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AIList Digest Volume 3 Issue 059
AIList Digest Monday, 6 May 1985 Volume 3 : Issue 59
Today's Topics:
Seminars - Artificial Language Learning (SU) &
Understanding Text with Diagrams (UTexas) &
Semantics and Metaphysics (CSLI) &
Diagram Understanding (SRI) &
Simple Description of the World (CSLI) &
Illocutionary Acts (UCB) &
A Computational Model of Skill Acquisition (SU) &
Marker-Passing during Problem Solving (UToronto)
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Date: Tue, 9 Apr 85 18:23:01 pst
From: gluck@SU-PSYCH (Mark Gluck)
Subject: Seminar - Artificial Language Learning (SU)
Morphological & prosodic cues in the learning
of a miniature phrase-structure language
RICHARD MEIER
(Stanford University)
I will claim that the input to language learning is a grouped
and structured sequence of words and that learning operates most
successfully on such structures, and not on mere word strings. After
briefly reviewing evidence for such groupings in natural language, this
claim will be supported by three experiements in artificial language
learning. These experiments allow rigorous control of the input to the
learner. Prior work had argued that, in such experiments, adult subjects
can learn complex syntactic rules only with extensive semantic mediation.
In the current experiments, subjects fully learned complex aspects of
syntax if they viewed, or heard, sentences (paired with an uninformative
semantics) containing one of three grouping cues for constituent structure:
prosody, function words, or agreement suffixes on the words within a
constituent. Absent such cues, subjects learned only limited aspects of
syntax. These results suggest that, in natural languages, such grouping
cues may subserve syntax learning.
April 12th 3:15pm Jordan Hall; Rm. 100
------------------------------
Date: Wed, 10 Apr 85 13:24:21 cst
From: briggs@ut-sally.ARPA (Ted Briggs)
Subject: Seminar - Understanding Text with Diagrams (UTexas)
Understanding Text with an Accompanying Diagram
by
Bill Bulko
noon Friday April 12
PAI 5.60
We are investigating the mechanisms by which a physics problem
specified jointly by English text and graphics images can be
understood. The investigation is guided by the study of the
following subproblems:
(1) What kinds of rules and knowledge would it take to understand
the information contained in a picture model and a block of
related English text?
(2) What kind of control structure is required?
(3) How can information contained in the picture but not in the
text, and vice versa, be recognized and understood? That is,
how can coreference between text and a picture be handled?
------------------------------
Date: Wed 3 Apr 85 16:26:36-PST
From: Emma Pease <Emma@SU-CSLI.ARPA>
Subject: Seminar - Semantics and Metaphysics (CSLI)
Excerpted from the CSLI Newsletter by Laws@SRI-AI.]
CSLI ACTIVITIES FOR *NEXT* THURSDAY, April 11, 1985
``Semantics for Natural Language: Metaphysics for the Simple-minded?''
Chris Menzel, CSLI
What, exactly, is the connection between semantics and metaphysics?
A semantical theory gives an account of the meaning of certain
expressions in natural language, and, intuitively, the meaning of an
expression has to do with the connection between the expression (or an
utterance of it) and the world. Thus, a simple-minded view might be
that (as far as it goes) a correct semantical theory ipso facto yields
the sober metaphysical truth about what there is.
To the contrary, implicit in much work in semantics is the idea
that all we should expect of a good theory is that it be, in Keenan's
terms, descriptively adequate: it should provide a theoretical
structure which preserves our judgments of logical truth and
entailment, never mind the question of the literal metaphysical
details of the structure (e.g., that the denotations of singular terms
are complex sets of sets rather than individuals).
