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AIList Digest Volume 7 Issue 036
AIList Digest Friday, 17 Jun 1988 Volume 7 : Issue 36
Today's Topics:
Philosophy:
Me and Karl Kluge
The Social Construction of Reality
Cognitive AI vs Expert Systems
Dance notation
definition of information
Announcements:
object-oriented database workshop: oopsla '88
LP'88 Conference Announcement
----------------------------------------------------------------------
Date: 10 Jun 88 14:42:15 GMT
From: mcvax!ukc!strath-cs!glasgow!gilbert@uunet.uu.net (Gilbert
Cockton)
Subject: Re: Me and Karl Kluge (no flames, no insults, no abuse)
In article <43@aipna.ed.ac.uk> Richard Caley writes:
>In <1312@crete.cs.glasgow.ac.uk>, Gilbert Cockton writes
>> work other than SOAR and ACT* where the Task Domain has been formally
>> studied before the computer implementation?
>Natural language processing. Much ( by no means all ) builds on the work
>of some school of linguistics.
and ignores most of the work beyond syntax :-) Stick to the
computable, not the imponderable. Hmm pragmatics. I know there is AI
work on pragmtics, but I don't know if a non-computational linguist
working on semantics and pragmatics would call it advanced research work.
>One stands on the shoulders of giants. Nobody has time to research
>their subject from the ground up.
But what when there is no ground? Then what? Hack first or study?
Take intelligent user interfaces, hacking first well before any study
of what problems real users on real tasks in real applications face
(exception Interllisp-D interface, but this was an end-user project!).
>According to your earlier postings, if ( strong ) AI was successful it
>would tell us that we have no free will, or at least that we can not assume
>we have it. I don't agree with this but it is _your_ argument and something
>which a computer program could tell us.
Agreed. Anything ELSE though that may be useful? (I accept that proof
of our logical (worse than physical) determinism would be a revelation)
>What do the theories of physics tell us that we couldn't find out by
>studying objects.
Nothing, but as these theories are based on the study of objects, we
know that if we were to repeat the study, we would confirm the
theories. Strong AI on the other hand conducts NO study of people, and
thus if we studied people in an area colonised at present by hackers
only, then we have no reason to believe that we would confirm the
model in the hacker's mind. There is no similarity at all between the
theories of physics and computational models of human behaviour, it
just so happens that some (like ACT*) do have an empirical input. The
problem with strong AI is that you don't have to have this input. No one
would dare call something a theory in physics which was based solely on
one individual's introspection constrained only by their progamming
ability. In AI, it seems acceptable (Schank's work for example, can
anyone give me references to the substantive critiques from within AI,
I know of ones by linguists).
>> The proper object of the study of humanity is humans, not machines
>Well, there go all the physical sciences, botany, music, mathematics . . .
And there goes your parser too. "of humanity" attaches to "the
study". Your list is not such a study, it is a study of the natural
world and some human artifacts (music, mathematics). These are not
studies of people, OK, and they thus tell us nothing essential about
ourselves, except that we can make music and maths, and that we can
find structures and properties for these artifacts. A study of
artifacts, cognitive, aesthetic or otherwise, is not necessarily a
study of humanity. The latter will embrace all artifacts, but not as
objects in themselves, but for their possible meanings.
--
Gilbert Cockton, Department of Computing Science, The University, Glasgow
gilbert@uk.ac.glasgow.cs <europe>!ukc!glasgow!gilbert
The proper object of the study of humanity is humans, not machines
------------------------------
Date: 13 Jun 88 15:14:53 GMT
From: mcvax!ukc!its63b!aipna!rjc@uunet.uu.net (Richard Caley)
Subject: Re: Me and Karl Kluge
In <1342@crete.cs.glasgow.ac.uk> Gilbert Cockton writes:
>In article <43@aipna.ed.ac.uk> Richard Caley writes:
>>Natural language processing...builds on the work of linguistics.
>and ignores most of the work beyond syntax :-)
Some does.
>Hmm pragmatics. I know there is AI
>work on pragmtics, but I don't know if a non-computational linguist
>working on semantics and pragmatics would call it advanced research work.
The criterion for it being interesting would not necessarily be explaining
something new, explaining something in a more extensible/elegant/practical/
formal ( choose your own hobby horse ) is just as good.
>But what when there is no ground? Then what? Hack first or study?
Maybe my metaphor was not well chosen. Rather than building up it
might be better to see the computational work building down, trying to
ground its borrowed theories ( of language or whatever ) in something
other than their own symbols and/or set theory.
your question becomes, what when there is nothing to hang your work from?
