI research. It examines how the theories and techniques developed on AI's home ground were translated into countries with different cultures and traditions: Italy, France, and the Soviet Union. The third part focuses on AI's periphery understood in the cultural and historical meaning of the term. It contains essays that locate some of the central concepts of AI, like representation and computability, within a broader philosophical (Descartes, Aristotle, Leibniz) and technical background (programming theory and practice). The fourth and final part of the volume is focused directly on the limitation of Turing's classic computability theory and its possible alternatives, some of which were studied in the early years of AI's research (e.g. Ashby's re-entrant information model), while others have been intensely studied in recent times (quantum automata).

'This book provides emerging research topics in cognitive informatics research with a focus on such topics as reducing cognitive overload, real-time process algebra, and neural networks for iris recognition, emotion recognition in speech, and the classification of musical chords'--Provided by publisher.

This book has been inspired by the portfolio of recent scientific outputs from a range of European and national research initiatives in cognitive science. It presents an overview of recent developments in cognition research and unites the various emerging research strands within a single text as a reference point for progress in the subject. It also provides guidance for new researchers on the breadth of the field and the interconnections between the various research strands identified here.

'This book covers issue of cognitive informatics with a transdisciplinary enquiry of cognitive and information sciences that investigates the internal information processing mechanisms and processes of the brain and natural intelligence, and their engineering applications via an interdisciplinary approach'--Provided by publisher.

There is a basic perplexity in our times. On the one hand, we?nd a blind trust in technology and rationalism. In our neo-liberalistically dominated world only what can be rapidly exploited and commercialized seems to count. The only opposing reaction to this kind of rationalism is an extreme rejection of all kinds of reasoning, and sometimes attendant religious fundamentalism. But instead of re?ecting on the limits and possibilites of reasoning, dialogue is replaced by a demagogic struggle between cultures. One cause of the blind trust in technology is misunderstandings about the sign- cance and the application of theories in the reception of the so-called Enlightenment. The Enlightenment is essentially characterized by two forces: (i) the conception of society as a social contract and (ii) the new science (New- nian physics, etc.). But as a result we lost ground: Atomistic individualism nourished the illusion of a self-contained ego prior to man's entering into a shared inter-subjective world. And in the new science, our constructions of reality became autonomous and indep- dent of our interventions. Thus we became caught in the inherent dynamism of our computational constructions of reality. Science, as it is applied today, operates with far too simple parameters and model-theoretic constructions – erroneously taking the latter (the models) as literal descriptions of reality.

The Handbook of Cognitive Science provides an overview of recent developments in cognition research, relying upon non-classical approaches. Cognition is explained as the continuous interplay between brain, body, and environment, without relying on classical notions of computations and representation to explain cognition. The handbook serves as a valuable companion for readers interested in foundational aspects of cognitive science, and neuroscience and the philosophy of mind. The handbook begins with an introduction to embodied cognitive science, and then breaks up the chapters into separate sections on conceptual issues, formal approaches, embodiment in perception and action, embodiment from an artificial perspective, embodied meaning, and emotion and consciousness. Contributors to the book represent research overviews from around the globe including the US, UK, Spain, Germany, Switzerland, France, Sweden, and the Netherlands.

“Only a small community has concentratedon general intelligence. No one has tried to make a thinking machine... The bottom line is that we really haven't progressed too far toward a truly intelligent machine. We have collections of dumb specialists in small domains; the true majesty of general intelligence still awaits our attack.... We have got to get back to the deepest questions of AI and general intelligence... ” –MarvinMinsky as interviewed in Hal's Legacy, edited by David Stork, 2000. Our goal in creating this edited volume has been to?ll an apparent gap in the scienti?c literature, by providing a coherent presentation of a body of contemporary research that, in spite of its integral importance, has hitherto kept a very low pro?le within the scienti?c and intellectual community. This body of work has not been given a name before; in this book we christen it “Arti?cial General Intelligence” (AGI). What distinguishes AGI work from run-of-the-mill “arti?cial intelligence” research is that it is explicitly focused on engineering general intelligence in the short term. We have been active researchers in the AGI?eld for many years, and it has been a pleasure to gather together papers from our colleagues working on related ideas from their own perspectives. In the Introduction we give a conceptual overview of the AGI?eld, and also summarize and interrelate the key ideas of the papers in the subsequent chapters.

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.

