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NL-KR Digest Volume 05 No. 20
NL-KR Digest (11/02/88 23:59:38) Volume 5 Number 20
Today's Topics: SEMINARS
Machiavelli : A Polymorphic Lang. for oo db (Unisys Seminar)
The Computational Linguistics of DNA (UPenn Seminar)
Philosophy Colloquium: Pollock
Seminar - To Think or Not to Think - McAllester
SUNY Buffalo Logic Colloq: Nelson
From CSLI Calendar, October 20, 4:5
Eric Saund--AI Revolving Seminar FRIDAY 10/28
Submissions: NL-KR@CS.ROCHESTER.EDU
Requests, policy: NL-KR-REQUEST@CS.ROCHESTER.EDU
----------------------------------------------------------------------
Date: Sun, 16 Oct 88 14:46 EDT
From: finin@PRC.Unisys.COM
Subject: Machiavelli : A Polymorphic Lang. for oo db (Unisys Seminar)
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 08:41 EDT
From: finin@PRC.Unisys.COM
Subject: The Computational Linguistics of DNA (UPenn Seminar)
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: Tue, 18 Oct 88 10:50 EDT
From: William J. Rapaport <rapaport@cs.Buffalo.EDU>
Subject: Philosophy Colloquium: Pollock
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
[Background material for this colloquium, and an introduction ot Oscar,
may be found in Prof. Pollock's article, ``My Brother, The Machine,''
_Nous_ 22 (1988) 173-212.]
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.
Call Bill Rapaport, Dept. of Computer Science, 636-3193, or Jim Lawler,
Dept. of Philosophy, 636-2444, for further information.
------------------------------
Date: Wed, 19 Oct 88 16:27 EDT
From: Barbara K. Moore <BARB@reagan.ai.mit.edu>
Subject: Seminar - To Think or Not to Think - McAllester
============================================================================
AI REVOLVING SEMINAR
============================================================================
FRIDAY, OCTOBER 21, 1988
4:00 p.m.
8TH FLOOR PLAYROOM, MIT AI LAB
David McAllester
"To Think or not to Think"
Automated inference is a central problem in type checking, program
verification, optimizing compilers, automatic programming, and AI
applications such as common sense knowledge representation, natural
language understanding and planning. This talk discusses ongoing research
in knowledge representation and automated reasoning. This research is
based on new inference techniques such as focused forward chaining,
monotone closure for taxanomic syntax, semantic modulation, and forward
chaining mathematical induction. Each particular inference mechanism can
be evaluated from both an engineering and a cognitive science perspective.
>From an engineering perspective the significance of an inference mechanism
is determined by its usefulness in solving engineering problems such as
program verification or automated programming. From a cognitive science
perspective the significance of an inference mechanism is determed by its
match with human cognitive power. Data will be presented showing how
certain inference mechanisms succeed or fail as cognitive models.
------------------------------
Date: Wed, 19 Oct 88 16:53 EDT
From: William J. Rapaport <rapaport@cs.Buffalo.EDU>
Subject: SUNY Buffalo Logic Colloq: 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.
------------------------------
Date: Wed, 19 Oct 88 20:31 EDT
From: Emma Pease <emma@csli.Stanford.EDU>
Subject: From CSLI Calendar, October 20, 4:5
The Resolution Problem for Natural-Language Processing
Week 4: Psychological Processes
Herb Clark
(herb@psych.stanford.edu)
20 October
I will review part of what is known about the process of resolving
ambiguities and indeterminacies from work in psychology. Last week I
took up, among other things, the issues of automaticity and modularity
in resolving structural ambiguities--that is, ambiguous words,
attachment ambiguities, and other local parsing ambiguities. The
question is, how are these ambiguities resolved so quickly and
apparently automatically on the basis of lexical, syntactic, semantic,
and pragmatic information, and what does this say about the process of
understanding in general? This week I will take up the more pragmatic
issues in resolution, such as how people resolve references,
illocutionary force, and implicatures, and how speakers and listeners
manage to do this collectively.
