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AIList Digest Volume 5 Issue 075
AIList Digest Thursday, 12 Mar 1987 Volume 5 : Issue 75
Today's Topics:
Seminars - Artificial and Natural Intelligence (UCB) &
Fluid Concepts and Creative Analogies (UMich) &
Representational Alignment (UCB) &
Induction, Knowledge, and Expert Systems (GMR) &
Search and Reasoning in AI (CMU) &
Multilisp (CMU)
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Date: Mon, 9 Mar 87 11:22:39 PST
From: admin%cogsci.Berkeley.EDU@berkeley.edu (Cognitive Science Program)
Subject: Seminar - Artificial and Natural Intelligence (UCB)
Berkeley Cognitive Science Program Presents a Special IDS 237B Seminar
Time: Thursday, March 19, 11:00-12:30
Place: Building T-4, Room 200
Speaker: Klaus Fuchs-Kittowski, Dept. of Theory and Organization of Science,
Humboldt University, Berlin, GDR
Visiting Professor, John Hopkins University
Chairman, Working Group 1 of TC9 of IFIP Computers
Title: ``Philosophical Views and Methodological Assumptions Regarding
the Relationships between Artificial and Natural Intelligence"
ABSTRACT
Computers are general agents of change in the information revo-
lution in the same way that Watt's steam engine revolutionized
industry. The problem of defining the relationship between
human and artificial intelligence is central to the problem of
applying computer power in a humane way in society. Resolution
of these questions requires application of multidisciplinary
approaches to what has traditionally been a mechanistic
approach. The multidisciplinary approach requires understanding
the unity of the syntax, semantics, and effects of information.
------------------------------
Date: Sun, 8 Mar 87 17:25:12 est
From: mm@farg.umich.edu (Melanie Mitchell)
Subject: Seminar - Fluid Concepts and Creative Analogies (UMich)
WEEKLY AI SEMINAR, UNIVERSITY OF MICHIGAN, ANN ARBOR
SPEAKER: Melanie Mitchell, EECS Dept., University of Michigan
DATE: Tuesday, March 17
TIME: 4:30 pm
PLACE: 1303 EECS Building (North Campus)
TITLE: "Fluid Concepts and Creative Analogies:
A Theory and its Computer Implementation"
Abstract
This talk is based on research done by Douglas R. Hofstadter,
Melanie Mitchell, and Robert M. French. We describe the principles
of Copycat, a computer model of how humans use concepts fluidly in
order to create analogies. Our model is centered on the Slipnet, a
network of overlapping concepts whose shapes are determined dynamically
by the situations faced by the program. Reciprocally, the state of the
Slipnet controls how Copycat perceives situations. The heart of what
Copycat does, given two situations, is to produce a worlds-mapping: a
coarse-grained mental correspondence between the situations, involving
two interdependent and mutually consistent facets: an object-to-object
mapping realized in structures called bridges, and a concept-to-concept
mapping realized in structures called pylons. Each pylon expresses a
so-called conceptual slippage, borrowed from the slipnet. Taken together,
the slippages constitute a recipe for translating actions in one situation
into their analogues in the other. Through the "coattails effect",
slippages can induce closely related slippages, allowing deeper and more
subtle analogies to be produced than would otherwise be possible.
For copies of a paper describing this research, send messages to
mm@farg.umich.EDU
------------------------------
Date: Wed, 11 Mar 87 10:36:25 PST
From: admin%cogsci.Berkeley.EDU@berkeley.edu (Cognitive Science
Program)
Subject: Seminar - Representational Alignment (UCB)
SESAME Colloquium 10/16
Jeff Shrager
Xerox Palo Alto Research Center
Monday 16 March 1987
2515 Tolman Hall
4:00 pm
Abstract
Analogy and conceptual combination deal with more than one knowledge
structure. Only structures which are based on the same terms and
relations can generally be combined by these mechanisms. In order to
make conceptual combination work smoothly with large representationally
heterogeneous knowledge bases, I am working toward automated high-level
to high-level representational alignment. My approach is based upon the
intuitive model of how two speakers would communicate if they had
incompatible understandings of some domain. The process involves
"grounding" terms and relations in the high-level representations into
common lower-level representations and then constructing constraints
based upon the structure of this grounding trace. This talk will focus
on the cognitive motivations for grounding and ground-directed alignment
and on the cognitive implications of the requirements imposed on mental
models by ground-directed alignment. Grounding highlights the
difference in the content terms of mental models: grounded terms versus
ungrounded terms, which have a counterpart in the difference between
empirical and derived terms in qualitative mental models. I show how
the grounding of such models into animations gives us a concrete handle
on the relationship between imagery and the symbolic processes.
