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.
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