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Computerscientistscreatemodelsofaperceivedreality.ThroughAItechniques,
these models aim at providing the basic support for emulating
cognitive - havior such as reasoning and learning, which is one of
the main goals of the AI research e?ort. Such computer models are
formed through the interaction of various acquisition and inference
mechanisms: perception, concept learning, conceptual clustering,
hypothesis testing, probabilistic inference, etc., and are
represented using di?erent paradigms tightly linked to the
processes that use them. Among these paradigms let us cite:
biological models (neural nets, genetic programming), logic-based
models (?rst-order logic, modal logic, rule-based s- tems), virtual
reality models (object systems, agent systems), probabilistic m-
els(Bayesiannets, fuzzylogic),
linguisticmodels(conceptualdependencygraphs, language-based
representations), etc.
OneofthestrengthsoftheConceptualGraph(CG)theoryisitsversatilityin
terms of the representation paradigms under which it falls. It can
be viewed and therefore used, under di?erent representation
paradigms, which makes it a p- ular choice for a wealth of
applications. Its full coupling with di?erent cognitive processes
lead to the opening of the ?eld toward related research communities
such as the Description Logic, Formal Concept Analysis, and
Computational Linguistic communities. We now see more and more
research results from one community enrich the other, laying the
foundations of common philosophical grounds from which a successful
synergy can emerg
Artificial Intelligence and cognitive science are the two fields
devoted to the study and development of knowledge-based systems
(KBS). Over the past 25years, researchers have proposed several
approaches for modeling knowledge in KBS, including several kinds
of formalism such as semantic networks, frames, and logics. In the
early 1980s, J.F. Sowa introduced the conceptual graph (CG) theory
which provides a knowledge representation framework consisting of a
form of logic with a graph notationand integrating several features
from semantic net and frame representations. Since that time,
several research teams over the world have been working on the
application and extension of CG theory in various domains ranging
from natural language processing to database modeling and machine
learning. This volume contains selected papers fromthe
international conference on Conceptual Structures held in the city
of Quebec, Canada, August 4-7, 1993. The volume opens with invited
papers by J.F. Sowa, B.R. Gaines, and J. Barwise.
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