In that case you should go out and do the groundwork or, better, get
someone trained in the empirical methods of that field to do it.
>(exception Interllisp-D interface, but this was an end-user project!).
ARGH don't even mention it, it just lost my days work for me.
>(I accept that proof of our logical (worse than physical) determinism
>would be a revelation)
Well physical determinism wouldn't be a revelation to many of us
who assume it already. I don't know your definition of logical determinism
so I can't say whether that is worse. If it is meant to apply to all
possible outcomes of strong AI it can't imply lack of free will ( read
as the property of making your own decisions, rather than exclusion from
causality ), what does it imply that is so shocking.
>>What do the theories of physics tell us that we couldn't find out by
>>studying objects.
>Nothing.
But they do. Studying objects just tells you what has happened. A (correct)
theory can be predictive, can be explainatory, can allow one to deduce
properties of the system under study which are not derivable from the
data.
>Strong AI on the other hand conducts NO study of people,
Strong AI does not require the study of people, it is not "computational
psycology". AI workers study people in order to avoid reinventing wheels.
>>> The proper object of the study of humanity is humans, not machines
>And there goes your parser too.
Oops. I'm afraid I read it as parallel to "The proper study of man is man".
It does seem to be something of a hollow statement; I can't think of
many people who study machines as a study of humanity ( except in the
degenerate case, if one believes humans are machines ). Some people
use machines as tools to study human beings, some study and build
machines.
------------------------------
Date: 15 Jun 88 15:39:28 GMT
From: amdahl!pyramid!prls!philabs!gcm!dc@ames.arpa (Dave Caswell)
Subject: Re: The Social Construction of Reality
In article <514@dcl-csvax.comp.lancs.ac.uk> Simon Brooke writes [. . .]:
.Wells, like fanatical adherents of other ideologies before him, first
.hurls abuse at his opponents, and finally, defeated, closes his ears. I
.note that he is in industry and not an academic; nevertheless he is
.posting into the ai news group, and must therefore be considered part of
.the American AI community. I haven't visited the States; I wonder if
.someone could tell me whether this extraordinary combination of ignorance
.and arrogance is frequently encountered in American intellectual life?
Yes it is extremely common, and not just within the AI community.
--
Dave Caswell
Greenwich Capital Markets uunet!philabs!gcm!dc
If it could mean something, I wouldn't have posted about it! -- Brian Case
------------------------------
Date: 17 Jun 88 01:17:03 GMT
From: krulwich-bruce@yale-zoo.arpa (Bruce Krulwich)
Subject: Cognitive AI vs Expert Systems (was Re: Me, Karl, Stephen,
Gilbert)
In article <19880615061536.5.NICK@INTERLAKEN.LCS.MIT.EDU>
dg1v+@ANDREW.CMU.EDU (David Greene) writes:
>I'm not advocating Mr. Cockton's views, but the limited literature breadth in
>many AI papers *is* self-defeating. For example, until very recently, few
>expert system papers acknowledged the results of 20+ years of psychology
>research on Judgement and Decision Making.
This says something about expert systems papers, not about papers discussing
serious attempts at modelling intelligence. It is wrong to assume (as both
you and Mr. Cockton are) that the expert system work typical of the business
world (in other words, applications programs) is at all similar to work done
by researchers investigating serious intelligence. (See work on case based
reasoning, explanation based learning, expectation based processing, plan
transformation, and constraint based reasoning, to name a few areas.)
Bruce Krulwich
Net-mail: krulwich@{yale.arpa, cs.yale.edu, yalecs.bitnet, yale.UUCP}
Goal in life: to sit on a quiet beach solving math problems for a
quarter and soaking in the rays.
------------------------------
Date: 13 Jun 88 08:44:22 GMT
From: mcvax!ukc!strath-cs!glasgow!gilbert@uunet.uu.net (Gilbert
Cockton)
Subject: Re: Human-human communication
In article <905@papaya.bbn.com> Hunter Barr writes:
>But you ignore the existance of useful dance notations. I don't know
>much about dance notation, and I am sure there is much lacking in it--
For an accessible introduction to the problem of dance notations, see
Singh, Beatty, Booth and Ryman in Siggraph'83. You can chase up
references from here. All I can add is that many choreographers (All
I have encountered) do NOT use notations, as none are up to the job.
There's research at New York into using figure animation, computer
graphics and body sensors (Columbia I think).
