Logical Learning Theory


Book Description

In 1989, B. F. Skinner told Joseph Rychlak that the greatest disappointment resulting from the "cognitive revolution" was the turning of the human organism into a machine. Intrigued by this statement, Rychlak decided that after many years of formulation it was time to present his fundamentally teleological view of the human being, which he calls the "logical learning theory" (LLT). In this new theoretical perspective the author re-presents such concepts as intention, purpose, and free will. Significant aspects of the "mind-body" issue are explored here. Rychlak addresses teleological issues and provides a language for proper conceptualization. He uses experimental findings to support the notion of behavior as self-directed rather than mechanistic. In the process, Rychlak places LLT on the side of teleological explanation, in which concepts like free will, self-choice, purpose and intention are no longer dismissed. Rychlak compares LLT and existing formulations of behavior, including classical and operant conditioning, social learning theory, social constructionism, cognitive science, gestalt theories, and personality theories. Extensive research data and thorough discussions support Rychlak's theory. A glossary is also included.




A Logical Theory of Causality


Book Description

A general formal theory of causal reasoning as a logical study of causal models, reasoning, and inference. In this book, Alexander Bochman presents a general formal theory of causal reasoning as a logical study of causal models, reasoning, and inference, basing it on a supposition that causal reasoning is not a competitor of logical reasoning but its complement for situations lacking logically sufficient data or knowledge. Bochman also explores the relationship of this theory with the popular structural equation approach to causality proposed by Judea Pearl and explores several applications ranging from artificial intelligence to legal theory, including abduction, counterfactuals, actual and proximate causality, dynamic causal models, and reasoning about action and change in artificial intelligence. As logical preparation, before introducing causal concepts, Bochman describes an alternative, situation-based semantics for classical logic that provides a better understanding of what can be captured by purely logical means. He then presents another prerequisite, outlining those parts of a general theory of nonmonotonic reasoning that are relevant to his own theory. These two components provide a logical background for the main, two-tier formalism of the causal calculus that serves as the formal basis of his theory. He presents the main causal formalism of the book as a natural generalization of classical logic that allows for causal reasoning. This provides a formal background for subsequent chapters. Finally, Bochman presents a generalization of causal reasoning to dynamic domains.




Encyclopedia of the Sciences of Learning


Book Description

Over the past century, educational psychologists and researchers have posited many theories to explain how individuals learn, i.e. how they acquire, organize and deploy knowledge and skills. The 20th century can be considered the century of psychology on learning and related fields of interest (such as motivation, cognition, metacognition etc.) and it is fascinating to see the various mainstreams of learning, remembered and forgotten over the 20th century and note that basic assumptions of early theories survived several paradigm shifts of psychology and epistemology. Beyond folk psychology and its naïve theories of learning, psychological learning theories can be grouped into some basic categories, such as behaviorist learning theories, connectionist learning theories, cognitive learning theories, constructivist learning theories, and social learning theories. Learning theories are not limited to psychology and related fields of interest but rather we can find the topic of learning in various disciplines, such as philosophy and epistemology, education, information science, biology, and – as a result of the emergence of computer technologies – especially also in the field of computer sciences and artificial intelligence. As a consequence, machine learning struck a chord in the 1980s and became an important field of the learning sciences in general. As the learning sciences became more specialized and complex, the various fields of interest were widely spread and separated from each other; as a consequence, even presently, there is no comprehensive overview of the sciences of learning or the central theoretical concepts and vocabulary on which researchers rely. The Encyclopedia of the Sciences of Learning provides an up-to-date, broad and authoritative coverage of the specific terms mostly used in the sciences of learning and its related fields, including relevant areas of instruction, pedagogy, cognitive sciences, and especially machine learning and knowledge engineering. This modern compendium will be an indispensable source of information for scientists, educators, engineers, and technical staff active in all fields of learning. More specifically, the Encyclopedia provides fast access to the most relevant theoretical terms provides up-to-date, broad and authoritative coverage of the most important theories within the various fields of the learning sciences and adjacent sciences and communication technologies; supplies clear and precise explanations of the theoretical terms, cross-references to related entries and up-to-date references to important research and publications. The Encyclopedia also contains biographical entries of individuals who have substantially contributed to the sciences of learning; the entries are written by a distinguished panel of researchers in the various fields of the learning sciences.




