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Abstraction is a fundamental mechanism underlying both human and
artificial perception, representation of knowledge, reasoning and
learning. This mechanism plays a crucial role in many disciplines,
notably Computer Programming, Natural and Artificial Vision,
Complex Systems, Artificial Intelligence and Machine Learning, Art,
and Cognitive Sciences. This book first provides the reader with an
overview of the notions of abstraction proposed in various
disciplines by comparing both commonalities and differences. After
discussing the characterizing properties of abstraction, a formal
model, the KRA model, is presented to capture them. This model
makes the notion of abstraction easily applicable by means of the
introduction of a set of abstraction operators and abstraction
patterns, reusable across different domains and applications. It is
the impact of abstraction in Artificial Intelligence, Complex
Systems and Machine Learning which creates the core of the book. A
general framework, based on the KRA model, is presented, and its
pragmatic power is illustrated with three case studies: Model-based
diagnosis, Cartographic Generalization, and learning Hierarchical
Hidden Markov Models.
Abstraction is a fundamental mechanism underlying both human and
artificial perception, representation of knowledge, reasoning and
learning. This mechanism plays a crucial role in many disciplines,
notably Computer Programming, Natural and Artificial Vision,
Complex Systems, Artificial Intelligence and Machine Learning, Art,
and Cognitive Sciences. This book first provides the reader with an
overview of the notions of abstraction proposed in various
disciplines by comparing both commonalities and differences. After
discussing the characterizing properties of abstraction, a formal
model, the KRA model, is presented to capture them. This model
makes the notion of abstraction easily applicable by means of the
introduction of a set of abstraction operators and abstraction
patterns, reusable across different domains and applications. It is
the impact of abstraction in Artificial Intelligence, Complex
Systems and Machine Learning which creates the core of the book. A
general framework, based on the KRA model, is presented, and its
pragmatic power is illustrated with three case studies: Model-based
diagnosis, Cartographic Generalization, and learning Hierarchical
Hidden Markov Models.
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Abstraction, Reformulation and Approximation - 6th International Symposium, SARA 2005, Airth Castle, Scotland, UK, July 26-29, 2005, Proceedings (Paperback, 2005 ed.)
Jean-Daniel Zucker, Lorenza Saitta
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R1,719
Discovery Miles 17 190
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Ships in 10 - 15 working days
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This volume contains the proceedings of the 6th Symposium on
Abstraction, Reformulation and Approximation (SARA 2005). The
symposium was held at Airth Castle, Scotland, UK, from July 26th to
29th, 2005, just prior to the IJCAI 2005 conference in Edinburgh.
Previous SARA symposia took place at JacksonHole in Wyoming, USA
(1994), Ville d'Estrel in Qubec, Canada (1995), Asilomar in
California, USA (1998), Horseshoe Bay, Texas, USA (2000), and
Kananaskis, Alberta, Canada (2002). This was then the ?rst time
that the s- posium was held in Europe. Continuing the tradition
started with SARA 2000, the proceedings have been published in the
LNAI series of Springer. Abstractions, reformulations and
approximations (AR&A) have found app- cationsin
avarietyofdisciplines andproblems, including
constraintsatisfaction, design, diagnosis, machine learning,
planning, qualitative reasoning, scheduling, resource allocation
and theorem proving, but are also deeply rooted in philo- phy and
cognitive science. The papers in this volume capture a
cross-section of the various facets of the ?eld and of its
applications. One of the primary uses of AR&A is oriented to
overcome computational intractability. AR&A techniques,
however, have also proved useful for knowledge acquisition,
explanation and other applications, as papers in this volume also
illustrate.
This book contains the papers presented at the 2nd IPMU Conference,
held in Urbino (Italy), on July 4-7, 1988. The theme of the
conference, Management of Uncertainty and Approximate Reasoning, is
at the heart of many knowledge-based systems and a number of
approaches have been developed for representing these types of
information. The proceedings of the conference provide, on one
hand, the opportunity for researchers to have a comprehensive view
of recent results and, on the other, bring to the attention of a
broader community the potential impact of developments in this area
for future generation knowledge-based systems. The main topics are
the following: frameworks for knowledge-based systems:
representation scheme, neural networks, parallel reasoning schemes;
reasoning techniques under uncertainty: non-monotonic and default
reasoning, evidence theory, fuzzy sets, possibility theory,
Bayesian inference, approximate reasoning; information theoretical
approaches; knowledge acquisition and automated learning.
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