--
Gilbert Cockton, Department of Computing Science, The University, Glasgow
gilbert@uk.ac.glasgow.cs <europe>!ukc!glasgow!gilbert
The proper object of the study of humanity is humans, not machines
------------------------------
Date: Wed, 15 Jun 88 23:06:13 PDT
From: John B. Nagle <jbn@glacier.stanford.edu>
Subject: Re: Dance notation
Smoliar's comment that no dance notation provides sufficient information
for the exact reproduction of a movement is true as far as it goes, but
misleading. Modern dance notation, by which I mean Labanotation or,
as it is sometimes called, kinetography Laban, is designed to convey,
in a concise form, the constraints on a movement necessary to produce the
desired effect. Although the notation provides for detailed description
of arm and hand motions, for example, the choreographer will not ordinarily
specify these unless they are essential to the movement desired. Movements
not specified are left to the discretion of the dancer. Placing the
dancer under unnecessarily tight constraints will result in an unnaturally
stiff performance (it is an ideal in ballet to achieve fluidity
despite overconstraint by the choreographer, but the ideal is reached only
in the better professional companies and at high cost to both company and
dancers). Nor is it usually necessary. Thus the tendency to specify only
the necessary.
The discretion of the dancer in executing a movement specified only
in outline, or what is referred to as "motif writing" in Labanotation, is
not unlimited. There are defaults. Where forward motion is specified
without additional annotation, a normal walk is assumed. There are
sufficient conventions to produce a generally similar performance should
two dancers perform from the same notation.
As a technical tour de force, it is quite possible, by the way, to
record in great detail the positions of the human body during a dance.
Both the inventor of VPL's "Z-glove" and the MIT Media Lab have developed
systems for so doing. It is not at all clear, though, what one does with
the information so obtained. One can play it back through an animation
system, of course. But it is not likely to be useful to a dancer.
John Nagle
------------------------------
Date: Thu, 16 Jun 88 10:49:16 PDT
From: Bob Riemenschneider <rar@ads.com>
Subject: Re^2: definition of information
=> From: bnevin@CCH.BBN.COM (Bruce E. Nevin)
=>
=> My understanding is that Carnap and Bar-Hillel set out to establish a
=> "calculus of information" but did not succeed in doing so.
I'm not sure what your criteria for success are, but it looks pretty good to
me. They didn't completely solve the problem of laying a foundation for
Carnap's approach to inductive logic. (But it certainly advanced the
state of the art--see, e.g., the second of Carnap's _Two Essays on Entropy_,
which was, obviously, heavily influenced by this work.) Advances have been
made since the original paper as well: see the bibiographies for Hintikka's
paper and Carnap's later works on inductive logic (especially "A System of
Inductive Logic" in _Studies in Inductive Logic_, vols. 1 and 2).
[Disclaimer: There are very serious problem's with Carnap's approach
to induction, which I have no wish to defend.]
=> Communication theory refers to a physical system's capacity to transmit
=> arbitrarily selected signals, which need not be "symbolic" (need not mean
=> or stand for anything). To use the term "information" in this connection
=> seems Pickwickian at least. "Real information"? Do you mean the
=> Carnap/Bar-Hillel program as taken up by Hintikka? Are you saying that
=> the latter has a useful representation of the meaning of texts?
The Carnap and Bar-Hillel approach is based on the idea that the information
conveyed by an assertion is that the actual world is a model of the
sentence (or: "... is a member of the class of possible worlds in which
sentence is true", or: "the present situation is a situation in which the
sentence is true", or: <fill in your own, based on your favorite
model-theory-like semantics>). This is certainly the most popular formal
account of information. They, and Hintikka, count state descriptions
to actually calculate the amount of information an assertion conveys, but
that's just because Carnap (and, I suppose, the others) are interested in
the logical notion of probability. If you start with a probability measure
over structures (or possible worlds, or situations, or ... ) as given, you
can be much more elegant--see, e.g., Scott and Krauss's paper on probabilities
over L-omega1-omega-definable classes of structures. (It's in one of those
late-60's North-Holland "Studies in Logic" volumes on inductive logic, maybe
_Aspects of Inductive Logic_.) I don't recall what, if anything, you said
about the application you have in mind, but, as the dynamic logic crowd
discovered, L-omega1-omega is a natural language for talking about computation
in general.
=> Bruce Nevin
=> bn@cch.bbn.com
=> <usual_disclaimer>
-- rar
------------------------------
Date: 13 Jun 88 16:11:58 GMT
From: ames!smu!ti-csl!mips.ti.com!fordyce@spam.istc.sri.com (David
Fordyce)
Subject: OBJECT-ORIENTED DATABASE WORKSHOP: OOPSLA '88
Article-I.D.: ti-csl.51420
OBJECT-ORIENTED DATABASE WORKSHOP
To be held in conjunction with the
OOPSLA '88
Conference on Object-Oriented Programming:
Systems, Languages, and Applications
26 September 1988
San Diego, California, U.S.A.
