Computational Logic And Human Thinking

The practical benefits of computational logic need not be limited to mathematics and computing. As this book shows, ordinary people in their everyday lives can profit from the recent advances that have been developed for artificial intelligence. The book draws upon related developments in various fields from philosophy to psychology and law. It pays special attention to the integration of logic with decision theory, and the use of logic to improve the clarity and coherence of communication in natural languages such as English. This book is essential reading for teachers and researchers who may be out of touch with the latest developments in computational logic. It will also be useful in any undergraduate course that teaches practical thinking, problem solving or communication skills. Its informal presentation makes the book accessible to readers from any background, but optional, more formal, chapters are also included for those who are more technically oriented.

How a computational framework can account for the successes and failures of human cognition At the heart of human intelligence rests a fundamental puzzle: How are we incredibly smart and stupid at the same time? No existing machine can match the power and flexibility of human perception, language, and reasoning. Yet, we routinely commit errors that reveal the failures of our thought processes. What Makes Us Smart makes sense of this paradox by arguing that our cognitive errors are not haphazard. Rather, they are the inevitable consequences of a brain optimized for efficient inference and decision making within the constraints of time, energy, and memory—in other words, data and resource limitations. Framing human intelligence in terms of these constraints, Samuel Gershman shows how a deeper computational logic underpins the “stupid” errors of human cognition. Embarking on a journey across psychology, neuroscience, computer science, linguistics, and economics, Gershman presents unifying principles that govern human intelligence. First, inductive bias: any system that makes inferences based on limited data must constrain its hypotheses in some way before observing data. Second, approximation bias: any system that makes inferences and decisions with limited resources must make approximations. Applying these principles to a range of computational errors made by humans, Gershman demonstrates that intelligent systems designed to meet these constraints yield characteristically human errors. Examining how humans make intelligent and maladaptive decisions, What Makes Us Smart delves into the successes and failures of cognition.

David E. Over is a leading cognitive scientist and, with his firm grounding in philosophical logic, he also exerts a powerful influence on the psychology of reasoning. He is responsible for not only a large body of empirical work and accompanying theory, but for advancing a major shift in thinking about reasoning, commonly known as the ‘new paradigm’ in the psychology of human reasoning. Over’s signature mix of philosophical logic and experimental psychology has inspired generations of researchers, psychologists, and philosophers alike over more than a quarter of a century. The chapters in this volume, written by a leading group of contributors including a number who helped shape the psychology of reasoning as we know it today, each take their starting point from the key themes of Over’s ground-breaking work. The essays in this collection explore a wide range of central topics—such as rationality, bias, dual processes, and dual systems—as well as contemporary psychological and philosophical theories of conditionals. It concludes with an engaging new chapter, authored by David E. Over himself, which details and analyses the new paradigm psychology of reasoning. This book is therefore important reading for scholars, researchers, and advanced students in psychology, philosophy, and the cognitive sciences, including those who are not familiar with Over’s thought already.

ACM Monograph Series: A Computational Logic focuses on the use of induction in proving theorems, including the use of lemmas and axioms, free variables, equalities, and generalization. The publication first elaborates on a sketch of the theory and two simple examples, a precise definition of the theory, and correctness of a tautology-checker. Topics include mechanical proofs, informal development, formal specification of the problem, well-founded relations, natural numbers, and literal atoms. The book then examines the use of type information to simplify formulas, use of axioms and lemmas as rewrite rules, and the use of definitions. Topics include nonrecursive functions, computing values, free variables in hypothesis, infinite backwards chaining, infinite looping, computing type sets, and type prescriptions. The manuscript takes a look at rewriting terms and simplifying clauses, eliminating destructors and irrelevance, using equalities, and generalization. Concerns include reasons for eliminating isolated hypotheses, precise statement of the generalization heuristic, restricting generalizations, precise use of equalities, and multiple destructors and infinite looping. The publication is a vital source of data for researchers interested in computational logic.

