Analogical reasoning is known as a powerful mode for drawing
plausible conclusions and solving problems. It has been the topic
of a huge number of works by philosophers, anthropologists,
linguists, psychologists and computer scientists. As such, it has
been early studied in artificial intelligence, with a particular
renewal of interest in the last decade.
The present volume provides a structured view of current
research trends on computational approaches to analogical
reasoning. It starts with an overview of analogical reasoning with
an extensive bibliography.
The 14 collected contributions cover a large scope of issues.
First, the use of analogical proportions and analogies is explained
and discussed in various natural language processing problems, as
well as in automated deduction. Then, different formal frameworks
for handling analogies are presented, dealing with case-based
reasoning, heuristic-driven theory projection, commonsense
reasoning about incomplete rule bases, logical proportions induced
by similarity and dissimilarity indicators and analogical
proportions in lattice structures. Lastly, the volume reports case
studies and discussions about the use of similarity judgments and
the process of analogy making, at work in IQ tests, creativity or
other cognitive tasks.
This volume gathers fully revised and expanded versions of
papers presented at an international workshop as well as invited
contributions. All chapters have benefited of a thorough peer
review process."
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