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.
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