Symbolic and Quantitative Approaches to Reasoning and Uncertainty: European Conference, ECSQARU '95 Fribourg, Switzerland, July 3–5, 1995 Proceedings

440 Pages·1995·7.429 MB·English

by  Natasha Alechina (auth.), Christine Froidevaux, Jürg Kohlas (eds.)

Lecture Notes in Artificial Intelligence 946
Subseries of Lecture Notes in Computer Science
Edited by .J G. Carbonell and J. Siekmann
Lecture Notes in Computer Science
Edited by G. Goos, J. Hartmanis and J. van Leeuwen
Christine Froidevaux Jtirg Kohlas (Eds.)
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European Conference, ECSQARU '95
Fribourg, Switzerland, July 3-5, 5991
Proceedings
regnirpS
Series Editors
Jaime G. Carbonell
School of Computer Science, Carnegie Mellon University
Pittsburgh, PA 15213-3891, USA
J6rg Siekmann
University of Saarland, German Research Center for AI (DFKI)
Stuhlsatzenhausweg 3, D-66123 Saarbrticken, Germany
Volume Editors
Christine Froidevaux
LRI, University of Paris-Sud
Bat. 490, F-91405 Orsay, France
Jtirg Kohlas
Institute of Informatics, University of Fribourg
Site Regina Mundi, CH-1700 Fribourg, Switzerland
CIP data applied for
Die Deutsche Bibliothek - CIP-Einheitsaufnahme
Symbolic quantitative approaches to reasoning and uncertainty :
European conference ; proceedings / ECSQARU '95, Fribourg,
Switzerland, July 3 - 5, 1995. Christine Froidevaux ; Ji~rg Kohlas
(ed.). - Berlin ; Heidelberg ; New York : Springer, 1995
(Lecture notes in computer science ; Vol. 946 : Lecture notes in
artificial intelligence)
ISBN 3-540-60112-0
NE: Froidevaux, Christine Hrsg.; ECSQARU <1995, Fribourg>; GT
CR Subject Classification (1991): 1.2.3, F.4.1
ISBN 3-540-60112-0 Springer-Verlag Berlin Heidelberg New York
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(cid:14)9 Springer-Verlag Berlin Heidelberg 1995
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SPIN 10486402 06/3142 - 5 4 3 2 1 0 Printed on acid-free paper
Preface
This volume contains the papers accepted for presentation at ECSQARU'95, the
European Conference on Symbolic and Quantitative Approaches to Reasoning
and Uncertainty, held at the University of Fribourg, Switzerland, July 3 to 5,
.5991
In recent years it has become apparent that an important part of the theory
of Artificial Intelligence is concerned with reasoning on the basis of uncertain,
vague, incomplete, or inconsistent information. Classical logic and probability
theory are only partially adequate for this and a variety of other formalisms both
symbolic and numerical have been developed, some of the most familiar being
nomonotonic logics, modal logics, fuzzy sets, possibility theory, belief functions,
evidence theory, and dynamic models of reasoning such as belief revision and
Bayesian networks.
ECSQARU'95 is the third in a biennial series of conferences which provide a
forum for the development, synthesis, and discussion of approaches to reasoning
and uncertainty. The first was held in Marseille in 1991 (LNCS 548) and the
second in Granada in 1993 (LNCS 747). Like its predecessors, ECSQARU'95 is
sponsored and organized by the DRUMS consortium (Defeasible Reasoning and
Uncertainty Management Systems, ESPRIT III Basic Research Action 6156),
which involves twenty-one European partners, and by the European Society for
Automated Practical Reasoning and Argumentation (ESAPRA).
The Executive Scientific Committee for the conference consisted of John
Bell (QMW, University of London), Christine Froidevaux (LRI, University of
Paris 11), Jfirg Kohlas (University of Fribourg), Rudolf Kruse (University of
Braunschweig), Ramon Lopez de Mantaras (CEAB, Blanes), Erik Sandewall
(University of LinkSping), Philippe Smets (IRIDIA, University of Brussels). We
gratefully acknowledge the contribution of the many referees, who were involved
in the reviewing process. Finally we would like to thank the University of Fri-
bourg for the support it gave the organizers. Jfirg Kohlas was responsible for
the local organization in Fribourg.
