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NL-KR Digest Volume 06 No. 10
NL-KR Digest (Tue Mar 21 14:59:45 1989) Volume 6 No. 10
Today's Topics:
Abstracts from Journal of Exp. and Theor. AI
BBN AI Seminar: Karen Srachik
visiting appt. at SUNY Buffalo
MT Proceedings
talks at UT Austin Center for CogSci
Bar-Ilan Symposium on Foundations of AI
Submissions: nl-kr@cs.rpi.edu
Requests, policy: nl-kr-request@cs.rpi.edu
Back issues are available from host archive.cs.rpi.edu [128.213.1.10] in
the files nl-kr/Vxx/Nyy (ie nl-kr/V01/N01 for V1#1), mail requests will
not be promptly satisfied. If you can't reach `cs.rpi.edu' you may want
to use `turing.cs.rpi.edu' instead.
---------------------------------------------------------
To: nl-kr@cs.rpi.edu
From: cfields@NMSU.Edu
Date: Fri, 3 Mar 89 17:17:17 MST
Subject: Abstracts from Journal of Exp. and Theor. AI
_________________________________________________________________________
The following are abstracts of papers appearing in the inaugural issue
of the Journal of Experimental and Theoretical Artificial
Intelligence. JETAI 1, 1 was published 1 January, 1989.
For submission information, please contact either of the editors:
Eric Dietrich Chris Fields
PACSS - Department of Philosophy Box 30001/3CRL
SUNY Binghamton New Mexico State University
Binghamton, NY 13901 Las Cruces, NM 88003-0001
dietrich@bingvaxu.cc.binghamton.edu cfields@nmsu.edu
JETAI is published by Taylor & Francis, Ltd., London, New York, Philadelphia
_________________________________________________________________________
Minds, machines and Searle
Stevan Harnad
Behavioral & Brain Sciences, 20 Nassau Street, Princeton NJ 08542, USA
Searle's celebrated Chinese Room Argument has shaken the foundations
of Artificial Intelligence. Many refutations have been attempted, but
none seem convincing. This paper is an attempt to sort out explicitly
the assumptions and the logical, methodological and empirical points
of disagreement. Searle is shown to have underestimated some features
of computer modeling, but the heart of the issue turns out to be an
empirical question about the scope and limits of the purely symbolic
(computational) model of the mind. Nonsymbolic modeling turns out to
be immune to the Chinese Room Argument. The issues discussed include
the Total Turing Test, modularity, neural modeling, robotics,
causality and the symbol-grounding problem.
_________________________________________________________________________
Explanation-based learning: its role in problem solving
Brent J. Krawchuck and Ian H. Witten
Knowledge Sciences Laboratory, Department of Computer Science,
University of Calgary, 2500 University Drive, NW, Calgary, Alta,
Canada, T2N 1N4.
`Explanation-based' learning is a semantically-driven,
knowledge-intensive paradigm for machine learning which contrasts
sharply with syntactic or `similarity-based' approaches. This paper
redevelops the foundations of EBL from the perspective of
problem-solving. Viewed in this light, the technique is revealed as a
simple modification to an inference engine which gives it the ability
to generalize the conditions under which the solution to a particular
problem holds. We show how to embed generalization invisibly within
the problem solver, so that it is accomplished as inference proceeds
rather than as a separate step. The approach is also extended to the
more complex domain of planning to illustrate that it is applicable to
a variety of logic-based problem-solvers and is by no means restricted
to only simple ones. We argue against the current trend to isolate
learning from other activity and study it separately, preferred
instead to integrate it into the very heart of problem solving.
- ---------------------------------------------------------------------------
The recognition and classification of concepts in understanding
scientific texts
Fernando Gomez and Carlos Segami
Department of Computer Science, University of Central Florida,
Orlando, FL 32816, USA.
In understanding a novel scientific text, we may distinguish the
following processes. First, concepts are built from the logical form
of the sentence into the final knowledge structures. This is called
concept formation. While these concepts are being formed, they are
also being recognized by checking whether they are already in
long-term memory (LTM). Then, those concepts which are unrecognized
are integrated in LTM. In this paper, algorithms for the recognition
and integration of concepts in understanding scientific texts are
presented. It is shown that the integration of concepts in scientific
texts is essentially a classification task, which determines how and
where to integrate them in LTM. In some cases, the integration of
concepts results in a reclassification of some of the concepts already
stored in LTM. All the algorithms described here have been
implemented and are part of SNOWY, a program which reads short
scientific paragraphs and answer questions.