Object-oriented database systems combine the strengths of
object-oriented programming languages and data models, and database
systems. This one-day workshop will expand on the theme and scope of a
similar OODB workshop held at OOPSLA '87. The 1988 Workshop will
consist of the following four panels:
Architectural issues: 8:30 AM - 10:00 AM
Therice Anota (Graphael), Gordon Landis (Ontologic),
Dan Fishman (HP), Patrick O'Brien (DEC),
Jacob Stein (Servio Logic), David Wells (TI)
Transaction management for cooperative work: 10:30 AM - 12:00 noon
Bob Handsaker (Ontologic), Eliot Moss (Univ. of Massachusetts),
Tore Risch (HP), Craig Schaffert (DEC),
Jacob Stein (Servio Logic), David Wells (TI)
Schema evolution and version management: 1:30 PM - 3:00 PM
Gordon Landis (Ontologic), Mike Killian (DEC),
Brom Mehbod (HP), Jacob Stein (Servio Logic),
Craig Thompson (TI), Stan Zdonik (Brown University)
Query processing: 3:30 PM - 5:00 PM
David Beech (HP), Paul Gloess (Graphael),
Bob Strong (Ontologic), Jacob Stein (Servio Logic),
Craig Thompson (TI)
Each panel member will present his position on the panel topic in 10
minutes. This will be followed by questions from the workshop
participants and discussions. To encourage vigorous interactions and
exchange of ideas between the participants, the workshop will be limited
to 60 qualified participants. If you are interested in attending the
workshop, please submit three copies of a single page abstract to the
workshop chairman describing your work related to object-oriented
database systems. The workshop participants will be selected based on
the relevance and significance of their work described in the abstract.
Abstracts should be submitted to the workshop chairman by 15 August 1988.
Participants selected will be notified by 5 September 1988.
Workshop Chairman:
Dr. Satish M. Thatte
Director, Information Technologies Laboratory
Texas Instruments Incorporated
P.O. Box 655474, M/S 238
Dallas, TX 75265
Phone: (214)-995-0340
Arpanet: Thatte@csc.ti.com CSNet: Thatte%ti-csl@relay.cs.net
Regards, David
------------------------------
Date: 16 Jun 88 21:06:21 GMT
From: nyser!cmx!skolem!kabowen@itsgw.rpi.edu (Ken Bowen)
Subject: LP'88 Conference Announcement
LP'88: 5th Conference on Logic Programming & 5th Symposium on
Logic Programming August 15-19, 1988 University of Washington,
Seattle, Washington
Information and telephone(credit card) registration:
Conference Registration,
University of Washington: (206)-543-2310
##TUTORIALS (All week):
INTRODUCTION TO PROLOG (Mon, 8/15 -- 8:30-5:00) Christopher
Mellish, University of Edinburgh
An introduction to Prolog for engineers, programmers, and scien-
tists with no background in the language. Tutorial Text: Pro-
gramming in Prolog, 3rd ed. W. Clocksin & C. Mellish, Springer-
Verlag.
ABSTRACT INTERPRETATION (Mon 8/15 -- 1:30-5:00) Maurice
Bruynooghe, Universiteit Leuven
Directed at the advanced Prolog programmer, the tutorial will
develop a general framework for extracting global properties of
logic programs (e.g., mode & type inferencing, detecting deter-
minism) via the use of abstract interpretation. The course will
sketch: (1) A formal framework for abstract interpretation of
logic programs which relies on familiar notions about the execu-
tion of logic programs and uses only a small amount of mathemati-
cal machinery concerning partial orders; (2) The process of
developing an application within this framework; (3) High-level
comments on the structure of a correctness proof of an applica-
tion.
IMPLEMENTATION OF PROLOG (Tues, 8/16 -- 8:30-12:00) D.H.D. War-
ren, Manchester Univ.
This tutorial presents the detailed design of the Prolog engine,
now known as the WAM. The tutorial provides a detailed under-
standing of the WAM and why WAM-based Prolog systems are effi-
cient. It also gives insight into how to write efficient Prolog
programs for WAM-based compilers. Attendees should know basic
Prolog programming and it would help to have some familiarity
with compiler technology.
PARALLEL EXECUTION SCHEMES (Thurs, 8/18 -- 8:30-5:00) L. Kale',
Univ. of Illinois
This tutorial will describe the individual schemes for parallel
execution of logic programs that have been proposed so far, and
develop an understanding of their place in the spectrum along the
dimensions of: degree of parallelism, overhead, targeted applica-
tions, and type of multi-processor best suited for the scheme.
