A guided tour of artificial intelligence research. Vol 2

529 Pages·2020·3.822 MB·English

by  Marquis P

Pierre Marquis
Odile Papini
Henri Prade
Editors
A Guided Tour
Artificial
of

Intelligence
Research
2
A I Algorithms
fi
A Guided Tour of Arti cial Intelligence Research
Pierre Marquis Odile Papini Henri Prade
(cid:129) (cid:129)
Editors
fi
A Guided Tour of Arti cial
Intelligence Research
Volume II: AI Algorithms
123
Editors
Pierre Marquis OdilePapini
CRIL-CNRS, Universitéd’Artois AixMarseille Université,Universitéde
andInstitut Universitaire deFrance Toulon, CNRS, LIS
Lens,France Marseille, France
HenriPrade
IRIT
CNRSand UniversitéPaulSabatier
Toulouse, France
ISBN978-3-030-06166-1 ISBN978-3-030-06167-8 (eBook)
https://doi.org/10.1007/978-3-030-06167-8
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General Presentation of the Guided Tour
fi
of Arti cial Intelligence Research
ArtificialIntelligence(AI)ismorethansixtyyearsold.Ithasasingularpositionin
thevastfieldsofcomputerscienceandengineering.ThoughAIisnowadayslargely
acknowledged for various developments and a number of impressive applications,
its scientific methods, contributions, and tools remain unknown to a large extent,
even in the computer science community. Notwithstanding introductory mono-
graphs, there do not exist treatises offering a detailed, up-to-date, yet organized
overview of the whole range of AI researches. This is why it was important to
review the achievements and take stock of the recent AI works at the international
level. This is the main goal of this A Guided Tour of Artificial Intelligence
Research.
This set of books is a fully revised and substantially expanded version, of a
panorama of AI research previously published in French (by Cépaduès, Toulouse,
France, in 2014), with a number of entirely new or renewed chapters. For such a
huge enterprise, we have largely benefited the support and expertise of the
French AI research community, as well as of colleagues from other countries. We
heartilythankallthecontributorsfortheircommitmentsandworks,withoutwhich
this quite special venture would not have come to an end.
Each chapter is written by one or several specialist(s) of the area considered.
Thistreatiseisorganizedintothreevolumes:Thefirstvolumegatherstwenty-three
chapters dealing with the foundations of knowledge representation and reasoning
formalization including decision and learning; the second volume offers an
algorithm-oriented view of AI, in fourteen chapters; the third volume, in sixteen
chapters, proposes overviews of a large number of research fields that are in
relation to AI at the methodological or at the applicative levels.
vii
viii GeneralPresentationoftheGuidedTourofArtificialIntelligenceResearch
Although each chapter can be read independently from the others, many
cross-referencesbetweenchapterstogetherwithaglobalindexfacilitateanonlinear
reading of the volumes. In any case, we hope that readers will enjoy browsing the
proposed surveys, and that some chapters will tease their curiosity and stimulate
their creativity.
July 2018 Pierre Marquis
Odile Papini
Henri Prade
Contents
Heuristically Ordered Search in State Graphs . . . . . . . . . . . . . . . . . . . . 1
Henri Farreny
Meta-heuristics and Artificial Intelligence . . . . . . . . . . . . . . . . . . . . . . . 27
Jin-Kao Hao and Christine Solnon
Automated Deduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
Thierry Boy de la Tour, Ricardo Caferra, Nicola Olivetti, Nicolas Peltier
and Camilla Schwind
Logic Programming . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83
Arnaud Lallouet, Yves Moinard, Pascal Nicolas and Igor Stéphan
Reasoning with Propositional Logic: From SAT Solvers
to Knowledge Compilation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115
Laurent Simon
Constraint Reasoning. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153
Christian Bessiere
Valued Constraint Satisfaction Problems . . . . . . . . . . . . . . . . . . . . . . . 185
Martin C. Cooper, Simon de Givry and Thomas Schiex
Belief Graphical Models for Uncertainty Representation
and Reasoning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 209
Salem Benferhat, Philippe Leray and Karim Tabia
Languages for Probabilistic Modeling Over Structured
and Relational Domains . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 247
Fabio Gagliardi Cozman
Planning in Artificial Intelligence. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 285
Régis Sabbadin, Florent Teichteil-Königsbuch and Vincent Vidal
ix
x Contents
Artificial Intelligence for Games. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 313
Bruno Bouzy, Tristan Cazenave, Vincent Corruble and Olivier Teytaud
Designing Algorithms for Machine Learning and Data Mining . . . . . . . 339
Antoine Cornuéjols and Christel Vrain
Formal Concept Analysis: From Knowledge Discovery
to Knowledge Processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 411
SébastienFerré,MarianneHuchard,MehdiKaytoue,SergeiO.Kuznetsov
and Amedeo Napoli
Constrained Clustering: Current and New Trends. . . . . . . . . . . . . . . . . 447
Pierre Gançarski, Thi-Bich-Hanh Dao, Bruno Crémilleux,
Germain Forestier and Thomas Lampert
Afterword—Artificial Intelligence and Operations Research. ..... .... 485
Philippe Chrétienne
Index .... .... .... .... .... ..... .... .... .... .... .... ..... .... 493
Preface: AI Algorithms
The simulation of “intelligent” behaviors, which is the main purpose of AI, is a
complex task. It requires the identification of a number of concepts and processes
on which such behaviors are anchored. These concepts (beliefs, preferences,
actions,etc.)andprocesses(learning,inference,decision,etc.)needtobemodeled.
Itisthusmandatorytodefinemodelsthataresuitedtotherealityonewantstodeal
with (in particular, those models must take account as much as possible for the
availablepiecesofinformation)andthatoffer“goodproperties”(fromanormative
point of view or from a descriptive point of view). The concepts, once modeled,
must be represented. This requires to define and study representation languages
(a piece of information pertaining to a given model having in general several
possiblerepresentations).Thechoiceofasuitablerepresentationlanguagetypically
dependsonseveralcriteria(expressiveness,succinctness,amongothers).Processes
must also be modeled to render a computer simulation feasible from the repre-
sentations at hand. This involves the design, study, and evaluation of dedicated
algorithms.
ThisvolumeoftheguidedtourofAIresearchfocusesonthislastissueandaims
topresentinfourteenchaptersthemainfamiliesofalgorithmsdevelopedorusedin
AI to learn, to infer, and to decide. The first two chapters (Chapters “Heuristically
Ordered Search in State Graphs” and “Meta-heuristics and Artificial Intelligence”)
deal with heuristic search and meta-heuristics, two families of approaches forming
thebackbone ofmany AIalgorithms. Thenextthreechaptersareaboutalgorithms
that process logic-based representations, and they present, respectively, the com-
putational problems encountered in automatic deduction (Chapter “Automated
Deduction”), those of logic programming (Chapter “Logic Programming”) already
evoked in the preface of this volume, and finally those of classical propositional
logic (including the famous SAT problem, which plays a key role in complexity
theory). The next three chapters are focused on algorithms suited to graph-based
representations: standard constraint networks (Chapter “Constraint Reasoning”),
valued constraint networks (Chapter “Valued Constraint Satisfaction Problems”),
probabilistic and non-probabilistic graphical models (Chapter “Belief Graphical
Models for Uncertainty Representation and Reasoning”). Chapter “Languages for
xi