This book introduces a generic approach to model the use and adaptation of mental models, including the control over this. In their mental processes, humans often make use of internal mental models as a kind of blueprints for processes that can take place in the world or in other persons. By internal mental simulation of such a mental model in their brain, they can predict and be prepared for what can happen in the future. Usually, mental models are adaptive: they can be learned, refined, revised, or forgotten, for example. Although there is a huge literature on mental models in various disciplines, a systematic account of how to model them computationally in a transparent manner is lacking. This approach allows for computational modeling of humans using mental models without a need for any algorithmic or programming skills, allowing for focus on the process of conceptualizing, modeling, and simulating complex, real-world mental processes and behaviors. The book is suitable for and is used as course material for multidisciplinary Master and Ph.D. students.
This book features best selected research papers presented at the International Conference on Machine Learning, Internet of Things and Big Data (ICMIB 2020) held at Indira Gandhi Institute of Technology, Sarang, India, during September 2020. It comprises high-quality research work by academicians and industrial experts in the field of machine learning, mobile computing, natural language processing, fuzzy computing, green computing, human–computer interaction, information retrieval, intelligent control, data mining and knowledge discovery, evolutionary computing, IoT and applications in smart environments, smart health, smart city, wireless networks, big data, cloud computing, business intelligence, internet security, pattern recognition, predictive analytics applications in healthcare, sensor networks and social sensing and statistical analysis of search techniques.
The conference objective is to discuss, through papers, new theoretical developments, and techniques in the fields (Computer and Communication Engineering, Control, Computer Science, and Information technology and their applications to real world problems)
This book presents a new approach that can be applied to complex, integrated individual and social human processes. It provides an alternative means of addressing complexity, better suited for its purpose than and effectively complementing traditional strategies involving isolation and separation assumptions. Network-oriented modeling allows high-level cognitive, affective and social models in the form of (cyclic) graphs to be constructed, which can be automatically transformed into executable simulation models. The modeling format used makes it easy to take into account theories and findings about complex cognitive and social processes, which often involve dynamics based on interrelating cycles. Accordingly, it makes it possible to address complex phenomena such as the integration of emotions within cognitive processes of all kinds, of internal simulations of the mental processes of others, and of social phenomena such as shared understandings and collective actions. A variety of sample models – including those for ownership of actions, fear and dreaming, the integration of emotions in joint decision-making based on empathic understanding, and evolving social networks – illustrate the potential of the approach. Dedicated software is available to support building models in a conceptual or graphical manner, transforming them into an executable format and performing simulation experiments. The majority of the material presented has been used and positively evaluated by undergraduate and graduate students and researchers in the cognitive, social and AI domains. Given its detailed coverage, the book is ideally suited as an introduction for graduate and undergraduate students in many different multidisciplinary fields involving cognitive, affective, social, biological, and neuroscience domains.
This book addresses the challenging topic of modeling adaptive networks, which often manifest inherently complex behavior. Networks by themselves can usually be modeled using a neat, declarative, and conceptually transparent Network-Oriented Modeling approach. In contrast, adaptive networks are networks that change their structure; for example, connections in Mental Networks usually change due to learning, while connections in Social Networks change due to various social dynamics. For adaptive networks, separate procedural specifications are often added for the adaptation process. Accordingly, modelers have to deal with a less transparent, hybrid specification, part of which is often more at a programming level than at a modeling level. This book presents an overall Network-Oriented Modeling approach that makes designing adaptive network models much easier, because the adaptation process, too, is modeled in a neat, declarative, and conceptually transparent Network-Oriented Modeling manner, like the network itself. Thanks to this approach, no procedural, algorithmic, or programming skills are needed to design complex adaptive network models. A dedicated software environment is available to run these adaptive network models from their high-level specifications. Moreover, because adaptive networks are described in a network format as well, the approach can simply be applied iteratively, so that higher-order adaptive networks in which network adaptation itself is adaptive (second-order adaptation), too can be modeled just as easily. For example, this can be applied to model metaplasticity in cognitive neuroscience, or second-order adaptation in biological and social contexts. The book illustrates the usefulness of this approach via numerous examples of complex (higher-order) adaptive network models for a wide variety of biological, mental, and social processes. The book is suitable for multidisciplinary Master’s and Ph.D. students without assuming much prior knowledge, although also some elementary mathematical analysis is involved. Given the detailed information provided, it can be used as an introduction to Network-Oriented Modeling for adaptive networks. The material is ideally suited for teaching undergraduate and graduate students with multidisciplinary backgrounds or interests. Lecturers will find additional material such as slides, assignments, and software.
