Bøger i Cognitive Technologies serien

This monograph explores the synergy of Artificial Intelligence (AI), Augmented Reality (AR) and cognitive processes to enhance spatial abilities ¿ an integral aspect of cognitive development. The ability to comprehend and manipulate spatial information is not only fundamental to our understanding of the physical world but also plays a pivotal role in numerous academic and professional fields. Recognizing the profound impact of spatial ability on scientific disciplines and educational achievement, this monograph takes on the challenge of enhancing spatial skills among users.The authors present the design and development of a mobile training system that incorporates AR features to enhance learners¿ spatial ability. Involving mental generation, transformation, and rotation of visual images for understanding spatial relationships, spatial ability is closely linked to success in various scientific disciplines and educational pursuits. While spatial visualization skills testsare available, this monograph takes a unique approach by focusing on developing targeted interventions to improve spatial ability. It aims to unlock new avenues for cognitive growth and dive into the untapped potential within the realm of spatial intelligence. Beyond its emphasis on spatial ability enhancement, this monograph goes above and beyond traditional approaches by integrating AI techniques into the training system. As such, it aims to provide personalized and adaptive learning experiences for learners. The training system, through intelligent techniques, dynamically analyzes individual learners¿ strengths, weaknesses, and progress, tailoring content and challenges to their specific needs. This effort establishes a new frontier in educational technology, offering a groundbreaking solution that not only augments spatial ability development but also showcases the transformative potential of AI in reshaping the learning experience.The book is a valuable resource for researchers, educators, developers and technology enthusiasts, as it exemplifies the profound impact of AI and AR in shaping the future of online learning experiences.

This book explores the challenges involved in handling medical big data in the diagnosis of neurological disorders. It discusses how to optimally reduce the number of neuropsychological tests during the classification of these disorders by using feature selection methods based on the diagnostic information of enrolled subjects. The book includes key definitions/models and covers their applications in different types of signal/image processing for neurological disorder data. An extensive discussion on the possibility of enhancing the abilities of AI systems using the different data analysis is included. The book recollects several applicable basic preliminaries of the different AI networks and models, while also highlighting basic processes in image processing for various neurological disorders. It also reports on several applications to image processing and explores numerous topics concerning the role of big data analysis in addressing signal and image processing in various real-world scenarios involving neurological disorders.This cutting-edge book highlights the analysis of medical data, together with novel procedures and challenges for handling neurological signals and images. It will help engineers, researchers and software developers to understand the concepts and different models of AI and data analysis. To help readers gain a comprehensive grasp of the subject, it focuses on three key features:¿ Presents outstanding concepts and models for using AI in clinical applications involving neurological disorders, with clear descriptions of image representation, feature extraction and selection.¿ Highlights a range of techniques for evaluating the performance of proposed CAD systems for the diagnosis of neurological disorders.¿ Examines various signal and image processing methods for efficient decision support systems. Soft computing, machine learning and optimization algorithms are also included to improve the CAD systems used.

She introduces an image schema logic as a language to model image schemas, and she shows how the semantic content of image schemas can be used to improve computational concept invention. The book will be of value to researchers in artificial intelligence, cognitive science, psychology, and creativity.

This book examines the contributions of the transhumanism approach to technology, in particular the contributed chapters are wary of the implications of this popular idea.The volume is organized into four parts concerning philosophical, military, technological and sociological aspects of transhumanism, but the reader is free to choose various reading patterns. Topics discussed include gene editing, the singularity, ethical machines, metaphors in AI, mind uploading, and the philosophy of art, and some perspectives taken or discussed examine transhumanism within the context of the philosophy of technology, transhumanism as a derailed anthropology, and critical sociological aspects that consider transhumanism in the context of topical concerns such as whiteness, maleness, and masculinity.The book will be of value to researchers engaged with artificial intelligence, and the ethical, societal, and philosophical impacts of science and technology.

This book opens with a lucid explanation of the theory of reactive Kripke semantics, followed by chapters demonstrating applications in a variety of areas. The book benefits both researchers and graduate students working in the field of mathematical logic.

This book helps readers learn the principles of natural language processing. It features a hands-on approach throughout, with extensive exercises, sample code in Prolog and Perl, and a detailed introduction to Prolog.

This book reviews the basics of rule learning as applied to classical machine learning and modern data mining. It connects attribute-value learning with inductive logic programming, and offers complete coverage of most important elements of rule learning.

This book focuses on rare category analysis where the majority classes have smooth distributions and the minority classes exhibit the compactness property. It focuses on challenging cases where the support regions of the majority and minority classes overlap.

This book covers modularity and independence in classical and nonclassical logic, including many-valued logics and structures, plus neighbourhood semantics and their connection to independence, as well as their common points and differences for various logics.

We review the elements of the graphical user interface of traditional spreadsheet applications and describe practical methodologies for designing user interfaces borrowed from the field of cognitive psychology.

This book examines preference representation, covering languages, nonmonotonic logics of preference and conditional networks, then moves to reasoning, exploring preference-based argumentation, rank-ordering outcomes, database queries and future research trends.

This book introduces a theory of interpretation. It defines a model that enables a meaningful representation of information, based on a dynamic view of information and a cognitive model of human information processing.

This book explores why, regarding practical reasoning, humans are sometimes still faster than artificial intelligence systems. It is the first to offer a self-contained presentation of neural network models for many computer science logics.

This book presents general methods for analysing the complexity of optimisation problems cast as valued constraint satisfaction problems (VCSPs). It is an ideal resource for researchers in constraint programming and discrete optimisation.

Starting with detailed introductions to information fusion and integration, measurement and probability theory, fuzzy sets, and functional equations, the authors then cover numerous topics in detail, including the synthesis of judgements, fuzzy measures, weighted means and fuzzy integrals.

This book is suitable for researchers and postgraduates in the areas of artificial intelligence, database theory, and logic. The authors illustrate the idea through the presentation of various algorithms and offer the most-up-to-date book on this topic.

Reinforcement learning has evolved to tackle domains that are yet to be fully understood, or are too complex for a closed description. In this book the author investigates whether suitable abstraction methods can overcome the discipline's deficiencies.

This book covers general logical tools for handling change. The tools are preferential reasoning, theory revision and reasoning in inheritance systems. Logics examined are nonmonotonic, deontic, modal, intuitionistic and temporal as well as counterfactuals.

This book presents biologically inspired walking machines interacting with their physical environment, and shows how the morphology and behavior control of machines can benefit from biological studies.

This book presents the concept of cognition in a clear, lucid and highly comprehensive style. It provides an in-depth analysis of mathematical models and algorithms, and demonstrates their application with real life experiments.

This book describes the theory, methodology and algorithmization of a new approach to problem solving and its realization in intelligent systems. It investigates cognitive reasoning at three levels of abstraction: conceptual, formal, and realizational.