April 1995 Christine Froidevaux, Jfirg Kohlas
Co-chairs
IV
Scientific Committee
J. Baldwin; J. Bell , F. Bergadano, Ph. Besnard, J. Bigham, B. Bouchon-
Meunier, G. Brewka, M. Delgado, P. Doherty, D. Dubois, L. Patinas del Cerro,
J. Fox, C. Proidevaux, D.M. Gabbay, P. G~irdenfors, E. Gregoire, P. tts
H. Herre, A. Hunter, J. Kohlas, P. Krause, R. Kruse, S. Lauritzen, R. Lopez
de Mantaras, W. Lukasiewicz, E. Mamdani, J.J. Meyer, S. Moral, B. Nebel, It.
Prade, M. Reinfrank, L. Saitta, E. Sandewall, T. Schaub, K. Schlechta, P. Siegel,
P. Smets, K. Sundermeyer, Y.H. Tan, J. Treur, D. Vermeir, F. Voorbraak, N.
Wilson, C. Witteveen, H.-J. Zimmermann.
Additional Referees
J.L. Arcos, S. Benferhat, A. Bonarini, A. Casales, L.M. de Campos, T. Draken-
gren, D. Driankov , G. Escalada-Imaz, F. Esteva, P. Fonck, P. Garcia, J. Geb-
hardt, L. Godo, D. Heerg, A. Herzig, W. van der ttoek, R. Kennes, U. Kjaerulff,
F. Klawonn, F. Levy, T. Linke, J. Mengin, P.J.A. Monney, K.G. Olesen, U.G.
Oppel, S. Parsons, E. Plaza, R.O. Rodriguez, A. Saffiotti, M. Sergot, C. Sierra,
J. Valdes, J.L. Verdegay, W. Wobcke., It. Xu
Table of Contents
For All Typical .................................
N. Alechina
A Posteriori Knowledge: From Ambiguous Knowledge and Undefined
Information to Knowledge .........................
M. Alvarado
Modeling Uncertain Relational Knowledge: The AV-Quantified
Production Rules Approach ......................... 18
P. Baroni, .G Guida, .S Mussi
Multiple Database Logic ............................ 28
M.R.F. Benevides
A Local Approach to Reasoning under Inconsistency in Stratified
Knowledge Bases ............................... 36
.S Benferhat, .D Dubois, .H Prade
Quasi-Classical Logic: Non-Trivializable Classical Reasoning from
Inconsistent Information .......................... 44
P. Besnard, A. Hunter
A Cost Bounded Possibilistic ATMS ..................... 52
.J Bigham, .Z Luo, .D Banerjee
A Substructural Connective for Possibilistic Logic ............. 60
L. Boldrin
Chain Graphs: Semantics and Expressiveness ................ 69
R.R. Bouckaert, .M Studen~
Axiomatic Treatment of Possibilistic Independence ............. 77
L.M. ed Campos, .J Gebhardt, R. Kruse
Parametric Structure of Probabilities in Bayesian Networks ........ 89
E. Castillo, J.M. Gutidrrez, A.S. Hadi
From Non-Monotonic Syntax-Based Entailment to Preference-Based
Argumentation ................................ 99
.C Cayrol
IIIV
Non-Monotonic Syntax-Based Entailment: A Classification of
Consequence Relations ........................... 107
C. Cayrol, M.-C. Lagasquie-Schiex
Local M6bius Transforms of Monotone Capacities ............. 115
A. Chateauneuf, J.-Y. Ja~ray
Automated Reasoning with Merged Contradictory Information whose
Reliability Depends on Topics ....................... 125
L. Cholvy
Linking Transition-Based Update and Base Revision ............ 133
M.-O. Cordier, J. Lang
Prioritized Transitions for Updates ...................... 142
M.-O. Cordier, P. Siegel
Probabilistic Satisfiability and Decomposition ................ 151
G.-B. Douanya Nguetsg, P. Hansen, B. Jaumard
Update Postulates without Inertia ...................... 162
D. Dubois, F. Dupin ed St.-Cyr, .H Prade
Similarity-Based Consequence Relations ............ ....... 171
D. Dubois, F. Es~eva, P. Garcia, L. Godo, H. Prade
Epistemic Approach to Actions with Typical Effects ............ 180
B. Dunin-Kfplicz, A. l~adzikowska
Nonmonotonic Belief State Frames and Reasoning Frames ......... 189
J. Engelfriet, H. Herre, J. Treur
A Bayesian Network Based Learning System ................ 197
K.J. Ezawa, T. Schuermann
Specificity by Default ............................. 207
P. Geerts, D. Vermeir
Constructing Flexible Dynamic Belief Networks from First-Order
Probabilistic Knowledge Bases ....................... 217
.15 Glesner, .D KoUer
On the Formalism of Stochastic Logic ..................... 227
S. Guiasu
Using Default Logic in Information Retrieval ................ 235
A. Hunter
Ix
Sensitivity Analysis in Bayesian Networks .................. 243
F.V. Jensen, S.H. Aldenryd, K.B. Jensen
Bayesian Approximation and Invariance of Bayesian Belief Functions . . . 251
