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After working through Building Problem Solvers, readers should have a deep understanding of pattern directed inference systems, constraint languages, and truth maintenance systems.
An argument that qualitative representations—symbolic representations that carve continuous phenomena into meaningful units—are central to human cognition. In this book, Kenneth Forbus proposes that qualitative representations hold the key to one of the deepest mysteries of cognitive science: how we reason and learn about the continuous phenomena surrounding us. Forbus argues that qualitative representations—symbolic representations that carve continuous phenomena into meaningful units—are central to human cognition. Qualitative representations provide a basis for commonsense reasoning, because they enable practical reasoning with very little data; this makes qualitative representati...
The emerging widespread use of artificial intelligence in education.
Analogy has been the focus of extensive research in cognitive science over the past two decades. Through analogy, novel situations and problems can be understood in terms of familiar ones. Indeed, a case can be made for analogical processing as the very core of cognition. This is the first book to span the full range of disciplines concerned with analogy. Its contributors represent cognitive, developmental, and comparative psychology; neuroscience; artificial intelligence; linguistics; and philosophy. The book is divided into three parts. The first part describes computational models of analogy as well as their relation to computational models of other cognitive processes. The second part ad...
Experts from a range of disciplines explore how humans and artificial agents can quickly learn completely new tasks through natural interactions with each other. Humans are not limited to a fixed set of innate or preprogrammed tasks. We learn quickly through language and other forms of natural interaction, and we improve our performance and teach others what we have learned. Understanding the mechanisms that underlie the acquisition of new tasks through natural interaction is an ongoing challenge. Advances in artificial intelligence, cognitive science, and robotics are leading us to future systems with human-like capabilities. A huge gap exists, however, between the highly specialized niche ...
Handbook of Knowledge Representation describes the essential foundations of Knowledge Representation, which lies at the core of Artificial Intelligence (AI). The book provides an up-to-date review of twenty-five key topics in knowledge representation, written by the leaders of each field. It includes a tutorial background and cutting-edge developments, as well as applications of Knowledge Representation in a variety of AI systems. This handbook is organized into three parts. Part I deals with general methods in Knowledge Representation and reasoning and covers such topics as classical logic in Knowledge Representation; satisfiability solvers; description logics; constraint programming; conce...
This volume features the complete text of all regular papers, posters, and summaries of symposia presented at the 16th annual meeting of the Cognitive Science Society.
As children acquire arithmetic skills, they often develop 'bugs'--small, local misconceptions that cause systematic errors. Mind Bugs combines a novel cognitive simulation process with careful hypothesis testing to explore how mathematics students acquire procedural skills in instructional settings, focusing in particular on these procedural misconceptions and what they reveal about the learning process.
This book describes the new generation of discrete choice methods, focusing on the many advances that are made possible by simulation. Researchers use these statistical methods to examine the choices that consumers, households, firms, and other agents make. Each of the major models is covered: logit, generalized extreme value, or GEV (including nested and cross-nested logits), probit, and mixed logit, plus a variety of specifications that build on these basics. Simulation-assisted estimation procedures are investigated and compared, including maximum stimulated likelihood, method of simulated moments, and method of simulated scores. Procedures for drawing from densities are described, includ...
Model-Based Approaches to Learning provides a new perspective called learning by system modeling. This book explores the learning impact of students when constructing models of complex systems.