The tutorial will be of interest to anyone planning to build a
parallel logic programming system, as well as beginning research-
ers in the area. A basic knowledge of logic programming will be
presumed
CONSTRAINT LOGIC PROGRAMMING (Tues, 8/16 -- 1:30-5:00) J-L.
Lassez et al., IBM
CLP offers a framework to reason with and about constraints in
the context of Logic Programming. The fundamental principles of
this paradigm are presented in order to illustrate the expressive
power of constraints and to show how they naturally merge with a
Logic Programming rule-based system. Next the design and imple-
mentation of a CLP system is discussed, focusing on efficiency
issues of constraint solving, followed by the descriptions of
several applications. A basic knowledge of Prolog is presumed.
CLP AND OPTIONS TRADING (Wed 8/17 -- 8:30-12:00) Catherine
Lassez, IBM and Fumio Mizoguchi, Science Univ. of Tokyo
This tutorial will explore the application of Constraint Logic
Programming (CLP) to financial problems, in particular to options
trading. The chosen examples will demonstrate the special
strengths of combined symbolic and numeric constraint-oriented
reasoning in a logic programming setting. Knowledge of CLP or
attendance at the "Introduction to CLP" tutorial is essential for
this course.
LOGIC PROGRAMMING & LEGAL REASONING (Wed 8/17 -- 8:30-12:00)
Robert Kowalski, Imperial College, and Marek Sergot, Imperial
College
The unique charateristics of legal reasoning are apparent not
only in legal domains, but underlie administrative procedures and
many data processing applications. The use of logic for analyz-
ing legal reasoning has a long tradition. Computer implementa-
tion of legal reasonoing involves representing and reasoning with
legal language, the relationship between rules and regulations,
and the policies they implement. The tutorial will examine the
use of logic programming for analyzing such questions, for both
real and hypothetical cases.
PRACTICAL PROLOG FOR REAL PROGRAMMERS (Thurs, 8/18 -- 1:30-5:00)
Richard O'Keefe, Quintus Computer Systems
This tutorial assumes that you understand the elementary aspects
of Prolog programming, such as recursion, pattern matching, par-
tial data structures, and so on, and want to know how to use Pro-
log to build practical programs. Topics covered will include
"choice points and how to use the cut", "setofPhow it works and
what it is good for", "efficient data structures", "mixed
language programming", and "programming methodology". All topics
will be illustrated by working code.
LOGIC GRAMMARS FOR NL& COMPILING (Fri, 8/19 -- 8:30-12:00) Har-
vey Abramson, Univ. of British Columbia
This tutorial assumes a basic knowledge of Logic Programming
techniques, but does not assume a detailed knowledge either of
linguistics or of compilation techniques. The tutorial will show
how logic programming naturally applies to both natural and for-
mal grammars. Tutorial topics include: 1) Use of Metamorphosis
Grammars and Definite Clause Grammars to produce derivation trees
and semantic transforms; 2) Use of related grammar formalisms;
3) Compilation from natural language to logical form and from
programming languages to machine code using Definite Clause
Translation Grammars, a logical version of Attribute Grammars; 4)
Top-down versus bottom-up parsing, chart-parsing, and the use of
parallelism and concurrency.
##INVITED SPEAKERS:
Layman E. Allen (U. Mich) Multiple Logical Interpretations of
Legal Rules: Impediment or Boon forExpert Systems?
