Learning and Reasoning with Complex Representations: PRICAI'96 Workshops on Reasoning with Incomplete and Changing Information and on Inducing Complex Representations Cairns, Australia, August 26–30, 1996 Selected Papers

293 Pages·1998·6.94 MB·English

by  Srinivas Padmanabhuni

Lecture Notes in Artificial Intelligence 1359
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
GrigorisA ntoniou Aditya K. Ghose
Mirostaw Truszczyfiski (Eds.)
gninraeL dna gninosaeR
htiw xelpmoC
snoitatneserpeR
PRICAI'96 Workshops
on Reasoning with Incomplete
dna Changing Information
dna on Inducing Complex Representations
Cairns, Australia, August 26-30, 1996
Selected Papers
r e g n~ i r p S
Series Editors
Jaime G. Carbonetl, Carnegie Mellon University, Pittsburgh, PA, USA
J6rg Siekmann, University of Saartand, Saarbrticken, Germany
Volume Editors
Grigoris Antoniou
School of Computing and InformationTechnology, Griffith University
Nathan, QLD 4111, Australia
E-mail: [email protected],edu.au
Aditya K. Ghose
Department of Business Systems, University of Wotlongong
Wollongong, NSW 2522, Australia
E-mail: [email protected]
Mirosiaw Truszczyfiski
Computer Science Department, University of Kentucky
773C Anderson Hall, Lexington, KY40506-0046, USA
E-mail: mirek cs.engr.uky.edu @
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Die Deutsche Bibliothek - CIP-Einheitsaufnahme
gninraeL dna gninosaeR complex with representations : selected
papers / PRICAI'96, Workshops on Reasoning with Incomplete and
Changing Infomlalion and on Inducing Complex Representations,
Cairns, Australia, August 26 - 30, 1996. Grigoris Aaatoniou ... (ed.). -
Berlin ; Heidelberg ; New York ; Barcelona ; Budapest ; Hong Kong
; London ; Milan ; Paris ; Santa Clara ; Singapore ; Tokyo : Springer,
1998
(Lecture notes in computer ~ienee ; Vol. 1359 : Lecture notes in
artificial intelligence)
ISBN 3-540-64413-X
CR Subject Classification (1991): 1.2
ISSN 0302-9743
ISBN 3-540-64413-X Springer-Verlag Berlin Heidelberg New York
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Preface
The Pacific Rim International Conference on Artificial Intelligence (PRI-
CAI) was set up to facilitate and stimulate the exchange of AI research,
information, and technology in the Pacific Rim, and serves as a sister
conference to its North American and European counterparts, AAAI and
ECAI. This volume is based on two workshops that took place at PRI-
CAI'96 in Cairns, Australia:
• The Workshop on Reasoning with Incomplete and Changing Infor-
mation, and
• The Workshop on Inducing Complex Representations.
Reasoning with incomplete information deals with a central problem in
AI, namely the imperfect nature of knowledge in most problems. In-
complete information refers to situations where the information actually
required for a decision to be made is not available. In such cases in-
telligent systems need to make plausible conjectures. Another source of
imperfect knowledge is that intelligent systems usually don't operate in a
static world, rather their environment changes over time. Thus intelligent
systems need to reason with changing information. This volume includes
papers which describe a variety of methods, including nonmonotonic rea-
soning, belief networks and belief revision.
The Workshop on Inducing Complex Representations was moti-
vated by the development, over the past few years, of several complex in-
ductive paradigms where the underlying knowledge representation is more
complex than that of the conventional propositional concept-learning sys-
tems and hence potentially more useful in real-life applications. Much of
this activity has focused on the induction of logic programs or Horn clause
theories, giving rise to the area of Inductive Logic Programming. More re-
cently, there has been growing interest in the problem of inducing theories
in a variety of distinct but related formalisms. These include constraint-
based representations, includingc onstraint logic programs, nonmonotonic
theories, spatial representations and equational representations. This
workshop set out to bring together researchers from all of these areas,
to identify commonalities in the methods utilised and to encourage cross-
fertilization of ideas. The response to the idea of such a forum was quite
encouraging. This volume contains papers representing all of the areas
mentioned above.
lY
The idea of reasoning with non-standard representations, such as
those required for dealing with incomplete information, cannot be di-
vorced from the problem of learning using such representations, and vice
versa. What we have attempted to do in this volume is highlight the
common underlying threads by placing research in both areas in juxta-
position. To our knowledge, this is the first time an attempt such as this
has been made. We wish to encourage, through this volume, an emerging
trend that involves greater synergy between learning and reasoning with
novel, complex, non-standard representations. The volume contains ex-
tended versions of selected papers presented at the two workshops. Each
of them was scrutinized by three referees from the program committees.