This book presents a mechanist philosophy of mind. I hold that the human mind is a system of computational or recursive rules that are embodied in the nervous system; that the material presence of these rules accounts for perception, conception, speech, belief, desire, intentional acts, and other forms of intelligence. In this edition I have retained the whole of the fIrst edition except for discussion of issues which no longer are relevant in philosophy of mind and cognitive psychology. Earlier reference to disputes of the 1960's and 70's between hard-line empiricists and neorationalists over the psychological status of grammars and language acquisition, for instance, has simply been dropped. In place of such material I have entered some timely or new topics and a few changes. There are brief references to the question of computer versus distributed processing (connectionist) theories. Many of these questions dissolve if one distinguishes as I now do in Chapter II between free and embodied algorithms. I have also added to my comments on artifIcal in telligence some reflections. on Searle's Chinese Translator. The irreducibility of machine functionalist psychology in my version or any other has been exaggerated. Input, output, and state entities are token identical to physical or biological things of some sort, while a machine system as a collection of recursive rules is type identical to representatives of equivalence classes. This nuld technicality emerges in Chapter XI. It entails that so-called "anomalous monism" is right in one sense and wrong in another.

This is the first volume of a unique collection that brings together the best English-language problems created for students competing in the Computational Linguistics Olympiad. These problems are representative of the diverse areas presented in the competition and designed with three principles in mind: · To challenge the student analytically, without requiring any explicit knowledge or experience in linguistics or computer science; · To expose the student to the different kinds of reasoning required when encountering a new phenomenon in a language, both as a theoretical topic and as an applied problem; · To foster the natural curiosity students have about the workings of their own language, as well as to introduce them to the beauty and structure of other languages; · To learn about the models and techniques used by computers to understand human language. Aside from being a fun intellectual challenge, the Olympiad mimics the skills used by researchers and scholars in the field of computational linguistics. In an increasingly global economy where businesses operate across borders and languages, having a strong pool of computational linguists is a competitive advantage, and an important component to both security and growth in the 21st century. This collection of problems is a wonderful general introduction to the field of linguistics through the analytic problem solving technique. "A fantastic collection of problems for anyone who is curious about how human language works! These books take serious scientific questions and present them in a fun, accessible way. Readers exercise their logical thinking capabilities while learning about a wide range of human languages, linguistic phenomena, and computational models. " - Kevin Knight, USC Information Sciences Institute

This volume on the semantic complexity of natural language explores the question why some sentences are more difficult than others. While doing so, it lays the groundwork for extending semantic theory with computational and cognitive aspects by combining linguistics and logic with computations and cognition. Quantifier expressions occur whenever we describe the world and communicate about it. Generalized quantifier theory is therefore one of the basic tools of linguistics today, studying the possible meanings and the inferential power of quantifier expressions by logical means. The classic version was developed in the 1980s, at the interface of linguistics, mathematics and philosophy. Before this volume, advances in "classic" generalized quantifier theory mainly focused on logical questions and their applications to linguistics, this volume adds a computational component, the third pillar of language use and logical activity. This book is essential reading for researchers in linguistics, philosophy, cognitive science, logic, AI, and computer science.

Lance Rips describes a unified theory of natural deductive reasoning and fashions a working model of deduction, with strong experimental support, that is capable of playing a central role in mental life. In this provocative book, Lance Rips describes a unified theory of natural deductive reasoning and fashions a working model of deduction, with strong experimental support, that is capable of playing a central role in mental life. Rips argues that certain inference principles are so central to our notion of intelligence and rationality that they deserve serious psychological investigation to determine their role in individuals' beliefs and conjectures. Asserting that cognitive scientists should consider deductive reasoning as a basis for thinking, Rips develops a theory of natural reasoning abilities and shows how it predicts mental successes and failures in a range of cognitive tasks. In parts I and II of the book, Rips builds insights from cognitive psychology, logic, and artificial intelligence into a unified theoretical structure. He defends the idea that deduction depends on the ability to construct mental proofs—actual memory units that link given information to conclusions it warrants. From this base Rips develops a computational model of deduction based on two cognitive skills: the ability to make suppositions or assumptions and the ability to posit sub-goals for conclusions. A wide variety of original experiments support this model, including studies of human subjects evaluating logical arguments as well as following and remembering proofs. Unlike previous theories of mental proof, this one handles names and variables in a general way. This capability enables deduction to play a crucial role in other thought processes, such as classifying and problem solving. In part III, Rips compares the theory to earlier approaches in psychology which confined the study of deduction to a small group of tasks, and examines whether the theory is too rational or too irrational in its mode of thought.