- --------------------------------------------------------------------------
Exploring the No-Function-In-Structure principle
Anne Keuneke and Dean Allemang
Laboratory for Artificial Intelligence Research, Department of
Computer and Information Science, The Ohio State University, 2036 Neil
Avenue Mall, Columbus, OH 43210-1277, USA.
Although much of past work in AI has focused on compiled knowledge
systems, recent research shows renewed interest and advanced efforts
both in model-based reasoning and in the integration of this deep
knowledge with compiled problem solving structures. Device-based
reasoning can only be as good as the model used; if the needed
knowledge, correct detail, or proper theoretical background is not
accessible, performance deteriorates. Much of the work on model-based
reasoning references the `no-function-in-structure' principle, which
was introduced be de Kleer and Brown. Although they were all well
motivated in establishing the guideline, this paper explores the
applicability and workability of the concept as a universal principle
for model representation. This paper first describes the principle,
its intent and the concerns it addresses. It then questions the
feasibility and the practicality of the principle as a universal
guideline for model representation.
___________________________________________________________________________
------------------------------
To: nl-kr@cs.rpi.edu
Date: Wed 15 Mar 89 23:11:07-EST
From: Marc Vilain <MVILAIN@G.BBN.COM>
Subject: BBN AI Seminar: Karen Srachik
BBN Science Development Program
AI Seminar Series Lecture
VISUAL NAVIGATION:
CONSTRUCTING AND UTILIZING SIMPLE MAPS OF AN INDOOR ENVIRONMENT
Karen Sarachik
MIT Artificial Intelligence Laboratory
(kbs@wheaties.ai.mit.edu)
BBN Labs
10 Moulton Street
2nd floor large conference room
10:30 am, Tuesday March 21
Much work with mobile robots has been done in the past using both vision and
sonar to build maps, or, given a map, to successfully plan and execute
trajectories to a goal. The most successful examples of robot navigation
occurred in carefully engineered environments where the robot was able to
accurately predict what its sensory input should be at any point, and correct
for drift by comparing actual input to the projected input. In unstructured
environments, however, the problem became much harder, and the obvious
approaches failed to produce good results. The problem is further complicated
by the fact that most interesting environments are not static, but rather are
changing continually.
In this talk I will discuss the problem from a different angle altogether,
using the way people navigate through buildings as insight and inspiration.
The goal is to navigate through an office environment using only visual
information gathered from four cameras, whose initial detailed configuration
is not known, placed onboard a mobile robot. The method is insensitive to
physical changes within the room it is inspecting, such as moving objects.
The map is built without the use of odometry or trajectory integration, which
are often unreliable. At the heart of this technique is the development of a
``room recognizer'' which is able to deduce the size and shape of a room in
conjunction with a ``door recognizer'' which recognizes a potential door by
finding two vertical edges close enough together. The long term goal of the
project described here is for the robot to build simple maps of its
environment, presumed to be a single floor of an office building, and to
localize itself within this framework.
- ------
------------------------------
To: nl-kr@cs.rpi.edu
Date: Wed, 8 Mar 89 12:29:49 EST
From: rapaport@cs.Buffalo.EDU (William J. Rapaport)
Subject: visiting appt. at SUNY Buffalo
=========================================================================
VISITING INSTRUCTIONAL POSITION
DEPARTMENT OF COMPUTER SCIENCE
SUNY AT BUFFALO
We have an opening for a Visiting Lecturer or Visiting Assistant Professor for
the 1989/90 Academic Year, the title depending on credentials. The teaching
load would be two courses per semester, and the salary would be $28,000 plus
benefits. Send a c.v. and names of four references to: Ms. Helene Kershner,
Assistant Chairman, Department of Computer Science, SUNY at Buffalo, 226 Bell
Hall, Buffalo, NY 14260-7022; or to kershner@cs.buffalo.edu. For full
consideration, applications should be received by April 1, 1989. SUNY is an
affirmative action/equal opportunity employer.
------------------------------
To: nl-kr@cs.rpi.edu
Date: Thu, 9 Mar 89 14:36:15 +0100
From: Klaus Schubert <dlt1!schubert@nluug.nl>
Phone: +31 30 911911
Telex: 40342 bso nl
Subject: MT Proceedings
************* BOOK ANNOUNCEMENT **** BOOK ANNOUNCEMENT ************************
Recently published:
NEW DIRECTIONS IN MACHINE TRANSLATION
Conference proceedings, Budapest 18-19 August 1988.