____________
NEXT WEEK'S TINLECTURE
Chaos
Bernardo Huberman
Xerox PARC
and
Applied Physics Department, Stanford University
(huberman.pa@xerox.com)
October 27
Recent developments in dynamical systems theory have led to a
reappraisal of our understanding of determinism and the origin of
noise in many physical systems. In particular, it has been established
that certain deterministic systems with few degrees of freedom can
exhibit random behavior that is analogous to that produced by the
tossing of a coin.
This talk will provide an introduction to the field of
deterministic chaos. It will also elucidate the notion of
universality, and its implications for the application of chaos theory
to many fields of science.
____________
NEXT WEEK'S CSLI SEMINAR
The Resolution Problem for Natural-Language Processing
Week 5: Early AI Research on Local Pragmatics
Jerry Hobbs
(hobbs@warbucks.ai.sri.com)
October 27
AI researchers have been grappling with problems in local pragmatics,
or the resolution problem, for at least the last fifteen years. We
will discuss Rieger's work on several of these problems, work on the
interpretation of nominal compounds, including that of Finin, and
early and more recent work on pronoun resolution, syntactic ambiguity,
metonymy, and quantifier scope ambiguity that has been in the same
spirit. All of this work has been characterized by attempts to aim
toward efficient and effective heuristics that use world knowledge in
a limited enough way to make the approach feasible. The shortcomings
of this family of approaches will also be discussed.
____________
SYMBOLIC SYSTEMS FORUM
Formalizing Commonsense Knowledge and Reasoning
in Mathematical Logic
John McCarthy
Friday, 21 October, 3:15
Bldg. 60
This Friday John McCarthy will be speaking on formalizing commonsense
knowledge and reasoning in mathematical logic. He is one of the
cofounders of artificial intelligence. He has worked on problems
associated with the logic approach to AI for thirty years and will
discuss what has been accomplished and what seem to be the next
problems. This involves representing by mathematical logical
sentences what a computer program should know about the commonsense
world in general and about specific situations. What it can infer
about what actions will achieve its goals is determined by logical
inference including both logical deduction and formalized nonmonotonic
reasoning.
As always, the Forum will be held at 3:15 in building 60. However,
because we are expecting a large crowd, it will meet in the lecture
hall right next to the entrances to the building instead of room 62N.
------------------------------
Date: Sat, 22 Oct 88 21:20 EDT
From: barb@reagan.ai.mit.edu
Subject: Eric Saund--AI Revolving Seminar FRIDAY 10/28
============================================================================
AI REVOLVING SEMINAR
============================================================================
FRIDAY, OCTOBER 28, 1988
4:00 p.m.
8TH FLOOR PLAYROOM, MIT AI LAB
Eric Saund
"The Role of Knowledge in Visual Shape Representation"
or
"What Should a Visual System Know Next?"
or
"To Swim or Not to Swim?"
This talk shows how knowledge about the visual world can be built
into a shape representation in the form of a descriptive vocabulary making
explicit the important spatial events and geometrical relationships
comprising an object's shape. We offer two specific computational tools
establishing a framework by which a shape representation may support a
variety of Later visual processing tasks: (1) By maintaining shape tokens
on a Scale-Space Blackboard, information about configurations of shape
events such as contours and regions can be manipulated symbolically, while
the pictorial organization inherent to a shape's spatial geometry is
preserved. (2) Through the device of dimensionality-reduction,
configurations of shape tokens can be interpreted in terms of their
membership within deformation classes; this provides leverage in
distinguishing shapes on the basis of subtle variations reflecting
deformations in their forms. The power in these tools derives from their
contributions to capturing knowledge about the visual world. In contrast
to ``building block'' approaches to shape representation (eg. generalized
cylinders), we employ a large and extensible vocabulary of shape
descriptors tailored to the constraints and regularities of particular
shape worlds. The approach is illustrated through a computer
implementation of a hierarchical shape vocabulary designed to offer
flexibility in supporting important aspects of shape recognition and shape
comparison in the two-dimensional shape domain of the dorsal fins of
fishes.
------------------------------
End of NL-KR Digest
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