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AIList Digest Volume 8 Issue 107
AIList Digest Thursday, 20 Oct 1988 Volume 8 : Issue 107
Seminars:
The SB-ONE Knowledge Representation Workbench - Alfred Kobsa
Cooperative Problem Solving Systems - Gerhard Fischer
Machiavelli : A Polymorphic Lang. for oo db - Atsushi Ohori
The Computational Linguistics of DNA - David Searls
OSCAR: A General Theory of Rationality - John Pollock
What My Robot Should Do Next - Ian Horswill
Expert Systems in Predictive Toxicology - Arnott and Snow
Church's Thesis, Connectionism, and Cognitive Science - Raymond J. Nelson
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Date: Tue, 27 Sep 88 11:21:08 EDT
From: finin@PRC.Unisys.COM
Subject: The SB-ONE Knowledge Representation Workbench - Alfred Kobsa
AI SEMINAR
UNISYS PAOLI RESEARCH CENTER
The SB-ONE Knowledge Representation Workbench
Alfred Kobsa
International Computer Science Institute, Berkeley
(on leave from the University of Saarbruecken, West Germany)
The SB-ONE system is an integrated knowledge representation workbench for
conceptual knowledge which was specifically designed to meet the requirements
of the field of natural-language processing. The representational formalism
underlying the system is comparable to KL-ONE, altough different in many
respects. A Tarskian semantics is given for the non-default part of it.
The user interface allows for a fully graphical definition of SB-ONE knowledge
bases. A consistency maintenance system checks for the syntactical
well-formedness of knowledge definitions. It rejects inconsistent entries, but
tolerates and records incomplete definitions. A partition mechanism allows for
the parallel processing of several knowledge bases, and for the inheritance of
(incomplete) knowledge structures between parititons.
The SB-ONE system is being employed in XTRA, a natural-language access system
to expert systems. The use of SB-ONE for meaning representation, user
modeling, and access to the expert system's frame knowledge base will be
briefly described.
10:00am Friday, October 14
BIC Conference Room
Unisys Paoli Research Center
Route 252 and Central Ave.
Paoli PA 19311
-- non-Unisys visitors who are interested in attending should --
-- send email to finin@prc.unisys.com or call 215-648-7446 --
* COMING ATTRACTION: On October 19, Marilyn Arnott (PhD from Texas in *
* Chemistry) will speak on the topic of an expert system for predictive *
* toxicology. The seminar will be held at 2:00 PM in the BIC Conference *
* Room. An exact title and an abstract will be distributed when they *
* become available. *
------------------------------
Date: Wed, 12 Oct 88 14:32:30 edt
From: dlm@allegra.att.com
Subject: Cooperative Problem Solving Systems - Gerhard Fischer
Cooperative Problem Solving Systems
Gerhard Fischer
University of Colorado
October 13, 1988
AT&T Bell Labs -- Murray Hill 3D-436 -- 10:00 am
ABSTRACT
Over the last few years we have constructed a number of
intelligent support systems (e.g. documentation systems,
help systems, critics, and a "software oscilloscope") which
support limited cooperative problem solving processes.
These systems and their limitations will be discussed and
future research directions towards the goal of truly
cooperative problem solving systems will be presented.
Sponsor: R.J.Brachman
------------------------------
Date: Sun, 16 Oct 88 14:46:30 EDT
From: finin@PRC.Unisys.COM
Subject: Machiavelli : A Polymorphic Lang. for oo db - Atsushi Ohori
AI SEMINAR
UNISYS PAOLI RESEARCH CENTER
Atsushi Ohori
University of Pennsylvania
Machiavelli : A Polymorphic Language
for Object-oriented Databases
Machiavelli is a programming language for databases and object-oriented
programming with a strong, statically checked type system. It is an
extension of the programming language ML with generalized relational
algebra, type inheritance and general recursive types. In Machiavelli,
various database operations including join and projection are available
as polymorphic operations, ML's abstract data types are extended with
inheritance declarations, and the type system includes general recursive
types.
In this talk, I will first introduce Machiavelli and show examples
demonstrating its expressive power in the context of both database
programming and object-oriented programming. I will then describe the
theoretical aspects of the language.
For the theoretical aspects of the language, I will show that, by defining
syntactic orderings on subsets of terms and types that correspond to
database objects, a generalized relational algebra can be introduced in a
strongly typed functional programming language. By allowing conditions on
substitutions for type variables, Milner's type inference algorithm can be
also extended to those new constructs. I will then show that by using the
type inference mechanism, ML's abstract data types can be extended to
support inheritance. Finally I will describe how the above mechanisms can
be extended to recursive types.
Joint work with Peter Buneman.
10:30 am - November 2, 1988
BIC Conference Room
Unisys Paoli Research Center
Route 252 and Central Ave.
