The idea of this book grew out of a symposium that was held at
Stony Brook in September 2012 in celebration of David S.Warren's
fundamental contributions to Computer Science and the area of Logic
Programming in particular. Logic Programming (LP) is at the nexus
of Knowledge Representation, Artificial Intelligence, Mathematical
Logic, Databases, and Programming Languages. It is fascinating and
intellectually stimulating due to the fundamental interplay among
theory, systems, and applications brought about by logic. Logic
programs are more declarative in the sense that they strive to be
logical specifications of "what" to do rather than "how" to do it,
and thus they are high-level and easier to understand and maintain.
Yet, without being given an actual algorithm, LP systems implement
the logical specifications automatically. Several books cover the
basics of LP but focus mostly on the Prolog language with its
incomplete control strategy and non-logical features. At the same
time, there is generally a lack of accessible yet comprehensive
collections of articles covering the key aspects in declarative LP.
These aspects include, among others, well-founded vs. stable model
semantics for negation, constraints, object-oriented LP, updates,
probabilistic LP, and evaluation methods, including top-down vs.
bottom-up, and tabling. For systems, the situation is even less
satisfactory, lacking accessible literature that can help train the
new crop of developers, practitioners, and researchers. There are a
few guides onWarren's Abstract Machine (WAM), which underlies most
implementations of Prolog, but very little exists on what is needed
for constructing a state-of-the-art declarative LP inference
engine. Contrast this with the literature on, say, Compilers, where
one can first study a book on the general principles and algorithms
and then dive in the particulars of a specific compiler. Such
resources greatly facilitate the ability to start making meaningful
contributions quickly. There is also a dearth of articles about
systems that support truly declarative languages, especially those
that tie into first-order logic, mathematical programming, and
constraint solving. LP helps solve challenging problems in a wide
range of application areas, but in-depth analysis of their
connection with LP language abstractions and LP implementation
methods is lacking. Also, rare are surveys of challenging
application areas of LP, such as Bioinformatics, Natural Language
Processing, Verification, and Planning. The goal of this book is to
help fill in the previously mentioned void in the LP literature. It
offers a number of overviews on key aspects of LP that are suitable
for researchers and practitioners as well as graduate students. The
following chapters in theory, systems, and applications of LP are
included.
General
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