Machine reading comprehension (MRC) is a cutting-edge technology in natural language processing (NLP). MRC has recently advanced significantly, surpassing human parity in several public datasets. It has also been widely deployed by industry in search engine and quality assurance systems. Machine Reading Comprehension: Algorithms and Practice performs a deep-dive into MRC, offering a resource on the complex tasks this technology involves. The title presents the fundamentals of NLP and deep learning, before introducing the task, models, and applications of MRC. This volume gives theoretical treatment to solutions and gives detailed analysis of code, and considers applications in real-world industry. The book includes basic concepts, tasks, datasets, NLP tools, deep learning models and architecture, and insight from hands-on experience. In addition, the title presents the latest advances from the past two years of research. Structured into three sections and eight chapters, this book presents the basis of MRC; MRC models; and hands-on issues in application. This book offers a comprehensive solution for researchers in industry and academia who are looking to understand and deploy machine reading comprehension within natural language processing. - Presents the first comprehensive resource on machine reading comprehension (MRC) - Performs a deep-dive into MRC, from fundamentals to latest developments - Offers the latest thinking and research in the field of MRC, including the BERT model - Provides theoretical discussion, code analysis, and real-world applications of MRC - Gives insight from research which has led to surpassing human parity in MRC
This work argues that cognitive development is experience driven, and processes entailed in acquiring information about the world are analyzed based on recent models of learning and induction. The way information is represented and accessed when performing cognitive tasks is considered paying particular attention to the implications of Parallel Distributed Processing (PDP) models for cognitive development. The first half of the book contains analyses of human reasoning processes (drawing on PDP models of analogy), development of strategies, and task complexity -- all based on aspects of PDP representations. It is proposed that PDP representations become more differentiated with age, so more vectors can be processed in parallel, with the result that structures of greater complexity can be processed. This model gives an account of previously unexplained difficulties in children's reasoning, including some which were influential in stage theories. The second half of the book examines processes entailed in some representative cognitive developmental tasks, including transitive inference, deductive inference (categorical syllogisms), hypothesis testing, learning set acquisition, acquisition and transfer of relational structures, humor, hierarchical classification and inclusion, understanding of quantity, arithmetic word problems, algebra, conservation, mechanics, and the concept of mind. Process accounts of tasks are emphasized, based on applications of recent developments in cognitive science.
Frans Coenen University of Liverpool, UK This volume comprises the refereed technical papers presented at AI2003, the Twenty third SGAI International Conference on the theory, practice and application of Artificial Intelligence, held in Cambridge in December 2003. The conference was organised by SGAI, the British Computer Society Specialist Group on Artificial Intelligence (previously known as SGES). The papers in this volume present new and innovative developments in the field, divided into sections on Machine Learning, Knowledge Representation and Reasoning, Knowledge Acquisition, Constraint Satisfaction, Scheduling and Natural Language Processing. This year's prize for the best refereed technical paper was won by a paper entitled An Improved Hybrid Genetic Algorithm: New Results for the Quadratic Assignment Problem by A. Misevicius (Department of Practical Informatics, Kaunas University of Technology, Lithuania). SGAI gratefully acknowledges the long-term sponsorship of Hewlett-Packard Laboratories (Bristol) for this prize, which goes back to the 1980s. This is the twentieth volume in the Research and Development series. The Application Stream papers are published as a companion volume under the title Applications and Innovations in Intelligent Systems XI. On behalf of the conference organising committee I should like to thank all those who contributed to the organisation of this year's technical programme, in particular the programme committee members, the referees and our administrator Fiona Hartree and Linsay Turbert.
The Secure Child: Timeless Lessons In Parenting and Childhood Education was designed to contribute meaning to the adage “what was old is new again.” Just as ideas in child psychology shifted in the 1960s from a focus on behavior to cognitive stages, we are currently seeing a shift away from stages of development toward an emphasis on the interplay between children and the world around them. Specifically, the book offers practical insights into how children can be helped to cope with their changing worlds. These insights emerged in the 1930s, a time of social and economic upheaval much like today. This collection of original papers by former students and colleagues of William E. Blatz, the renowned psychologist and pediatrician known as the “Dr. Spock of Canada,” makes a vital contribution by bringing forward and examining his work in the context of contemporary ideas about human development, parenting, and education. The collection forms a prologue to an included guide written by Blatz and colleagues, The Expanding World of the Child. The previously unpublished work articulates a comprehensive functional approach to parenting and childhood education. The unique format of this book will make it useful for courses in parenting, childhood education as well scholarship in child psychology, personality theory, and socialization.
Antenna is an array of conductors. It is the interface between radio waves which propagate through electric and space currents in metal conductors. They are required by transmitters and radio receivers to combine its electrical connection to electromagnetic field. Radio waves are electromagnetic waves. They carry signals at the speed of light through air without any transmission loss. They can be classified by operating principles or applications. Antennas are classified as omnidirectional or directional. Other types include whip antenna, dipole antenna, etc. Antennas and propagation act as keys for any radio system. Wave propagation is the study of the ways in which waves travel. The study of radio wave's behavior while traveling from one point to another is known as radio propagation. Most of the topics introduced in this book cover new techniques and the applications of antennas and wave propagation. It aims to shed light on some of the unexplored aspects of this field. It will serve as a valuable source of reference for those interested in antennas and wave propagation.
