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A concise introduction to a complex field, bringing together recent work in cognitive science and cognitive robotics to offer a solid grounding on key issues. This book offers a concise and accessible introduction to the emerging field of artificial cognitive systems. Cognition, both natural and artificial, is about anticipating the need for action and developing the capacity to predict the outcome of those actions. Drawing on artificial intelligence, developmental psychology, and cognitive neuroscience, the field of artificial cognitive systems has as its ultimate goal the creation of computer-based systems that can interact with humans and serve society in a variety of ways. This primer brings together recent work in cognitive science and cognitive robotics to offer readers a solid grounding on key issues. The book first develops a working definition of cognitive systems—broad enough to encompass multiple views of the subject and deep enough to help in the formulation of theories and models. It surveys the cognitivist, emergent, and hybrid paradigms of cognitive science and discusses cognitive architectures derived from them. It then turns to the key issues, with chapters devoted to autonomy, embodiment, learning and development, memory and prospection, knowledge and representation, and social cognition. Ideas are introduced in an intuitive, natural order, with an emphasis on the relationships among ideas and building to an overview of the field. The main text is straightforward and succinct; sidenotes drill deeper on specific topics and provide contextual links to further reading.
"This book presents recent research efforts in Artificial Intelligence about building artificial systems capable of performing cognitive tasks. A fundamental issue addressed in this book is if these cognitive processes can have any meaningfulness to the artificial system being built"--Provided by publisher.
Cognitive Design for Artificial Minds explains the crucial role that human cognition research plays in the design and realization of artificial intelligence systems, illustrating the steps necessary for the design of artificial models of cognition. It bridges the gap between the theoretical, experimental, and technological issues addressed in the context of AI of cognitive inspiration and computational cognitive science. Beginning with an overview of the historical, methodological, and technical issues in the field of cognitively inspired artificial intelligence, Lieto illustrates how the cognitive design approach has an important role to play in the development of intelligent AI technologies and plausible computational models of cognition. Introducing a unique perspective that draws upon Cybernetics and early AI principles, Lieto emphasizes the need for an equivalence between cognitive processes and implemented AI procedures, in order to realize biologically and cognitively inspired artificial minds. He also introduces the Minimal Cognitive Grid, a pragmatic method to rank the different degrees of biological and cognitive accuracy of artificial systems in order to project and predict their explanatory power with respect to the natural systems taken as a source of inspiration. Providing a comprehensive overview of cognitive design principles in constructing artificial minds, this text will be essential reading for students and researchers of artificial intelligence and cognitive science.
This comprehensive book gives an overview of how cognitive systems and artificial intelligence (AI) can be used in electronic warfare (EW). Readers will learn how EW systems respond more quickly and effectively to battlefield conditions where sophisticated radars and spectrum congestion put a high priority on EW systems that can characterize and classify novel waveforms, discern intent, and devise and test countermeasures. Specific techniques are covered for optimizing a cognitive EW system as well as evaluating its ability to learn new information in real time. The book presents AI for electronic support (ES), including characterization, classification, patterns of life, and intent recognition. Optimization techniques, including temporal tradeoffs and distributed optimization challenges are also discussed. The issues concerning real-time in-mission machine learning and suggests some approaches to address this important challenge are presented and described. The book covers electronic battle management, data management, and knowledge sharing. Evaluation approaches, including how to show that a machine learning system can learn how to handle novel environments, are also discussed. Written by experts with first-hand experience in AI-based EW, this is the first book on in-mission real-time learning and optimization.
Artificial Intelligence (AI) is dominated by the 'Basic Model' that the mind stands to the brain as the program stands to the computer. This conception that the theory of computability defines the boundaries of thought can be sustained only if the mental processes of human beings operate in the same fashion as do the programs of computers. The standard conception and the Basic Model are subjected to a thorough critique in this book, which offers evidence that the Basic Model is irretrievably flawed and that the standard conception has to be rejected.
This volume provides an exceptional perspective on the nature, evolution, contributions and future of the field of Cognitive Systems Engineering (CSE). It is a resource to support both the teaching and practice of CSE. It accomplishes this through its organization into two complementary approaches to the topic. The first is an historical perspective: In the retrospections of leaders of the field, what have been the seminal achievements of cognitive human factors? What are the "lessons learned" that became foundational to CSE, and how did that foundation evolve into a broader systems view of cognitive work? The second perspective is both pedagogical and future-looking: What are the major conceptual issues that have to be addressed by CSE and how can a new generation of researchers be prepared to further advance CSE? Topics include studies of expertise, cognitive work analysis, cognitive task analysis, human performance, system design, cognitive modeling, decision making, human-computer interaction, trust in automation, teamwork and ecological interface design. A thematic focus will be on systems-level analysis, and such notions as resilience engineering and systems-level measurement. The book features broad coverage of many of the domains to which CSE is being applied, among them industrial process control, health care, decision aiding and aviation human factors. The book’s contributions are provided by an extraordinary group of leaders and pathfinders in applied psychology, cognitive science, systems analysis and system design. In combination these chapters present invaluable insights, experiences and continuing uncertainties on the subject of the field of CSE, and in doing so honor the career and achievements of Professor David D. Woods of Ohio State University.