William F. Bayse (FBI) Law Enforcement Applications of Logic Pro-
gramming
Alan Bundy (U. Edinburgh) A Broader Interpretation of Logic in
Logic Programming
Giorgio Levi (U. Pisa) Models, Unfolding Rules, and Fixpoint Se-
mantics
Carlo Zaniolo (MCC) Design & Implementation of a Logic-Based
Language for Data Intensive Applications
OVERALL SCHEDULE:
Sunday (8/14):
3:30-5:30: Registration
5:30-- : Informal reception
Monday (8/15):
9:00-9:30: Opening Session
9:30-10:30 Layman Allen
10:30-11:00 Break
11:00-12:30: Paper sessions: LP & FP #1; E & V #1; Imp #1
12:30-2:00: Lunch
2:00-3:30: Paper sessions: PrE #1; SemN#1; OR// #1
3:30-4:00: Break
4:00-5:30: Paper sessions: Ap #1; SemI #1; Imp #2
Tentative: Panel on Prolog Standards
5:30-- : Conference reception
Tuesday (8/16):
8:30-10:00: Paper sessions: PrS; Cx + MT; //C #1
10:00-10:30: Break
10:30-12:00: Paper sessions: Obj + E & V #2; RP #1; //C #2
12:00-1:30: Lunch
1:30-3:30: Paper sessions: Meta; CN + GP #1; OR// #2
3:30-4:00: Break
4:00-5:00: Giorgio Levi
7:30-- : Demonstrations
Wednesday (8/17):
8:30-10:00: Paper sessions: AbI # 1; &-OR// #1; GP #2
10:00-10:30: Break
10:30-12:00: Alan Bundy
12:00-1:30: Lunch
1:30-- : Free afternoon
Thursday (8/18):
8:30-10:00: Paper sessions: LP&FP#2 + Db#1; RP#2+Types#1; //C # 3
10:00-10:30: Break
10:30-12:00: Paper sessions: Ap #2; Imp #3; SemN #2
12:00-1:00: Lunch
1:00-2:30: Paper sessions: Db #2; SemI#2+Time; Types #2
2:30-3:30: Paper sessions: UC; PrE #2; &-//
3:30-4:00: Break
4:00-5:00: William Bayse
5:30-- : Conference Dinner--Speaker: J. Alan Robinson
Friday (8/19):
8:30-10:00: Paper sessions: Ap #3; AbI #2; &-OR// #2
10:00-10:30: Break
10:30-11:30: Carlo Zanielo
11:30-12:00: Panel/Closing session
##CONTRIBUTED PAPERS:
%%APPLICATIONS & PROGRAMMING METHODOLOGY
* (Ap) Applications
P.G. Bosco, C. Cecchi and C. Moiso, Exploiting the Full Power of
Logic Plus Functional Programming (#1)
Tony Kusalik and C. McCrosky, Improving First-Class Array Expres-
sions Using Prolog (#1)
Toramatsu Shintani, A Fast Prolog-based Inference Engine KORE/IE
(#1)
M. Dincbas, H. Simonis and P. van Hentenryck, Solving a Cutting-
Stock Problem in Constraint Logic Programming (#2)
Catherine Lassez and Tien Huynh, A CLP(R) Option Analysis Sys-
tem(#2)
Peter B. Reintjes, A VLSI Design Environment in PROLOG (#2)
T.W.G. Docker, SAME - A Structured Analysis Tool and its Imple-
mentation in Prolog (#3)
Kevin Steer, Testing Data Flow Diagrams with PARLOG (#3)
*(CN) Constructive negation
David Chan, Constructive Negation Based on the Completed Database
Adrian Walker, Norman Foo, Andrew Taylor and Anand Rao, Deduced
Relevant Types and Constructive Negation
(Db) Databases
Raghu Ramakrishnan, Magic Templates: A Spellbinding Approach to
Logic Programming (#1)
P. Franchi-Zannettacci and I. Attali, Unification-free Execution
of TYPOL Programs by Semantic Attributes Evaluation (#2)
D.B. Kemp and R.W. Topor, Completeness of a Top-Down Query
Evaluation Procedure for Stratified Databases (#2)
Hirohisa Seki and Hidenori Itoh, An Evaluation Method of Strati-
fied Programs under the Extended Closed World Assumption (#2)
*(GP) Grammar & Parsing
R. Trehan and P.F. Wilk, A Parallel Chart Parser for the Commit-
ted Choice Non-Deterministic (CCND) Logic Languages (#1)
Harvey Abramson, Metarules and an Approach to Conjunction in De-
finite Clause Translation Grammars: Some Aspects of... (#2)
Veronica Dahl, Representing Linguistic Knowledge through Logic
Programming (#2)
Lynette Hirschman, William C. Hopkins and Robert Smith, OR-
Parallel Speed-up in Natural Language Processing: A Case Study
(#2)
*(LP&FP) Logic & Functional programming
Jean H. Gallier and Tomas Isakowitz, Rewriting in Order-sorted
Equational Logic (#1)
Claude Kirchner, Order-Sorted Equational Unification (#1)
Joseph L. Zachary, A Pragmatic Approach to Equational Logic Pro-
gramming (#1)
Staffan Bonnier and Jan Maluszynski, Towards a Clean Amalgamation
of Logic Programs with External Procedures (#2)