We are particularly happy to include two invited papers by interna-
tionally recognized researchers. Henry Kyburg's paper on "Approximate
Validity" derives from the theme of the first workshop, while the paper
entitled "Curried Least General Generalization: a framework for higher
order concept learning", by Srinivas Padmanabhuni, Randy Goebel and
Koichi Furukawa derives from the theme of the second workshop. We
have also included two tutorial papers which give introductions to se-
lected aspects of the theme of this volume.
This volume would not have been completed without the active
support of many persons. First we thank the authors of the contributions
since they delivered the backbone of this volume. Our thanks go also to
the workshop participants for interesting discussions. The members of
the program committees assisted us greatly in improving the quality of
the volume. We believe that they represent the high standard of AI
researchers in the region, and the diversity of the Pacific Rim.
Finally our thanks go to Joerg Siekmann for his interest in our book
proposal, and to Springer-Verlag for its efficient assistance in publishing
this volume in its Lecture Notes in Artificial Intelligence series.
October 1997
Grigoris Antoniou, Aditya Ghose, and Mirek Truszczynski
Program Committee: Reasoning with Incomplete
and
Changing Information
Grigoris Antoniou, Griffith University
David Billington, Griffith University
Phan Minh Dung, Asian Institute of Technology
Boon Toh Low, Chinese Universoift y Hong Kong
.C Kym MacNish, University of Western Auatralia
Javier Pinto, Pontifical Catholic University of Chile
Mirek Truszczynski, University of Kentucky
Mary-Anne Williams, Universoift y Newcastle
Program Committee: Inducing Complex Repre-
sentations
Koichi Furukawa, Keio University
Aditya Ghose, University of Wollongong
Randy Goebel, University of Alberta
Fumio Mizoguchi, Science University of Tokyo
Srinivas Padmanabhuni, University of Alberta
Table of Contents
Introduction
Inductive constraint logic programming: An overview
Srinivas Padmanabhuni and Aditya K. Ghose
Some approaches to reasoning with incomplete
and changing information
Grigoris Antoniou and Maw-Anne Williams
Invited Papers
Curried least general generalization:
A framework for higher order concept learning 54
Srinivas Padmanabhuni, Randy lebeoG and Koichi Furukawa
Approximate validity 16
Henry E. Kyburg, Jr.
Inducing Complex Representations
Inductive theories from equational systems 87
Michael BuImer
The role of default representations in incremental learning 29
Aditya K. Ghose, Srinivas Padmanabhuni and Randy lebeoG
Learning stable concepts in a changing world 601
Michael Harries and Kim Horn
Inducing complex spatial descriptions in two dimensional scenes 321
Brendan McCane, Terry Caelli and Otivier ed leV
A framework for learning constraints: Preliminary report 331
Srinivas Padmanabhuni, Jia-Huai You and Aditya K. Ghose
Induction of Constraint Logic Programs 841
Mich~le Sebag, Rouveirol Cgline and Jean-Francois Puget
Reasoning with Changing and Incomplete Information
Belief network algorithms:
A study of performance based on domain characterization 861
N. Jitnah and A. E. Nicholson
A group decision and negotiation support system
for argumentation based reasoning 881
Nikos Karacapilidis and Dimitris PapaYas
From belief revision to design revision:
Applying theory change to changing requirements 602
.C K. MacNish and M.-A. Williams
Using histories to model observations in theories of action 122
Javier A. Pinto
Modelling inertia in action languages (Extended Report) 432
Mikhail Prokopenko and Pavlos Peppas
Combinatorial interpretation of uncertainty and conditioning 842
Arthur Ramer
Probabilistic diagnosis as an update problem 652
Angelo .C Restificar
Cooperative combination of default logic and autoepistemic logic 762
Choh Man Teng
Inductive Constraint Logic Programming: An
Overview
Srinivas Padmanabhuni
Department of Computing Science
University of Alberta
Edmonton, Canada, T6G 2H1
[email protected]
Aditya K. Ghose
Decision Systems Lab
Dept. of Business Systems
University of Wollongong
Wollongong, NSW 2522 Australia
[email protected]
Abstract. This paper a provides brief introductainodn overview of the
emerging area of Inductive Constraint Logic Programming (ICLP). It
discusses some of the existing work in the area and presents some of the
research issues and open questions that need to be addressed.