------------------------------
Date: Wed, 11 Mar 87 15:55 EST
From: "R. Uthurusamy" <SAMY%gmr.com@RELAY.CS.NET>
Subject: Seminar - Induction, Knowledge, and Expert Systems (GMR)
Seminar at the General Motors Research Laboratories in Warren, Michigan.
Friday, March 20, 1987 at 10 a.m.
INDUCTION, KNOWLEDGE, and EXPERT SYSTEMS
J. ROSS QUINLAN
Head, School of Computing Sciences
New South Wales Institute of Technology, Sydney, Australia
ABSTRACT
This general talk examines inductive inference as a knowledge acquisition
methodology, both from the perspective of the performance characteristics
of the knowledge so acquired and its intelligibility. A relatively simple
class of induction methods that generate decision trees for classification
tasks is outlined and illustrated. A case study in which this approach was
used to generate diagnostic knowledge in the domain of thyroid assays is
presented, and the performance of the decision trees is compared with that
of a conventional expert system constructed by interviewing endocrinologists.
Finally, recent work in which decision trees are re-expressed as collections
of production rules has been found to improve both the accuracy and
comprehensibility of the inductively acquired knowledge.
Non-GMR personnel interested in attending please contact
R. Uthurusamy [ samy@gmr.com ] 313-986-1989
------------------------------
Date: 9 Mar 87 16:59:31 EST
From: Patty.Hodgson@isl1.ri.cmu.edu
Subject: Seminar - Search and Reasoning in AI (CMU)
AI SEMINAR
TOPIC: Search and Reasoning in AI
SPEAKER: Herb Simon
PLACE: Wean Hall 5409
DATE: Tuesday, March 10, 1987
TIME: 3:30 pm
ABSTRACT: What is the relation between the search paradigm in AI and
the reasoning or deductive paradigm implicit or explicit in most theorem
proving programs, PROLOG, and Nilsson's text?? The talk will undertake to
show that these two points of view cannot be distinguished on logic grounds
but that they represent very different heuristic viewpoints about how
AI systems are to be constructed, and about the relation of these systems
to the "real world." The talk will develop and extend views published in
Artificial Intelligence 28 21:7-29 (1983).
------------------------------
Date: 10 Mar 87 11:55:40 EST
From: Karen.Olack@h.cs.cmu.edu
Subject: Seminar - Multilisp (CMU)
Speaker: Robert Halstead
Date: March 16, 1987
Time: 2:00 p.m.
Place: Wean Hall 8220
Topic: Multilisp: A Language for Parallel Symbolic Computing
ABSTRACT
Multilisp is an extension of Scheme with additional operators and
additional semantics for parallel execution. These have been added
without removing side effects from the language. The principal
parallelism construct in Multilisp is the "future," which exhibits some
features of both eager and lazy evaluation. Current work focuses on
making Multilisp a more humane programming environment, on expanding the
power of Multilisp to express task scheduling policies, and on measuring
the properties of Multilisp programs with the goal of designing a
parallel architecture well tailored for efficient Multilisp execution.
Multilisp has been implemented, and runs on the shared-memory
Concert multiprocessor, using as many as 27 processors. The
implementation uses interesting techniques for task scheduling and
garbage collection. The task scheduler helps control excessive resource
utilization by means of an unfair scheduling policy; the garbage
collector uses a multiprocessor algorithm modeled after the incremental
garbage collector of Baker.
The talk will briefly describe Multilisp, discuss the areas of
current activity, and indicate the future direction of the project in
the areas of language design, application development, and
multiprocessor architecture.
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End of AIList Digest
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