Steffen Holldobler, From Paramodulation to Narrowing (#2)
*(Meta) Meta-programming
A. Bruffaerts and E. Henin, Proof Trees for Negation as Failure
or Yet Another Prolog Meta-Interpreter
Patrizia Coscia, Paola Franceschi, Giorgio Levi et. al., Meta-
Level Definition and Compilation of Inference Engines in the Ep-
silon Logic Programming Environment
C.S. Kwok and M.J. Sergot, Implicit Definition of Logic Programs
Arun Lakhotia and Leon Sterling,Composing Prolog Meta-
Interpreters
*(Obj) Objects
Weidong Chen and D.S. Warren, Objects as Intensions
John S. Conery, Logical Objects
* (PrE) Programming environments
Miguel Calejo and Luis Moniz Pereira, A Framework for Prolog De-
bugging (#1)
Dave Plummer, Coda: An Extended Debugger for PROLOG (#1)
Ehud Shapiro and Yossi Lichtenstein, Abstract Algorithmic Debug-
ging (#1)
Mike Brayshaw and Marc Eisenstadt, Adding Data and Procedure
Abstraction to the Transparent Prolog Machine (TPM) (#2)
Michael Gorlick and Carl Kesselman, Gauge: A Workbench for the
Performance Analysis of Logic Programs (#2)
*(PrS) Problem-solving & novel techniques
Jonas Barklund, Nils Hagner and Malik Wafin, Condition Graphs
Philippe Codognet, Christian Codognet and Gilberto File, Yet
Another Intelligent Backtracking Method
Sei-ichi Kondoh and Takashi Chikayama, Macro Processing in Prolog
*(Time) Temporal reasoning
Kave Eshghi, Abductive Planning with Event Calculus
*(Ty) Types
Paul Voda, Types of Trilogy (#1)
M.H. van Emden, Conditional Answers for Polymorphic Type Infer-
ence (#2)
Uday S. Reddy, Theories of Polymorphism for Predicate Logic Pro-
grams (#2)
Jiyang Xu and David S. Warren, A Type Inference System for Prolog
(#2)
*(UC) Unification & constraints
D. Scott Parker and R.R. Muntz, A Theory of Directed Logic Pro-
grams and Streams
Graeme S. Port, A Simple Approach to finding the Minimal Subsets
of Equations Needed to Derive a Given Equation by Unification
%%THEORY & PROGRAM ANALYSIS
*(AbI) Abstract interp. & data dependency
Maurice Bruynooghe and Gerda Jenssens, An Instance of Abstract
Interpretation Intergrating Type and Mode Inferencing, Part1: the
abstract domain (#1)
Manuel Hermenegildo, Richard Warren & Saumya Debray, On the Prac-
ticality of Global Flow Analysis of Logic Programs (#1)
Annika Waern, An Implementation Technique for the Abstract In-
terpretation of Prolog (#1)
Saumya Debray, Static Analysis of Parallel Logic Programs (#2)
Kim Marriott and Herald Sondergaard, Bottom-up Abstract Imterpre-
tation of Logic Programs (#2)
Will Winsborough and Annika Waern, Transparent And-Parallelism in
the Presence of Shared Free Variables (#2)
*(Cx) Complexity
K.R. Apt and Howard A. Blair, Arithmetic Classification of Per-
fect Models of Stratified Programs
Stephane Kaplan, Algorithmic Complexity of Logic Programs
*(E&V) Extensions and variations of LP
Donald Loveland and Bruce T. Smith, A Simple Near-Horn Prolog In-
terpreter (#1)
Dale Miller and Gopalan Nadathur, An Overview of l-PROLOG (#1)
Jack Minker, Jorge Lobo and Arcot Rajasekar, Weak Completion
Theory for Non-Horn Programs (#1)
Bharat Jayaraman and Anil Nair, Subset-logic Programming: Appli-
cation and Implementation (#2)
*(RP) Reasoning about programs
Charles Elkan and David McAllester, Automated Inductive Reasoning
about Logic Programs (#1)
Laurent Fribourg, Equivalence-Preserving Transformations of In-
ductive Properties of Prolog Programs (#1)
K. Marriott, L. Naish and J.L. Lassez, Most Specific Logic Pro-
grams (#1)
H. Fujita, A. Okumura and K. Furukawa, Partial Evaluation of GHC
Programs Based on UR-set with Constraint Solving (#2)
John Hannan and Dale Miller, Uses of Higher-Order Unification for
Implementing Program Transformers (#2)
*(SemI) Semantic issues
Aida Batarekh and V.S. Subrahmanian, Semantical Equivalences of
(non-Classical) Logic Programs (#1)
Kenneth Kunen, Some Remarks on the Completed Database (#1)
Maurizio Martelli, M. Falaschi, G. Levi and C. Palamidessi, A New
Declarative Semantics for Logic Languages (#1)