1 Introduction
Inductive Logic Programming (ILP) refers to a class of machine learning algo-
rithms where the agent learns a first-order theory from examples and background
knowledge. The ILP framework in machinlee arning is perhaps the most general
of all because of the complexity of the concepts learned. The use of first-order
logic programs as the underlying representation makes ILP systems more pow-
erful and useful than the conventional empirical machine learning systems. ILP
systems have been successfully used in a variety of real life domains including
mesh design, protein synthesis, games and fault diagnosis. Most of the first-order
representations used in ILP systems are variants of horn-clause based clausal
logic of Prolog.
ILP systems have been weak in handling numerical knowledge. Although
some existing ILP systems are capable of handling numerical knowledge, the
approach in most cases is ad-hoc. Given the obvious utility of extending the
power of the logic programming framework to computational domains other
than than Herbrand terms (such as sets, strings, integers, reals etc.) and the
well-known success of various constraint logic programming languages such as
languages like CLP(R), CHIP etc. in various real life applications [1], there is
a clear need for developing an inductive framework similar to that of ILP but
based on constraint logic programming schemes.
2
2 Existing work in ICLP
Given that this is a relatively new area, there only a small number of frameworks
that attempt a solution to the ICLP problem.
2.1 Kawamura and Furukawa
Furukawa and Kawamura ]2[ adopted the dominant paradigm in ILP, namely the
paradigm of inverse resolution for generalizing constraints. As si well known in
ILP literature, inverse resolution methods are essentially based on the inversion
of the process involved in deduction using resolution. In resolution, given a pair
of clauses, called resolvents, a third clause is deduced. In contrast, the process of
inverse resolution seeks to invoennet of the resolvents, given one tohfe resolvents
and the solved clause. The process basically revolves around three operations,
truncation, absorption and intra-construction of two logic program rules.
Truncation Let P1 and P2 be two clauses whose bodies are empty. Then the
truncation of P1 and P2 yields a clause which si more general than both P1
and P2.
Absorption Suppose two clauses R1 and R2 on resolution yield the clause R3.
Absorption refers to the operation of guessing R2 given R3 and R1.
Intra-Construction Given two clauses 1C and ,2C intra-construction refers to
the process of generating three clauses R1, R2, and R3 such that R1 and R2
on resolution yield 1C and ,2R R3 yield 2C respectively.
In this sense the process of inverse resolution inverts the deduction involved in
resolution process.
The ILP frameworks are based on algorithms which are variants of the above-
mentioned basic inverse resolution algorithm. The model of 0-subsumption used
in ILP cannot be extended to ICLP because of the universalg eneralization rule
adopted in ILP systems, namely, of replacing constants by variables, to yield a
generalized formula. But this method si bound to fail with constraint logic pro-
grams because of the constraints involved. For instance, the generalization of two
equations {x = ,7 x = }2 si x = y by 0-subsumption. This type of generalization
si meaningless, and necessitates the development of domain specific mechanisms
to generalize constraint logic programs based on the domain of the constraint s
involved.
Kawamura and Furukawa generalize the concepts of least general general-
ization for general logic programs to constraint logic program generalization,
by considering the logic program components and generalization of constraints
components separately and merging them. This raises the question of whether
there always exists a least general generalization for a given set of constraints.
Even oinf exists there should be a method to compute the greatest lower bound
of all possible constraint generalizations.
In their framework, Kawamura and Furukawa devise methods to compute
the inverse resolution based generalizations for linear algebra based constraints.