Edited by Dan Maxwell, Klaus Schubert and Toon Witkam.
[= Distributed Language Translation 4]
Dordrecht / Providence: Foris Publications, 1988, 259 pp.
Available from
Foris Publications
Postbus 509
NL-3300 AM Dordrecht
Netherlands
Distributor for the USA and Canada:
Foris Publications USA
P. O. Box 5904
Providence RI 02903
USA
Distributor for Japan:
Toppan Company
Sufunomoto Bldg.
1-6, Kanda Surugadai
Chiyoda-ku
Tokyo 101
Japan
*******************************************************************************
C O N T E N T S
W. John HUTCHINS (Norwich, Great Britain):
Recent developments in machine translation
Tibor V'AMOS (Budapest, Hungary):
Language and the computer society
Ivan I. OUBINE / Boris D. TIKHOMIROV (Moscow, Soviet Union):
The state of the art in machine translation in the U.S.S.R.
DONG Zhen Dong (Peking, China):
MT research in China
Christian BOITET (Grenoble, France):
Pros and cons of the pivot and transfer approaches in multilingual
machine translation
Michiko KOSAKA / Virginia TELLER / Ralph GRISHMAN (New York, USA):
A sublanguage approach to Japanese-English machine translation
Iv'an GUZM'AN DE ROJAS (La Paz, Bolivia):
ATAMIRI - interlingual MT using the Aymara language
Klaus SCHUBERT (Utrecht, Netherlands):
The architecture of DLT - interlingual or double direct?
Christa HAUENSCHILD (Berlin, F.R.Germany):
Discourse structure \(mi some implications for machine translation
Jun-ichi TSUJII (Kyoto, Japan [now Manchester, Great Britain]):
What is a cross-linguistically valid interpretation of discourse?
Christian GALINSKI (Vienna, Austria):
Advanced terminology banks supporting knowledge-based MT
Wera BLANKE (Berlin, German D.R.):
Terminologia Esperanto-Centro - efforts for terminological
standardization in the planned language
Dietrich M. WEIDMANN (Schaffhausen, Switzerland):
Universal applicability of dependency grammar
Bengt SIGURD (Lund, Sweden):
Translating to and from Swedish by SWETRA - a multilanguage
translation system
G'abor PR'OSZ'EKY (Budapest, Hungary):
Hungarian - a special challenge to machine translation?
Claude PIRON (Geneva, Switzerland):
Learning from translation mistakes
Petr SGALL (Prague, Czechoslovakia):
On some results of the conference
Index
------------------------------
To: nl-kr@cs.rpi.edu
Date: Fri 17 Mar 89 09:41:41-CST
From: Kent Wittenburg <HI.WITTENBURG@MCC.COM>
Subject: talks at UT Austin Center for CogSci
Colloquium, Center for Cognitive Science, Linguistics Department, University
of Texas at Austin
When: Monday, March 20, 3:30 PM
Where: GRG 220, U.T. campus
PARSING AS A RACE
Graeme Hirst
Department of Computer Science
University of Toronto
Abstract:
We present a processing model that integrates some
important psychological claims about the human sentence
parsing mechanism, namely that processing is influenced by limitations on
working memory and by various syntactic preferences.
The model uses time-constraint information
to resolve conflicting preferences in a psychologically plausible way.
The starting point for this proposal is the Sausage Machine model (Frazier
and Fodor, 1978; Fodor and Frazier, 1980). From
there, we attempt to overcome the original model's dependence on
ad hoc aspects of its grammar, and its omission of verb-frame preferences.
We also add mechanisms for lexical disambiguation and semantic
processing in parallel with syntactic processing.
[This paper is co-authored with Susan McRoy.]
- -------------------------------------------
Colloquium, Human Interface Laboratory, MCC
When: Tuesday, March 21, 10:30 AM
Where: ACA conference room, MCC Balcones Research Center, Austin TX
KNOWLEDGE REPRESENTATION PROBLEMS IN NATURAL LANGUAGE UNDERSTANDING
Graeme Hirst
Department of Computer Science
University of Toronto
Abstract:
In artificial intelligence these days, just about anything that's any
good is `knowledge-based'. Consequently, knowledge representation
formalisms are big business, and are available in a wide range of
styles and colors to suit the various demands of consumers in the
marketplace. I want to argue that consumers in the
natural language understanding research community are not as well
served as they might be, and many of their needs have been overlooked.
