Abstract In this chapter we provide an overview of probabilistic logic
networks (PLN), including our motivations for developing PLN and the
guiding principles underlying PLN. We discuss foundational choices we
made, introduce PLN knowledge representation, and briefly introduce
inference rules and truth-values. We also place PLN in context with
other approaches to uncertain inference. 1.1 Motivations This book
presents Probabilistic Logic Networks (PLN), a systematic and pragmatic
framework for computationally carrying out uncertain reasoning - r-
soning about uncertain data, and/or reasoning involving uncertain
conclusions. We begin with a few comments about why we believe this is
such an interesting and important domain of investigation. First of all,
we hold to a philosophical perspective in which "reasoning" - properly
understood - plays a central role in cognitive activity. We realize that
other perspectives exist; in particular, logical reasoning is sometimes
construed as a special kind of cognition that humans carry out only
occasionally, as a deviation from their usual (intuitive, emotional,
pragmatic, sensorimotor, etc.) modes of thought. However, we consider
this alternative view to be valid only according to a very limited
definition of "logic." Construed properly, we suggest, logical reasoning
may be understood as the basic framework underlying all forms of
cognition, including those conventionally thought of as illogical and
irrational.