A.V. Joshi, S.C. Sahasrabudhe, K. Shankar
Model-Based Diagnostics Using Hints .................... 259
J. Kohlas, P.J.A. Monney, R. Haenni, N. Lehmann
An Axiomatic Approach to Extensional Probability Measures ....... 267
L Kramosil
The Dynamics of Default Reasoning ..................... 277
B. van Linder, W. van der Hock, J.-J. .hC Meyer
Lemma Handling in Default Logic Theorem Provers ............ 285
T. Linke, T. Schaub
Uncertain Reasoning in Concept Lattices .................. 293
T. Lukasiewicz
A Theorem Prover for Default Logic based on Prioritized Conflict
Resolution and an Extended Resolution Principle ............ 301
J. Mengin
A Theorem Prover for Lukaszewicz' Open Default Theory ......... 311
P. Nicolas, B. Dural
Two Different Types of Discontinuity of Bayesian Learning in Causal
Probabilistic Networks ........................... 320
.U G. Oppel
Revision in Extended Propositional Calculus ................ 328
.O Papini, A. Rauzy
Using Qualitative Uncertainty in Protein Topology Prediction ....... 336
S. Parsons
Circumscribing Features and Ftuents: Reasoning about Action with
Default Effects ................................ 344
A. Radzikowska
Using Maximum Entropy to Compute Marginal Probabilities in a Causal
Binary Tree Need Not Take Exponential Time .............. 352
P.C. Rhodes, G.R. Garside
Yet Some More Considerations on Cumulativity in Default Logics .... 364
V. Risch
X
A Two-Stage Approach to First Order Default Reasoning ......... 379
K. Sehlech~a
A Reduction of the Theory of Confirmation to the Notions of Distance and
Measure .................................... 387
K. Schlechta
Cluster-Based Specification Techniques in Dempster-Shafer Theory .... 395
J. Schubert
AND/OR Trees for Knowledge Representation ............... 405
L. State, R. State
Why Defeasible Deontic Logic needs a Multi Preference Semantics .... 412
Y.-H. Tan, L.W.N. van der Torte
Numeric Defaults: About an Expressive First-Order Framework for
Reasoning with Infinitesimal Probabilities ................. 420
E. Weydert
Author Index .................................. 429
For all typical
Natasha Alechina
FWI, University of Amsterdam
.1P Muidergracht 24, 1018 TV Amsterdam
The Netherlands
e-mail [email protected]
Abstract. In the paper a binary quantifier Hx(7~ , r is studied. It can
be read as 'typically, ~'s are r After investigating some formal prop-
erties of this quantifier, we compare the resulting logic with predicate
conditional logic and discuss possible applications of binary quantifiers
in formalizing defeasible reasoning.
1 Introduction
The present paper contains one more suggestion concerning a formal way to treat
statements like '~o's are (normally, typically) r common sense generalizations
which can be used as premises in defeasible reasoning. The common feature of
those statements is that they ca~ be true while some exceptions (7~'s which are
not r are present. In the paper they are formalized with the use of a binary
generalized quantifier H, with IIx(v(x), r informally meaning 'for all typ-
ical ~0's bt is true'. A related work in this respect is, first of all, Badaloni and
Zanardo (1990), (1991). Another connection (which was already discussed by
Badaloni and Zanardo) is the way such statements are formalized in the theory
of circumscription: if V(x) and x is not abnormal, then r In section 4 possi-
ble applications of the logic with generalized quantifiers to defeasible reasoning
are sketched. In section 3 we compare two possible readings of commonsense
generalizations: 'normally, T implies r (cf. Morreau (1992)) and 'all typical s'~7
are ~b's'.
2 Language and models
Consider a first-order language Lv// with a binary generalized quantifier H. A
well-formed formula is defined as usual; if ~7 and r are well-formed formulas, so
r
A model M for LVH is a triple < D,T,I >, where D is a domain, I an
interpretation function, and T is a function which takes a subset S of D and
returns a set ST _C D (the set of 'typical S's'). T obeys only one constraint,
namely
STC_S.
The truth definition for the generalized quantifier reads as follows:
M ~ IIx(~o,r r {d: M ~" 7~x/a~)T _C {d: U ~" r