Reliable Reasoning


Book Description

The implications for philosophy and cognitive science of developments in statistical learning theory. In Reliable Reasoning, Gilbert Harman and Sanjeev Kulkarni—a philosopher and an engineer—argue that philosophy and cognitive science can benefit from statistical learning theory (SLT), the theory that lies behind recent advances in machine learning. The philosophical problem of induction, for example, is in part about the reliability of inductive reasoning, where the reliability of a method is measured by its statistically expected percentage of errors—a central topic in SLT. After discussing philosophical attempts to evade the problem of induction, Harman and Kulkarni provide an admirably clear account of the basic framework of SLT and its implications for inductive reasoning. They explain the Vapnik-Chervonenkis (VC) dimension of a set of hypotheses and distinguish two kinds of inductive reasoning. The authors discuss various topics in machine learning, including nearest-neighbor methods, neural networks, and support vector machines. Finally, they describe transductive reasoning and suggest possible new models of human reasoning suggested by developments in SLT.




Ways of Learning


Book Description

Whilst most teachers are skilled in providing opportunities for the progression of children’s learning, it is often without fully understanding the theory behind it. With greater insight into what is currently known about the processes of learning and about individual learning preferences, teachers are better equipped to provide effective experiences and situations which are more likely to lead to lasting attainment. Now fully updated, Ways of Learning seeks to provide an understanding of the ways in which learning takes place, which teachers can make use of in their planning and teaching, including: An overview of learning Behaviourism and the beginning of theory Cognitive and constructivist learning Multiple intelligences Learning styles Difficulties with learning The influence of neuro-psychology Relating theory to practice The third edition of this book includes developments in areas covered in the first and second editions, as well as expanding on certain topics to bring about a wider perspective; most noticeably a newly updated and fully expanded chapter on the influence of neuro-educational research. The book also reflects changes in government policy and is closely related to new developments in practice. Written for trainee teachers, serving teachers, and others interested in learning for various reasons, Ways of Learning serves as a valuable introduction for students setting out on higher degree work who are in need of an introduction to the topic.




Does Mathematical Study Develop Logical Thinking?: Testing The Theory Of Formal Discipline


Book Description

For centuries, educational policymakers have believed that studying mathematics is important, in part because it develops general thinking skills that are useful throughout life. This 'Theory of Formal Discipline' (TFD) has been used as a justification for mathematics education globally. Despite this, few empirical studies have directly investigated the issue, and those which have showed mixed results.Does Mathematical Study Develop Logical Thinking? describes a rigorous investigation of the TFD. It reviews the theory's history and prior research on the topic, followed by reports on a series of recent empirical studies. It argues that, contrary to the position held by sceptics, advanced mathematical study does develop certain general thinking skills, however these are much more restricted than those typically claimed by TFD proponents.Perfect for students, researchers and policymakers in education, further education and mathematics, this book provides much needed insight into the theory and practice of the foundations of modern educational policy.




Probability Theory


Book Description

Probability theory




Learning Theory


Book Description

This book constitutes the refereed proceedings of the 19th Annual Conference on Learning Theory, COLT 2006, held in Pittsburgh, Pennsylvania, USA in June 2006. The 43 revised full papers presented together with 2 articles on open problems and 3 invited lectures were carefully reviewed and selected from a total of 102 submissions. The papers cover a wide range of topics including clustering, un- and semisupervised learning, statistical learning theory, regularized learning and kernel methods, query learning and teaching, inductive inference, learning algorithms and limitations on learning, online aggregation, online prediction and reinforcement learning.




Personality and Life-Style of Young Male Managers


Book Description

Personality and Life-Style of Young Male Managers: A Logical Learning Theory Analysis presets the teleological theory of behavior wherein individuals are presumed to be agents of their behavior rather than the mere mediators or conduits of influences funneling into their cognitive processes. This book provides the basic data of the longitudinal study that involve personal interviews and independent personality measures drawn from objective and projective tests. Organized into two parts encompassing 17 chapters, this book begins with an overview of the basic research design, instrumentation, and the broader implications of scientific description and theoretical observations in the context of empirical proof. This text then reviews the fundamental findings of the longitudinal investigation. Other chapters consider the religious–humanism life theme as one of the vivid indicants that a man would be well adjusted in personality. This book discusses as well the types of men who continued to involve themselves on the parental–familial life theme are intelligent, self-confident, and prone to be leaders. The final chapter deals with the service life theme, which seem to take all the types of personality patterns. This book is a valuable resource for psychologists and research workers.




Logical and Relational Learning


Book Description

This first textbook on multi-relational data mining and inductive logic programming provides a complete overview of the field. It is self-contained and easily accessible for graduate students and practitioners of data mining and machine learning.