Paoli PA 19311
-- non-Unisys visitors who are interested in attending should --
-- send email to finin@prc.unisys.com or call 215-648-7446 --
------------------------------
Date: Tue, 18 Oct 88 08:40:47 EDT
From: finin@PRC.Unisys.COM
Subject: The Computational Linguistics of DNA - David Searls
UNIVERSITY OF PENNSYLVANIA
DEPARTMENT OF COMPUTER
AND INFORMATION SCIENCE
The Computational Linguistics of DNA
David Searls
Unisys Paoli Research Center
Genetic information, as expressed in the four-letter alphabet of the
DNA of living organisms, represents a complex and richly-expressive
linguistic system that encodes procedural instructions on how to
create and maintain life. There is a wealth of understanding of the
semantics of this language from the field of molecular biology, but
its syntax has been elaborated primarily at the lowest lexical levels,
without benefit of formal computational approaches that might help to
organize its description and analysis. In this talk, I will examine
some linguistic properties of DNA, and propose that generative
grammars can and should be used to describe genetic information in a
declarative, hierarchical manner. Furthermore, I show how a Definite
Clause Grammar implementation can be used to perform various kinds of
analyses of sequence information by parsing DNA. This approach
promises to be useful in recombinant DNA experiment planning systems,
in simulation of genetic systems, in the interactive investigation of
complex control sequences, and in large-scale search over huge DNA
sequence databases.
THURSDAY, OCTOBER 20, 1988
REFRESHMENTS
2:30 - 3:00
129 Pender
COLLOQUIUM
3:00 - 4:30
216 MOORE
------------------------------
Date: 18 Oct 88 15:18:18 GMT
From: sunybcs!rapaport@rutgers.edu (William J. Rapaport)
Subject: OSCAR: A General Theory of Rationality - John Pollock
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UPDATE UPDATE UPDATE UPDATE UPDATE UPDATE UPDATE UPDATE UPDATE UPDATE UPDATE
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UNIVERSITY AT BUFFALO
STATE UNIVERSITY OF NEW YORK
DEPARTMENT OF PHILOSOPHY
GRADUATE GROUP IN COGNITIVE SCIENCE
and
GRADUATE RESEARCH INITIATIVE IN COGNITIVE AND LINGUISTIC SCIENCES
PRESENT
JOHN POLLOCK
Department of Philosophy
University of Arizona
OSCAR: A General Theory of Rationality
The enterprise is the construction of a general theory of rationality
and its implementation in an automated reasoning system named OSCAR.
The paper describes a general architecture for rational thought. This
includes both theoretical reasoning and practical reasoning, and builds
in important interconnections between them. It is urged that a sophis-
ticated reasoner must be an _introspective reasoner_, capable of moni-
toring its own reasoning and reasoning about it. An introspective rea-
soner is built on top of a non-introspective reasoner that represents
the system's default reasoning strategies. The introspective reasoner
engages in practical reasoning about reasoning in order to overide these
default strategies. The paper concludes with a discussion of some
aspects of the default reasoner, including the manner in which reasoning
is interest-driven and the structure of defeasible reasoning.
Wednesday, October 26, 1988
4:00 P.M.
684 Baldy Hall, Amherst Campus
There will be an evening discussion at 8:00 P.M.,
at Mary Galbraith's, 130 Jewett Parkway, Buffalo.
Copies of the paper are available from Bill Rapaport, Dept. of Computer
Science, 636-3193. Contact Rapaport or Jim Lawler, Dept. of Philosophy,
636-2444, for further information.
------------------------------
Date: Tue 18 Oct 88 12:24:18-EDT
From: Marc Vilain <MVILAIN@G.BBN.COM>
Subject: What My Robot Should Do Next - Ian Horswill
BBN Science Development Program
AI Seminar Series Lecture
WHAT MY ROBOT SHOULD DO NEXT: NAVIGATION WITHOUT PLANNING;
VISION WITHOUT INVERSE-OPTICS.
Ian D. Horswill
MIT Artificial Intelligence Lab
(IDH@WHEATIES.AI.MIT.EDU)
BBN Labs
10 Moulton Street
2nd floor large conference room
10:30 am, Tuesday October 25
In this talk I will discuss a system which performs a variety of
low-level navigation activities without many of the traditional
trappings of robot navigation such as mapping, planning, callibrated
cameras, surface reconstruction, or dead reconning. In particular, the
system chases moving objects, investigates static ones, and follows
along corridors using a camera for visual feedback.
Rather than committing to a pre-planned path and attempting to follow
it accurately, the system constantly re-answers the question "what
should I do next?". By continuously reasessing the situation, the
system is able to operate in dynamic and even unpredictable
environments where mapping and planning are unfeasible. By breaking
the problem up into managable routine tasks such as corridor
following, the system is able to perform the tasks using dramatically
simpler machinery than conventional systems while guaranteeing bounded
response time (0.2 seconds in our present implementation).