For next week's TINlunch I will provide a framework for discussion
by laying out the simple-minded view and its chief rival in somewhat
more detail. Being rather simple-minded myself, I'll attempt to
defend a reasonable version of the former. As grist for both
philosophical mills I will draw upon recent work in intensional logic,
Montague grammar, generalized quantifiers, the semantics of plurals,
and situation semantics. --Chris Menzel
------------------------------
Date: Mon 8 Apr 85 11:19:34-PST
From: PENTLAND@SRI-AI.ARPA
Subject: Seminar - Diagram Understanding (SRI)
Area P1 Talk --
WHERE: SRI Int'l Room EK242 (conference room)
WHEN: Tues April 9 at 2:30
DIAGRAM UNDERSTANDING:
THE INTERSECTION OF COMPUTER GRAPHICS AND COMPUTER VISION
Fanya S. Montalvo
MIT, Artificial Intelligence Laboratory
ABSTRACT
A problem common to Computer Vision and Computer Graphics is
identified. It deals with the representation, acquisition, and
validation of symbolic descriptions for visual properties. The
utility of treating this area as one is explained in terms of
providing the facility for diagrammatic conversations with systems. I
call this area "Diagram Understanding", which is analogous to Natural
Language Understanding. The recognition and generation of visual
objects are two sides of the same symbolic coin. A paradigm for the
discovery of higher-level visual properties is introduced, and its
application to Computer Vision and Computer Graphics described. The
notion of denotation is introduced in this context. It is the map
between linguistic symbols and visual properties. A method is
outlined for associating symbolic descriptions with visual properties
in such a way that human subjects can be brought into the loop in
order to validate (or specify) the denotation map. Secondly, a way of
discovering a natural set of visual primitives is introduced.
------------------------------
Date: Wed 3 Apr 85 16:26:36-PST
From: Emma Pease <Emma@SU-CSLI.ARPA>
Subject: Seminar - Simple Description of the World (CSLI)
Excerpted from the CSLI Newsletter by Laws@SRI-AI.]
CSLI ACTIVITIES FOR *NEXT* THURSDAY, April 11, 1985
``What if the World Were Really Quite Simple?''
Alex Pentland, CSLI
One of the major stumbling blocks for efforts in AI has been the
apparent overwhelming complexity of the natural world; for instance,
when an AI program tries to decide on a course of action (or the
meaning of a sentence) it is often defeated by the incredible number
of alternatives to consider. Results such as those of Tversky,
however, argue that people are able to use characteristics of the
current situation to somehow "index" directly into the two or three
most likely alternatives, so that deductive reasoning per se plays a
relatively minor role.
How could people accomplish such indexing? One possibility is that
the structure of our environment is really quite a bit simpler that it
appears on the surface, and that people are able to use this structure
to constrain their reasoning much more tightly than is done in current
AI research.
Is it possible that the world is really relatively simple? In
forming a scientific theory we may trade the size and complexity of
description against the amount of error. Because modern scientific
endeavors have placed great emphasis on increasingly accurate
description, very little effort has gone toward discovering a grain
size of description at which the world may be relatively simply
described while still maintaining a useful level of accuracy.
I will argue that such a simple description of the world is
plausible, discuss progress in discovering such a descriptive
vocabulary, and comment on how knowledge of such a vocabulary might
have a profound impact on AI and psychology. --Alex Pentland
------------------------------
Date: Wed, 24 Apr 85 17:34:14 pst
From: chertok%ucbcogsci@Berkeley (Paula Chertok)
Subject: Seminar - Illocutionary Acts (UCB)
BERKELEY COGNITIVE SCIENCE PROGRAM
Cognitive Science Seminar -- IDS 237B
TIME: Tuesday, April 30, 11 - 12:30
PLACE: 240 Bechtel Engineering Center
SPEAKER: Herbert H. Clark, Department of Psychology, Stan-
ford University
TITLE: ``Illocutionary acts, illocutionary perfor-
mances''
From John Austin on, theorists have said a good deal about
what it is to be a question, assertion, promise, or other illocu-
tionary act. But in their characterizations they have generally
assumed a rather strong idealization about how illocutionary acts
are performed. Among other things, they have taken these four
points for granted: (1) An illocutionary act is a preplanned
event. (2) It is performed by the speaker acting alone. (3) The
speaker acts with certain definite intentions about affecting his
addressee. And (4) the speaker discharges these intentions
merely by issuing a sentence (or sentence surrogate) in the right
circumstances. As with any idealization, these assumptions
aren't quite right. Indeed, I will document that illocutionary
acts in conversation are not preplanned events but processes that
the participants may alter midcourse for various purposes, and
that they are accomplished by the speaker and addressees acting
together. Once the traditional assumptions are replaced by more
realistic ones, we are led to quite a different notion of illocu-
tionary act.
