Artificial Intelligence For Cognitive Modeling

With all the material available in the field of artificial intelligence (AI) and soft computing-texts, monographs, and journal articles-there remains a serious gap in the literature. Until now, there has been no comprehensive resource accessible to a broad audience yet containing a depth and breadth of information that enables the reader to fully understand and readily apply AI and soft computing concepts. Artificial Intelligence and Soft Computing fills this gap. It presents both the traditional and the modern aspects of AI and soft computing in a clear, insightful, and highly comprehensive style. It provides an in-depth analysis of mathematical models and algorithms and demonstrates their applications in real world problems. Beginning with the behavioral perspective of "human cognition," the text covers the tools and techniques required for its intelligent realization on machines. The author addresses the classical aspects-search, symbolic logic, planning, and machine learning-in detail and includes the latest research in these areas. He introduces the modern aspects of soft computing from first principles and discusses them in a manner that enables a beginner to grasp the subject. He also covers a number of other leading aspects of AI research, including nonmonotonic and spatio-temporal reasoning, knowledge acquisition, and much more. Artificial Intelligence and Soft Computing: Behavioral and Cognitive Modeling of the Human Brain is unique for its diverse content, clear presentation, and overall completeness. It provides a practical, detailed introduction that will prove valuable to computer science practitioners and students as well as to researchers migrating to the subject from other disciplines.

This book is written in a clear and thorough way to cover both the traditional and modern uses of artificial intelligence and soft computing. It gives an in-depth look at mathematical models, algorithms, and real-world problems that are hard to solve in MATLAB. The book is intended to provide a broad and in-depth understanding of fuzzy logic controllers, genetic algorithms, neural networks, and hybrid techniques such as ANFIS and the GA-ANN model. Features: A detailed description of basic intelligent techniques (fuzzy logic, genetic algorithm and neural network using MATLAB) A detailed description of the hybrid intelligent technique called the adaptive fuzzy inference technique (ANFIS) Formulation of the nonlinear model like analysis of ANOVA and response surface methodology Variety of solved problems on ANOVA and RSM Case studies of above mentioned intelligent techniques on the different process control systems This book can be used as a handbook and a guide for students of all engineering disciplines, operational research areas, computer applications, and for various professionals who work in the optimization area.

John Funge introduces a new approach to creating autonomous characters. Cognitive modeling provides computer-animated characters with logic, reasoning, and planning skills. Individual chapters in the book provide concrete examples of advanced character animation, automated cinematography, and a real-time computer game. Source code, animations, images, and other resources are available at the book's website, listed below.

This book offers a groundbreaking approach to bridging the gap between various disciplines involved in cognitive modeling in education. By drawing on the fields of learning, neuro science, cognitive science, neurobiology, and computer science, it provides a new perspective on how we can integrate these disciplines with education to create more effective learning environments. The main objective of this book is to delve into the ethical, sociological, and technological questions surrounding the introduction of intelligent and smart learning environments in education. By analyzing these issues, this book aims to bridge the gap between the various disciplines involved in cognitive modeling and education, while highlighting the benefits and risks associated with these advancements. With the emergence of AI-based tutors, coaches, and learning environments, students now have access to a new type of self-learning and self-training that was previously unavailable. Distance learning has become increasingly popular in recent years, and the use of computer-assisted learning tools has revolutionized the way we think about education. The goal of education must be to instill in students a desire to learn for themselves, and this can only be achieved through active, self-directed, and reflective learning. With intelligent tutoring systems, students are empowered to take an active role in their own education, rather than simply being passive recipients of information. This book offers practical strategies for teachers to facilitate this transition, enabling them to act as facilitators and guides rather than one-way communicators. By embracing this new approach to education, we can help students become lifelong learners who are equipped with the skills they need to succeed in the 21st century. As we cannot predict the future with certainty, the true effects of education may only be revealed in the long run, making it critical to understand the potential consequences of introducing these new learning tools. By exploring these complex topics, this book offers valuable insights for educators, policymakers, and anyone interested in the future of education.

A comprehensive introduction to the computational modeling of human cognition.