I will investigate exactly what is required of a KR in
these roles in NLU, where some of the problems lie, and where we might
look for some of the solutions. I take as a starting point the idea
that we need a structured representation with a denotational
semantics. I assume that, other things being equal, a compositional
representation is to be preferred -- that is, a representation in
which the meaning of the whole is a systematic function of the meaning
of the parts from which it is constructed. For example, if we have
representations of "on Tuesday" and "Ross kissed Nadia" we could
combine them in some fairly obvious way to get the representation of
"Ross kissed Nadia on Tuesday".
I will discuss work in my research group on these issues,
including the semantics of focusing adverbs, the representation of
ambiguities of description, and the representation of ontological
assertions.
I will give little time to issues of tractability (cf. Levesque and
Brachman), but rather emphasize questions that are prior to such
issues. Also, I won't address problems in representations that just
try to describe the world or the laws of physics or commonsense
(Hayes, Lenat, Hobbs, etc.).
- ------
------------------------------
To: nl-kr@cs.rpi.edu
Date: Sun, 12 Mar 89 17:02:59 IDT
From: GOLUMBIC%ISRAEARN.BITNET@CUNYVM.CUNY.EDU
Subject: Bar-Ilan Symposium on Foundations of AI
*** Second Announcement ***
Bar-Ilan Symposium on the
Foundations of Artificial Intelligence
19-21 June 1989
Sponsored by the Research Institute for the Mathematical Sciences
Bar-Ilan University, Ramat Gan, Israel
Invited speakers:
John McCarthy (Stanford University)
"Formalized Common Sense Knowledge and Reasoning"
Ronald Rivest (M.I.T.)
"Recent Developments in Machine Learning Theory"
Joseph Halpern (IBM Research)
"Reasoning about Knowledge and Probability"
................................................................
The Annals of Mathematics and Artificial Intelligence will publish a
special issue, containing selected refereed full length papers, as a
permanent record of the Symposium. These should be submitted shortly
after the conclusion of the Symposium and be at the standard of the
best professional journals.
High quality research papers are solicited for consideration by the
program committee to be presented at the Symposium. Submission of
extended abstracts should be sent by 31 March 1989 in triplicate to:
Prof. Martin Golumbic
BISFAI-89 Program Chair
IBM Israel Scientific Center
Technion City
Haifa, Israel
or by electronic mail to: golumbic@israearn.bitnet
Decisions on talks will be made within one month of receipt.
................................................................
The Bar-Ilan Symposium on the Foundations of Artificial Intelligence is
intended to become a biennial event which will focus on a range of
topics of concern to scholars applying quantitative, combinatorial,
logical, algebraic and algorithmic methods to AI areas as diverse as
decision support, automatic reasoning, knowledge-based systems, machine
learning, computer vision, and robotics. These include applied
logicians, algorithms and complexity researchers, AI theorists, and
applications specialists using mathematical methods. By sponsoring such
symposia, we hope to influence the spawning of new areas of applied
mathematics and the strengthening of the scientific underpinnings of
artificial intelligence.
............ REGISTRATION AND HOTEL ACCOMODATIONS ........
We have reserved a block of hotel accommodations at the
Kfar Hamaccabia Hotel in Ramat Gan, a first-class hotel which also
has sports facilities available gratis for the Symposium participants.
The Symposium will take place at the University, which is a short ride,
or a half-hour walk, from the hotel. The room rate is $44 single or
$54 double (including breakfast). Reservations must be made DIRECTLY
WITH THE AGENT
Sharon Tours, Attn: Ms. Dennis, P.O.Box 2605, Ramat Gan, Israel
Tel: 972-3-738144 FAX: 972-3-724365
mentioning the Bar-Ilan Symposium.
To allow the organizers to reserve sufficient lecture room space,
please fill in and return this portion of the form to
Dr. Ariel Frank, BISFAI-89 Organizing Chair
Department of Mathematics and Computer Science
Bar-Ilan University, Ramat Gan, ISRAEL
(email: ariel@bimacs.bitnet)
________________________________________________________________
****** PLEASE RETURN THIS FORM *********
Name: ________________________________________________________
Affiliation: _________________________________________________
Address: _____________________________________________________
Electronic mail: ____________________________________________
_____ I will attend the Bar-Ilan Symposium June 19-21, 1989
_____ Please send me the third announcement in May 1989.
I do / do not plan to submit a paper.
------------------------------
End of NL-KR Digest
*******************