Build next-generation Artificial Intelligence systems with Java Key Features Implement AI techniques to build smart applications using Deeplearning4j Perform big data analytics to derive quality insights using Spark MLlib Create self-learning systems using neural networks, NLP, and reinforcement learning Book Description In this age of big data, companies have larger amount of consumer data than ever before, far more than what the current technologies can ever hope to keep up with. However, Artificial Intelligence closes the gap by moving past human limitations in order to analyze data. With the help of Artificial Intelligence for big data, you will learn to use Machine Learning algorithms such as k-means, SVM, RBF, and regression to perform advanced data analysis. You will understand the current status of Machine and Deep Learning techniques to work on Genetic and Neuro-Fuzzy algorithms. In addition, you will explore how to develop Artificial Intelligence algorithms to learn from data, why they are necessary, and how they can help solve real-world problems. By the end of this book, you'll have learned how to implement various Artificial Intelligence algorithms for your big data systems and integrate them into your product offerings such as reinforcement learning, natural language processing, image recognition, genetic algorithms, and fuzzy logic systems. What you will learn Manage Artificial Intelligence techniques for big data with Java Build smart systems to analyze data for enhanced customer experience Learn to use Artificial Intelligence frameworks for big data Understand complex problems with algorithms and Neuro-Fuzzy systems Design stratagems to leverage data using Machine Learning process Apply Deep Learning techniques to prepare data for modeling Construct models that learn from data using open source tools Analyze big data problems using scalable Machine Learning algorithms Who this book is for This book is for you if you are a data scientist, big data professional, or novice who has basic knowledge of big data and wish to get proficiency in Artificial Intelligence techniques for big data. Some competence in mathematics is an added advantage in the field of elementary linear algebra and calculus.
This book gives the most comprehensive, in depth and contemporary assessment of this classic topic in artificial intelligence. It is the first to elaborate in such detail the numerous conflicting points of view on many aspects of this multifaceted, controversial subject. It offers new insights into Turing's own interpretation and is essential reading for research on the Turing test and for teaching undergraduate and graduate students in philosophy, computer science, and cognitive science.
Design of cognitive systems for assistance to people poses a major challenge to the fields of robotics and artificial intelligence. The Cognitive Systems for Cognitive Assistance (CoSy) project was organized to address the issues of i) theoretical progress on design of cognitive systems ii) methods for implementation of systems and iii) empirical studies to further understand the use and interaction with such systems. To study, design and deploy cognitive systems there is a need to considers aspects of systems design, embodiment, perception, planning and error recovery, spatial insertion, knowledge acquisition and machine learning, dialog design and human robot interaction and systems integration. The CoSy project addressed all of these aspects over a period of four years and across two different domains of application – exploration of space and task / knowledge acquisition for manipulation. The present volume documents the results of the CoSy project. The CoSy project was funded by the European Commission as part of the Cognitive Systems Program within the 6th Framework Program.
Creativity is one of the least understood aspects of intelligence and is often seen as `intuitive' and not susceptible to rational enquiry. Recently, however, there has been a resurgence of interest in the area, principally in artificial intelligence and cognitive science, but also in psychology, philosophy, computer science, logic, mathematics, sociology, and architecture and design. This volume brings this work together and provides an overview of this rapidly developing field. It addresses a range of issues. Can computers be creative? Can they help us to understand human creativity? How can artificial intelligence (AI) enhance human creativity? How, in particular, can it contribute to the `sciences of the artificial', such as design? Does the new wave of AI (connectionism, geneticism and artificial life) offer more promise in these areas than classical, symbol-handling AI? What would the implications be for AI and cognitive science if computers could not be creative? These issues are explored in five interrelated parts, each of which is introducted and explained by a leading figure in the field. - Prologue (Margaret Boden) - Part I: Foundational Issues (Terry Dartnall) - Part II: Creativity and Cognition (Graeme S. Halford and Robert Levinson) - Part III: Creativity and Connectionism (Chris Thornton) - Part IV: Creativity and Design (John Gero) - Part V: Human Creativity Enhancement (Ernest Edmonds) - Epilogue (Douglas Hofstadter) For researchers in AI, cognitive science, computer science, philosophy, psychology, mathematics, logic, sociology, and architecture and design; and anyone interested in the rapidly growing field of artificial intelligence and creativity.