7.0477 Knowledge Representation 1994 (1/233)

Sun, 13 Feb 1994 19:53:42 EST

Humanist Discussion Group, Vol. 7, No. 0477. Sunday, 13 Feb 1994.

Date: 10 Feb 1994 10:27:40 -0500 (EST)
From: ide@cs.vassar.edu (Nancy M. Ide)
Subject: KR'94

From: KR94 Conference Service <kr94@mail2.ai.univie.ac.at>

KR'94 - Program and Registration Information

Fourth International Conference on
Principles of Knowledge Representation and Reasoning

Gustav Stresemann Institut
Bonn, Germany

May 24-27, 1994

With support from the Gesellschaft fuer Informatik, the Austrian Society for
Artificial Intelligence, the Canadian Society for Computational Studies of In-
telligence, and the European Coordinating Committee on Artificial Intelli-
gence; in cooperation with the American Association for Artificial Intelli-
gence and the International Joint Conferences on Artificial Intelligence, Inc.


KR'94, the first in its series to be held in Europe, provides a more intimate
setting than that of general AI conferences for researchers studying explicit
representations of knowledge manipulated by inference algorithms, which pro-
vide an important foundation for much work in Artificial Intelligence from na-
tural language to expert systems.

The conference emphasizes both the theoretical principles of knowledge
representation and reasoning and the relationships between these principles
and their embodiments in working systems. Presented papers, invited talks,
panels, and audience discussion will address the following important ques-

(1) What issues arise in representing and using knowledge about real problems,
and how can they be addressed?
(2) What are the theoretical principles in knowledge representation and rea-
(3) How can these principles be embodied in implemented knowledge representa-
tion systems, and what practical tradeoffs arise?
(4) How do these approaches to problems relate to corresponding approaches in
other parts of AI (natural language, robotics, etc.) or in other fields
(psychology, philosophy, logic, economics, cognitive science, computer
science, management, engineering, etc.)


The KR'94 Conference will be held at the Gustav Stresemann Institut (GSI) in
Bonn, Germany. The GSI is located just south of the downtown area within easy
reach of the main train station. Major airports are Cologne/Bonn (with regular
bus service to downtown Bonn), Duesseldorf (1 hour by train) and Frankfurt (2
hours by train). Registered participants will receive detailed information
about the GSI and how to get there.


KR'94 information:

E-mail: kr94@cs.uni-bonn.de

Regular KR'94
Mail: Institute of Computer Science III
University of Bonn
Roemerstr. 164
D-53117 Bonn

Phone: +49-228-550-281

Fax: +49-228-550-382

Automatic E-mail: If you send a message to kr94-info@cs.uni-bonn.de, a reply
containing a copy of this announcement will be sent to the address in the
sender field (without being read by a person).


Conference Chair:
Erik Sandewall, Department of Computer and Information Science,
Linkoeping University, Sweden

Program Chairs:
Jon Doyle, Laboratory for Computer Science, MIT, USA
Piero Torasso, Dipartimento di Informatica, Universita' di Torino, Italy

Local Arrangements Chair:
Gerhard Lakemeyer, Institute of Computer Science III, University of Bonn,

Publicity Chair:
Werner Horn, Austrian Research Institute for Artificial Intelligence,


