This collection represents the primary reference work for researchers and students in the area of Temporal Reasoning in Artificial Intelligence. Temporal reasoning has a vital role to play in many areas, particularly Artificial Intelligence. Yet, until now, there has been no single volume collecting together the breadth of work in this area. This collection brings together the leading researchers in a range of relevant areas and provides an coherent description of the breadth of activity concerning temporal reasoning in the filed of Artificial Intelligence. Key Features: - Broad range: foundations; techniques and applications - Leading researchers around the world have written the chapters - Covers many vital applications - Source book for Artificial Intelligence, temporal reasoning - Approaches provide foundation for many future software systems · Broad range: foundations; techniques and applications · Leading researchers around the world have written the chapters · Covers many vital applications · Source book for Artificial Intelligence, temporal reasoning · Approaches provide foundation for many future software systems

The handbook of logic in artificial intelligence and logic programming is an international reference work in five volumes. It has been created in response to the growing need for an in-depth survey of the applications of logic in artifical intelligence. The dramatic increase in research in recent years means that logic is now widely recognised as one of the foundational disciplines of computing and has found applications in virtually all aspects of the subject, from software engineeringand hardware to programming languages and artificial intelligence. Yet this handbook is the first authoritative text to pull together the accumulated research level material, and as such is a unique and invaluable reference source. The Handbook of Logic in Artificial Intelligence and Logic Programming is a multi-author multi-volume work covering all the major areas of application of logic to artifical intelligence and logic programming. The Handbook comprises five volumes, each an in-depth overview of one of the major topics in this area. It is the result of years of co-operative effort by internationally renowned researchers in the field, and will no doubt be the standard reference work in artifical intelligence and logic programming for years to come- essential reading for all those interested in this subject. This latest volume covers, among other subjects, epistemic reasoning, time and change for AI, and temporal nonmonotonic reasoning.

Handbook of Automated Reasoning.

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; conceptual graphs; nonmonotonic reasoning; model-based problem solving; and Bayesian networks. Part II focuses on classes of knowledge and specialized representations, with chapters on temporal representation and reasoning; spatial and physical reasoning; reasoning about knowledge and belief; temporal action logics; and nonmonotonic causal logic. Part III discusses Knowledge Representation in applications such as question answering; the semantic web; automated planning; cognitive robotics; multi-agent systems; and knowledge engineering. This book is an essential resource for graduate students, researchers, and practitioners in knowledge representation and AI. * Make your computer smarter * Handle qualitative and uncertain information * Improve computational tractability to solve your problems easily

Solving challenging computational problems involving time has been a critical component in the development of artificial intelligence systems almost since the inception of the field. This book provides a concise introduction to the core computational elements of temporal reasoning for use in AI systems for planning and scheduling, as well as systems that extract temporal information from data. It presents a survey of temporal frameworks based on constraints, both qualitative and quantitative, as well as of major temporal consistency techniques. The book also introduces the reader to more recent extensions to the core model that allow AI systems to explicitly represent temporal preferences and temporal uncertainty. This book is intended for students and researchers interested in constraint-based temporal reasoning. It provides a self-contained guide to the different representations of time, as well as examples of recent applications of time in AI systems.