D. Pedreschi and P. Mancarella, An Algebra of Logic Programs (#2)
Stan Raatz and Jean H. Gallier, A Relational Semantics for Logic
Programming (#2)
V. S. Subrahmanian, Intuitive Semantics for Quantitative Rule
Sets (#2)
* (SemN) Semantics of negation
Melvin Fitting and Miriam Ben-Jacob, Stratified and Three-valued
Logic Programming Semantics (#1)
Vladimir Lifschitz and Michael Gelfond, The Stable Model Seman-
tics for Logic Programming (#1)
Teodor Przymusinski, Semantics of Logic Programs and Non-
monotonic Reasoning (#1)
Yves Moinard, Pointwise Circumscription is Equivalent to Predi-
cate Completion (sometimes) (#2)
Halina Przymusinska and Teodor Przymusinski, Weakly Perfect Model
Semantics for Logic Programs (#2)
* (MT) Miscellaneous Theory
M.A. Nait Abdallah, Heuristic Logic and the Process of Discovery
##IMPLEMENTATION & PARALLELISM
* (&//) AND-parallelism
V. Kumar and Y-J Lin, AND-parallel Execution of Logic Programs on
a Shared Memory Multoprocessor: A Summary of Results
Kotagiri Ramamohanarao and Zoltan Somogyi, A Stream AND-Parallel
Execution Algorithm with Backtracking
*(& - OR //) AND-OR parallelism
P. Biswas, Su and Yun, A Scalable Abstract Machine Model to Sup-
port Limited OR (LOR)/Restricted-AND Parallelism (RAP) in Logic
Programs (#1)
K.W. Ng and H.F. Leung, The Competition Model for Parallel Execu-
tion of Logic Programs (#1)
Prabhakaran Raman and Eugene W. Stark, Fully Distributed, AND-OR
Parallel Execution of Logic Programs (#1)
P. Biswas and Tseng, A Data-Driven Parallel Execution Model for
Logic Programs (#2)
Jacques Chassin de Kergommeaux and Philippe Robert, An Abstract
Machine to Implement Efficiently OR-AND Parallel Prolog (#2)
L.V. Kale, B. Ramkumar and W.W. Shu, A Memory Organization In-
dependent Binding Environment for AND and OR Parallel Execution
of Logic Programs (#2)
* (Imp) Implementation
Hamid Bacha, MetaProlog Design and Implementation (#1)
Gerda Janssens, Bart Demoen & Andre Marien, Register Allocation
for WAM, Based upon an Adaptable Unification Order (#1)
Jonathan Mills and Kevin Buettner, Assertive Demons (#1)
D.A. Chu and F.G. McCabe, SWIFT - a New Symbolic Processor (#2)
Subash Shankar, A Hierarchical Associative Memory Architecture
for Logic Programming Unification (#2)
Charles Stormon, Mark Brule and John Oldfield et. al.,
An Architecture Based in Content-Addressable Memory for the Rapid
Execution of Prolog (#2)
David Hemmendinger, A Compiler and Semantic Analyzer Based on
Categorial Grammars (#3)
Feliks Kluzniak, Compile Time Garbage Collection for Proportional
Prolog (#3)
K. Kurosawa, S. Yamaguchi, S. Abe and T. Bandoh, Instruction
Architecture for High Performance Integrated Prolog Processor
IPP (#3)
*(Or//) OR-parallelism and parallel Prolog
Khayri Ali, OR-Parallel Execution of Prolog on BC-Machine (#1)
Lee Naish, Parallelizing NU-Prolog (#1)
Ross Overbeek, Mats Carlsson and Ken Danhof, Practical Issues Re-
lating to the Internal Database Predicates in an OR-Parallel Pro-
log: .... (#1)
Hiyan Alshawi and D.B. Moran, The Delphi Model and some Prelim-
inary Experiments (#2)
Ewing Lusk, Ralph Butler, Terry Disz and Robert Olsen et. al.,
Scheduling OR-Parallelism: an Argonne Perspective (#2)
*(// C) Concurrent sys: GHC, Parlog, CP etc.
Atsuhiro Goto, Y. Kimura, T. Nakagawa and T. Chikayama, Lazy
Reference Counting - An Incremental Garbage Collection Method for
Parallel Inference Machines (#1)
Hamish Taylor, Localising the GHC Suspension Test (#1)
Handong Wu, An Extended Dataflow Model of FGHC (#1)
Leon Alkalaj and Ehud Shapiro, An Architectural Model for a Flat
Concurrent Prolog Processor (#2)
V.J. Saraswat, A Somewhat Logical Formulation of CLP Synchronisa-
tion Primitives (#2)
S. Klinger and E. Shapiro, A Decision Tree Compilation Algorithm
for Flat Concurrent Prolog (#3)
Martin Nilsson and Hidehiko Tanaka, A Flat GHC Implementation for
Supercomputers (#3)
Sven-Olof Nystrom, Control Structures for Guarded Horn Clauses
(#3)
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