Giuseppe Attardi (U. Pisa, Italy),
Franz Baader (DFKI, Germany),
Fahiem Bacchus (U. Waterloo, Canada),
Philippe Besnard (IRISA, France),
Piero Bonissone (GE, USA),
Craig Boutilier (UBC, Canada),
Ron Brachman (AT&T, USA)
Maurice Bruynooghe (KUL, Belgium),
Anthony Cohn (U. Leeds, UK),
Ernest Davis (NYU, USA),
Rina Dechter (UC Irvine, USA),
Johan de Kleer (Xerox, USA),
Oskar Dressler (Siemens, Germany),
Jennifer Elgot-Drapkin (Arizona State U., USA),
Richard Fikes (Stanford U., USA),
Alan Frisch (U. York, UK),
Hector Geffner (Simon Bolivar U., Venezuela),
Georg Gottlob (TU Wien, Austria),
Pat Hayes (U. Illinois, USA),
Hirofumi Katsuno (NTT, Japan),
Henry Kautz (AT&T, USA),
Sarit Kraus (Bar-Ilan U., Israel),
Maurizio Lenzerini (U. Rome, Italy),
Vladimir Lifschitz (U. Texas, USA),
David Makinson (Unesco, France),
Joao Martins (IST, Portugal)
David McAllester (MIT, USA),
John-Jules Meyer (U. Amsterdam, Netherlands),
Katharina Morik (U. Dortmund, Germany),
Johanna Moore (U. Pittsburgh, USA),
Hideyuki Nakashima (ETL, Japan),
Bernhard Nebel (U Ulm, Germany),
Hans Juergen Ohlbach (Max Planck Institut, Germany),
Lin Padgham (Linkoeping U., Sweden),
Peter Patel-Schneider (AT&T, USA),
Ramesh Patil (USC/ISI, USA),
Raymond Perrault (SRI, USA),
David Poole (UBC, Canada),
Henri Prade (IRIT, France),
Anand Rao (AAII, Australia),
Jeff Rosenschein (Hebrew U., Israel),
Stuart Russell (UC Berkeley, USA),
Len Schubert (U. Rochester, USA)
Marek Sergot (Imperial College, UK),
Lokendra Shastri (ICSI, USA),
Yoav Shoham (Stanford U., USA),
Lynn Stein (MIT, USA),
Devika Subramanian (Cornell U., USA),
William Swartout (USC/ISI, USA),
Austin Tate (AIAI, Edinburgh, UK),
Peter van Beek (U. Alberta, Canada),
Michael Wellman (U. Michigan, USA)


Beyond Ignorance-Based Systems,
W. A. Woods --- Sun Microsystems Laboratories, Inc., USA

The field of artificial intelligence has a long tradition of exploiting the
potential of limited domains. While this is beneficial as a way to get start-
ed and has utility for applications of limited scope, these approaches will
not scale to systems with more open-ended domains of knowledge. Many
"knowledge-based" systems actually derive their success as much from ignorance
as from the knowledge that they contain. That is, they succeed because they
don't know any better. Too great a reliance on a closed-world assumption and
default reasoning in a limited domain can result in a system that is fundamen-
tally limited and cannot be extended beyond its initial domain.

If the field of knowledge-based systems is to move beyond this stage, we need
to develop knowledge representation and reasoning technology that is more
robust in the face of domain extensions. Nonmonotonic reasoning becomes a lia-
bility if the fundamental abilities of a system can be destroyed by the addi-
tion of knowledge from a new domain. This talk will discuss some of the chal-
lenges that we must meet to develop systems that can handle diverse ranges of

Non Standard Theories of Uncertainty in Knowledge Representation and Reasoning
Didier Dubois --- IRIT-CNRS Universite' Paul Sabatier, Toulouse, France

The last 15 years have witnessed a noticeable but scattered research effort
towards a rational theory of plausible reasoning. While Bayesian nets have
recently blossomed in this area, the role of logic and symbolic representa-
tions continue to be prominent. Besides, the monopoly of probability theory
as a tool for modelling uncertainty has been challenged by alternative ap-
proaches such as belief functions and possibility theory. Current efforts
search for a knowledge representation framework that combines the merits of
classical logic and Bayesian probability. The aim of this talk is to try and
provide a perspective view of uncertainty theories in plausible reasoning.
The lecture will touch on the following issues:

- The use of ordering relations in uncertainty modelling and its link to
non-monotonic reasoning.
- The problem of compositionality, and the difference between partial truth
(as in fuzzy logic) and uncertainty.
- Why Bayesian probabilities might be questioned in reasoning tasks that
are not decision-driven.
- The importance of representing generic, exception-tolerant, knowledge as
distinct from uncertain evidence in plausible reasoning tasks.
- The analysis of three forms of belief change: updating, revision, and
focusing and their role in defeasible inference systems.

Knowledge Representation Issues in Integrated Planning and Learning Systems
Jaime Carbonell --- Carnegie Mellon University, USA

Advances in Machine Learning and in non-linear planning systems in Artificial
Intelligence have proceeded somewhat independently of Knowledge Representation
issues. In essence, both fields borrow from KR the very essentials (e.g.
typed FOL, or simple inheritance methods), and then proceed to address other
important issues. However, the increasing sophistication of integrated archi-
tectures such as SOAR. PRODIGY and THEO at CMU (that combine problem solving,
planning and learning) place new demands on their KR infrastructures. These
demands include reasoning about strategic knowledge as well as factual
knowledge, supporting representational shifts in domain knowledge, and meta-
reasoning about the system's own reasoning and learning processes. The
presentation will focus on the PRODIGY architecture and its needs and implica-
tions for KR, especially when these may be in divergence with the primary ac-
tive topics in modern KR research.


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