Cognitive Systems - Information Processing Meets Brain Science presents an overview of the exciting, truly multidisciplinary research by neuroscientists and systems engineers in the emerging field of cognitive systems, providing a cross-disciplinary examination of this cutting-edge area of scientific research. This is a great example of where research in very different disciplines touches to create a new emerging area of research. The book illustrates some of the technical developments that could arise from our growing understanding of how living cognitive systems behave, and the ability to use that knowledge in the design of artificial systems. This unique book is of considerable interest to researchers and students in information science, neuroscience, psychology, engineering and adjacent fields. - Represents a remarkable collection of relevant experts from both the life sciences and computer science - Includes state-of-the-art reviews of topics in cognitive systems from both a life sciences and a computer science perspective - Discusses the impact of this research on our lives in the near future

Categorization, the basic cognitive process of arranging objects into categories, is a fundamental process in human and machine intelligence and is central to investigations and research in cognitive science. Until now, categorization has been approached from singular disciplinary perspectives with little overlap or communication between the disciplines involved (Linguistics, Psychology, Philosophy, Neuroscience, Computer Science, Cognitive Anthropology). Henri Cohen and Claire Lefebvre have gathered together a stellar collection of contributors in this unique, ambitious attempt to bring together converging disciplinary and conceptual perspectives on this topic.'Categorization is a key concept across the range of cognitive sciences, including linguistics and philosophy, yet hitherto it has been hard to find accounts that go beyond the concerns of one or two individual disciplines. The Handbook of Categorization in Cognitive Science provides just the sort of interdisciplinary approach that is necessary to synthesize knowledge from the different fields and provide the basis for future innovation.'Professor Bernard Comrie, Department of Linguistics, Max Planck Institute for Evolutionary Anthropology, Germany'Anyone concerned with language, semantics, or categorization will want to have this encyclopedic collection.'Professor Eleanor Rosch, Dept of Psychology, University of California, Berkeley, USA

'What is mind?''Can we build synthetic or artificial minds?'Think these questions are only reserved for Science Fiction? Well, not anymore. This collection presents a diverse overview of where the development of artificial minds is as the twenty first century begins. Examined from nearly all viewpoints, Visions of Mind includes perspectives from philosophy, psychology, cognitive science, social studies and artificial intelligence. This collection comes largely as a result of many conferences and symposiums conducted by many of the leading minds on this topic. At the core is Professor Aaron Sloman's symposium from the spring 2000 UK Society for Artificial Intelligence conference. Authors from that symposium, as well as others from around the world have updated their perspectives and contributed to this powerful book. The result is a multi-disciplinary approach to the long term problem of designing a human-like mind, whether for scientific, social, or engineering purposes. The topics addressed within this text are valuable to both artificial intelligence and cognitive science, and also to the academic disciplines that they draw on and feed. Among those disciplines are philosophy, computer science, and psychology

The interdisciplinary field of cognitive science brings together elements of cognitive psychology, mathematics, perception, and linguistics. Focusing on the main areas of exploration in this field today, Cognitive Science presents comprehensive overviews of research findings and discusses new cross-over areas of interest. Contributors represent the most senior and well-established names in the field. This volume serves as a high-level introduction, with sufficient breadth to be a graduate-level text, and enough depth to be a valued reference source to researchers.

The book includes all the background material required to understand the principles underlying intelligence, as well as enough detailed information on intelligent robotics and simulated agents so readers can begin experiments and projects on their own.By the mid-1980s researchers from artificial intelligence, computer science, brain and cognitive science, and psychology realized that the idea of computers as intelligent machines was inappropriate. The brain does not run'programs'; it does something entirely different. But what? Evolutionary theory says that the brain has evolved not to do mathematical proofs but to control our behavior, to ensure our survival. Researchers now agree that intelligence always manifests itself in behavior—thus it is behavior that we must understand. An exciting new field has grown around the study of behavior-based intelligence, also known as embodied cognitive science,'new AI,'and'behavior-based AI.'This book provides a systematic introduction to this new way of thinking. After discussing concepts and approaches such as subsumption architecture, Braitenberg vehicles, evolutionary robotics, artificial life, self-organization, and learning, the authors derive a set of principles and a coherent framework for the study of naturally and artificially intelligent systems, or autonomous agents. This framework is based on a synthetic methodology whose goal is understanding by designing and building.The book includes all the background material required to understand the principles underlying intelligence, as well as enough detailed information on intelligent robotics and simulated agents so readers can begin experiments and projects on their own. The reader is guided through a series of case studies that illustrate the design principles of embodied cognitive science.