------------------------------
Date: Tue, 18 Oct 88 14:32:32 EDT
From: finin@PRC.Unisys.COM
Subject: Expert Systems in Predictive Toxicology - Arnott and Snow
AI SEMINAR
UNISYS PAOLI RESEARCH CENTER
EXPERT SYSTEMS IN PREDICTIVE TOXICOLOGY
Marilyn S. Arnott and Ina B. Snow
LogiChem Inc.
Boyertown, PA 19512
A prototype system focusing on the possible teratogenicity of members
of one class of chemicals, aliphatic acids, has been developed and
validated. The system evaluates any chemical which can be metabolized
to an aliphatic acid, then performs structure-activity relationship
(SAR) analysis on the resulting acid to determine its potential
teratogenicity. The prototype was validated by comparing results from
the system to laboratory results from three types of teratogenesis
bioassays on 36 aliphatic acids. The outcome cast doubt on the
usefulness of one of the bioassays, and, additionally, detected an
error in the published structure of one of the compounds tested.
We are presently in the early design phase of an expert system to
predict carcinogenic potential of chemicals. The system is being
developed in cooperation with senior scientists at the EPA, who use
SAR analysis to evaluate the potential health hazards of new chemicals
under review by the agency.
Wednesday, October 19, 2:00
BIC Conference Room
Unisys Paoli Research Center
Paoli Pa
-- non-Unisys visitors who are interested in attending should --
-- send email to finin@prc.unisys.com or call 215-648-7446 --
------------------------------
Date: Wed, 19 Oct 88 16:53:17 EDT
From: rapaport@cs.Buffalo.EDU (William J. Rapaport)
Subject: Church's Thesis, Connectionism, and Cognitive Science -
Raymond J. Nelson
UNIVERSITY AT BUFFALO
STATE UNIVERSITY OF NEW YORK
BUFFALO LOGIC COLLOQUIUM
GRADUATE GROUP IN COGNITIVE SCIENCE
and
GRADUATE RESEARCH INITIATIVE IN COGNITIVE AND LINGUISTIC SCIENCES
PRESENT
RAYMOND J. NELSON
Truman Handy Professor of Philosophy
Case Western Reserve University
CHURCH'S THESIS, CONNECTIONISM, AND COGNITIVE SCIENCE
Wednesday, November 16, 1988
4:00 P.M.
684 Baldy Hall, Amherst Campus
The Church-Turing Thesis (CT) is a central principle of contemporary
logic and computability theory as well as of cognitive science (which
includes philosophy of mind). As a mathematical principle, CT states
that any effectively computable function of non-negative integers is
general recursive; in computer and cognitive-science terms, it states
that any effectively algorithmic symbolic processing is Turing comput-
able, i.e., can be carried out by an idealized stored-program digital
computer (one with infinite memory that never fails or makes mistakes).
In this form, CT is essentially an empirical principle.
Many cognitive scientists have adopted the working hypothesis that the
mind/brain (as a cognitive organ) is some sort of algorithmic symbol-
processor. By CT, it follows that the mind/brain is (or realizes) a
system of recursive rules. This may be interpreted in two ways, depend-
ing on two types of algorithm, free or embodied. A free algorithm is
represented by any program; an embodied algorithm is one built into a
network (such as an ALU unit or a neuronal group).
CT is being challenged by connectionism, which asserts that many cogni-
tive processes, including perception in particular, are not symbol
processes, but rather subsymbol processes of entities that have no
literal semantic interpretation. These are parallel, distributed, asso-
ciative memory processes totally unlike serial, executive-driven, von
Neumann computers. CT is also being challenged by evolutionism, which
is a form of connectionism that denies that phylogenesis produces a
mind/brain adapted to fixed categories or distal stimuli (even fuzzy
ones). Computers deal only with fixed categories (either in machine
language, codes such as ASCII, or declarations in higher-level
languages). So, if connectionists are right, CT is false: there are
processes that are provably (I will suggest a proof) effective and algo-
rithmic but are not Turing-computable.
However, if CT in empirical form is true, and if the processes involved
are effective, then connectionism or, in general, anti-computationalism
is false.
A direct argument that does not appeal to CT but that tends to confirm
it is that embodied algorithm networks as a matter of fact are parallel,
distributed, associative, and subsymbolic even in von Neumann computers,
not to say super-multiprocessors. Finally, I claim that the embodied
algorithm network models are not only _not_ antithetical to evolutionism
but dovetail nicely with the theory that the mind/brain evolves through
the life of the individual.
REFERENCES
Edelman, G. (1987), _Neural Darwinism_ (Basic Books).
Nelson R. J. (1988), ``Connections among Connections,'' _Behavioral &
Brain Sci._ 11.
Smolensky, P. (1988), ``On the Proper Treatment of Connectionism,''
_Behavioral & Brain Sci._ 11.
There will be an evening discussion at a time and place to be announced.
Contact John Corcoran, Department of Philosophy, 636-2444 for further
information.
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End of AIList Digest
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