The view I will develop is that performing illocutionary
acts in conversation is a collaborative process between speaker
and addressees. One of the goals of these participants is to
establish the mutual belief, roughly by the beginning of each new
contribution, that the addressees have understood the speaker's
meaning well enough for current purposes. The speaker and
addressees have systematic linguistic techniques for reaching
this goal. In support of this view I will report a study by
Deanna Wilkes-Gibbs and myself on how definite references get
made in conversation and another study by Edward F. Schaefer and
myself on what it is, more generally, to make certain contribu-
tions to conversation.
------------------------------
Date: Thu, 25 Apr 85 05:01:59 pst
From: gluck@SU-PSYCH (Mark Gluck)
Subject: Seminar - A Computational Model of Skill Acquisition (SU)
[Forwarded from the CSLI bboard by Laws@SRI-AI.]
Psych. Dept. Friday Cognitive Seminar
April 26th 3:15pm Jordan Hall; Rm. 100
A Computational Model of
Skill Aquisition
KURT VAN LEHN (Xerox PARC)
A theory will be presented that describes how people learn certain
procedural skills, such as the written algorithms of arithmetic and
algebra, from multi-lesson curricula. There are two main hypotheses.
(1) Teachers enforce, perhaps unknowingly, certain constraints that
relate the structure of the procedure to the structure of the lesson
sequence, and moreover, students employ these constraints, perhaps
unknowingly, as they induce a procedure from the lesson sequence. (2)
As students follow the procedure they have induced, they employ a
certain kind of meta-level problem solving to free themselves when their
interpretation of the procedure gets stuck. The theory's predictions,
which are generated by a computer model of the putative learning and
problem solving processes, have been tested against error data from
several thousand students. The usual irrefutability of computer
simulations of complex cognition has been avoided by a linguistic style
of argumentation that assigns empirical responsibility to individual
hypotheses.
------------------------------
Date: Wed, 10 Apr 85 13:04:45 est
From: Voula Vanneli <voula%toronto.csnet@csnet-relay.arpa>
Subject: Seminar - Marker-Passing during Problem Solving (UToronto)
UNIVERSITY OF TORONTO
DEPARTMENT OF COMPUTER SCIENCE
(GB = Galbraith Bldg., 35 St. George St.)
ARTIFICIAL INTELLIGENCE SEMINAR - Wednesday, April 10, 4 pm,
GB 244
Jim Hendler
Dept. of Computer Science, Brown University
Studies of Marker Passing in Knowledge Representation
and Problem Solving Systems.
A standard problem in Artificial Intelligence systems that
do planning or problem solving is called the "late-
information, early-decision paradox." This occurs when the
planner makes a choice as to which action to consider, prior
to encountering information that could either identify an
optimal solution or that would present a contradiction. As
the decision is made in the absence of this information it
is often the wrong one, leading to much needless processing.
In this talk I describe how the technique known as "marker-
passing" can be used by a problem-solver. Marker-passing,
which has been shown in the past to be useful for such cog-
nitive tasks as story comprehension and word sense disambi-
guation, is a parallel, non-deductive, "spreading activa-
tion" algorithm. By combining this technique with a plan-
ning system the paradox described above can often be circum-
vented. The marker-passer can also be used by the problem-
solver during "meta-rule" invocation and for finding certain
inherent problems in plans. An implementation of such a
system is discussed as are the design "desiderata" for a
marker-passer.
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End of AIList Digest
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