Applied Biomedical Engineering Using Artificial Intelligence and Cognitive Models focuses on the relationship between three different multidisciplinary branches of engineering: Biomedical Engineering, Cognitive Science and Computer Science through Artificial Intelligence models. These models will be used to study how the nervous system and musculoskeletal system obey movement orders from the brain, as well as the mental processes of the information during cognition when injuries and neurologic diseases are present in the human body. The interaction between these three areas are studied in this book with the objective of obtaining AI models on injuries and neurologic diseases of the human body, studying diseases of the brain, spine and the nerves that connect them with the musculoskeletal system. There are more than 600 diseases of the nervous system, including brain tumors, epilepsy, Parkinson's disease, stroke, and many others. These diseases affect the human cognitive system that sends orders from the central nervous system (CNS) through the peripheral nervous systems (PNS) to do tasks using the musculoskeletal system. These actions can be detected by many Bioinstruments (Biomedical Instruments) and cognitive device data, allowing us to apply AI using Machine Learning-Deep Learning-Cognitive Computing models through algorithms to analyze, detect, classify, and forecast the process of various illnesses, diseases, and injuries of the human body. Applied Biomedical Engineering Using Artificial Intelligence and Cognitive Models provides readers with the study of injuries, illness, and neurological diseases of the human body through Artificial Intelligence using Machine Learning (ML), Deep Learning (DL) and Cognitive Computing (CC) models based on algorithms developed with MATLAB® and IBM Watson®. Provides an introduction to Cognitive science, cognitive computing and human cognitive relation to help in the solution of AI Biomedical engineering problems Explain different Artificial Intelligence (AI) including evolutionary algorithms to emulate natural evolution, reinforced learning, Artificial Neural Network (ANN) type and cognitive learning and to obtain many AI models for Biomedical Engineering problems Includes coverage of the evolution Artificial Intelligence through Machine Learning (ML), Deep Learning (DL), Cognitive Computing (CC) using MATLAB® as a programming language with many add-on MATLAB® toolboxes, and AI based commercial products cloud services as: IBM (Cognitive Computing, IBM Watson®, IBM Watson Studio®, IBM Watson Studio Visual Recognition®), and others Provides the necessary tools to accelerate obtaining results for the analysis of injuries, illness, and neurologic diseases that can be detected through the static, kinetics and kinematics, and natural body language data and medical imaging techniques applying AI using ML-DL-CC algorithms with the objective of obtaining appropriate conclusions to create solutions that improve the quality of life of patients

The leading thinkers from the cognitive science tradition participated in a workshop sponsored by Sandia National Laboratories in July of 2003 to discuss progress in building their models. The goal was to summarize the theoretical and empirical bases for cognitive systems and to present exemplary developments in the field. Following the workshop, a great deal of planning went into the creation of this book. Eleven of the twenty-six presenters were asked to contribute chapters, and four chapters are the product of the breakout sessions in which critical topics were discussed among the participants. An introductory chapter provides the context for this compilation. Cognitive Systems thus presents a unique merger of cognitive modeling and intelligent systems, and attempts to overcome many of the problems inherent in current expert systems. It will be of interest to researchers and students in the fields of cognitive science, computational modeling, intelligent systems, artificial intelligence, and human-computer interaction.

Resulting from the need for greater realism in models of human and organizational behavior in military simulations, there has been increased interest in research on integrative models of human performance, both within the cognitive science community generally, and within the defense and aerospace industries in particular. This book documents accomplishments and lessons learned in a multi-year project to examine the ability of a range of integrated cognitive modeling architectures to explain and predict human behavior in a common task environment that requires multi-tasking and concept learning. This unique project, called the Agent-Based Modeling and Behavior Representation (AMBR) Model Comparison, involved a series of human performance model evaluations in which the processes and performance levels of computational cognitive models were compared to each other and to human operators performing the identical tasks. In addition to quantitative data comparing the performance of the models and real human performance, the book also presents a qualitatively oriented discussion of the practical and scientific considerations that arise in the course of attempting this kind of model development and validation effort. The primary audiences for this book are people in academia, industry, and the military who are interested in explaining and predicting complex human behavior using computational cognitive modeling approaches. The book should be of particular interest to individuals in any sector working in Psychology, Cognitive Science, Artificial Intelligence, Industrial Engineering, System Engineering, Human Factors, Ergonomics and Operations Research. Any technically or scientifically oriented professional or student should find the material fully accessible without extensive mathematical background.