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AIList Digest Volume 4 Issue 200
AIList Digest Monday, 29 Sep 1986 Volume 4 : Issue 200
Today's Topics:
Seminars - Chemical Structure Generation (SU) &
Fuzzy Relational Databases (SMU) &
General Logic (MIT) &
Generic Tasks in Knowledge-Based Reasoning (MIT),
Conference - Workshop on Qualitative Physics
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Date: Mon 22 Sep 86 23:39:33-PDT
From: Olivier Lichtarge <LICHTARGE@SUMEX-AIM.ARPA>
Subject: Seminar - Chemical Structure Generation (SU)
I will be presenting my thesis defense in biophysics Thursday
September 25 in the chemistry Gazebo, starting at 2:15.
Solution Structure Determination of Beta-endorphin by NMR
and
Validation of Protean: a Structure Generation Expert System
Solution structure determination of proteins by Nuclear Magnetic
Resonance involves two steps. First, the collection and interpretation
of data, from which the secondary structure of a protein is
characterized and a set of constraints on its tertiary structure
identified. Secondly, the generation of 3-dimensional models of the
protein which satisfy these constraints. This thesis presents works in
both these areas: one and two-dimensional NMR techniques are applied
to study the conformation of @g(b)-endorphin; and Protean, a new
structure generation expert system is introduced and validated by
testing its performance on myoglobin.
It will be shown that @g(b)-endorphin is a random coil in water. In
a 60% methanol and 40% water mixed solvent the following changes take
place: an @g(a)-helix is induced between residues 14 and 27, and a
salt bridge forms between Lysine28 and Glutamate31, however, there
still exists no strong evidence for the presence of tertiary structure.
The validation of Protean establishes it as an unbiased and accurate
method of generating a representative sampling of all the possible
conformations which satisfy the experimental data. At the solid level,
the precision is good enough to clearly define the topology of the
protein. An analysis of Protean's performance using data sets of
dismal to ideal quality permits us to define the limits of the
precision with which a structure can be determined by this method.
------------------------------
Date: WED, 20 apr 86 17:02:23 CDT
From: E1AR0002%SMUVM1.BITNET@WISCVM.WISC.EDU
Subject: Seminar - Fuzzy Relational Databases (SMU)
Design of Similarity-Based (Fuzzy) Relational Databases
Speaker: Bill P. Buckles, University of Texas, Arlington
Location 315 SIC, Southern Methodist University
Time: 2:00PM
While the core of an expert system is its inference mechanism, a
common component is a database or other form of knowledge
representation. The authors have developed a variation of the
relational database model in which data that is linguistic or
inherently uncertain may be represented. The keystone concept of
this representation is the replacement of the relationship " is
equivalent to" with the relationship "is similar to". Similarity is
defined in fuzzy set theory as an $n sup 2$ relationship over a
domain D, |D| = n such that
i. s(x,x)=1, x member D
ii. s(x,y)=s(y,x) x,y member D
iii. s(x,y) >= max[min(s(x,y),s(y,z))]; x, y, z member D
Beginning with a universal relation, a method is given for developing
the domain sets, similarity relationships and base relations for a
similarity-based relational database. The universal relation itself
enumerates all domains. The domain sets may be numeric (in which case
no further design is needed) or scalar (in which case the selection of
a comprehensive scalar set is needed). Similarity relationship
contains $n sup 2$ values where n is the number of scalars in a domain
set. A method is described for developing a set of consistent values
when initially given n-1 values. The base relations are derived using
fuzzy functional dependencies. This step also requires the
identification of candidate keys.
------------------------------
Date: Fri 26 Sep 86 10:47:21-EDT
From: Lisa F. Melcher <LISA@XX.LCS.MIT.EDU>
Subject: Seminar - General Logic (MIT)
Date: Thursday, October 2, 1986
Time: 1:45 p.m......Refreshments
Time: 2:00 p.m......Lecture
Place: NE43 - 512A
" GENERAL LOGIC "
Gordon Plotkin
Department of Computer Science
University of Edinburgh, Scotland
A good many logics have been proposed for use in Computer Science.
Implementing them involves repeating a great deal of work. We propose a
general account of logics as regards both their syntax and inference rules.
As immediate target we envision a system to which one inputs a logic
obtaining a simple proof-checker. The ideas build on work in logic of
Paulson, Martin-Lof and Schroeder-Heister and in the typed lambda-calculus of
Huet and Coquand and Meyer and Reinhold. The slogan is: Judgements are
Types. For example the judgement that a proposition is true is identified
with its type of proofs; general and hypothetical judgements are identified
with dependent product types. This gives one account of Natural Deduction.
It would be interesting to extend the work to consider (two-sided) sequent
calculi for classical and modal logics.
Sponsored by TOC, Laboratory for Computer Science
Albert Meyer, Host
------------------------------
Date: Fri 26 Sep 86 14:47:36-EDT
From: Rosemary B. Hegg <ROSIE@XX.LCS.MIT.EDU>
Subject: Seminar - Generic Tasks in Knowledge-Based Reasoning (MIT)
Date: Wednesday, October 1, 1986
Time: 2.45 pm....Refreshments
3.00 pm....Lecture
Place: NE43-512A
GENERIC TASKS IN KNOWLEDGE-BASED REASONING:
CHARACTERIZING AND DESIGNING EXPERT SYSTEMS AT THE
``RIGHT'' LEVEL OF ABSTRACTION
B. CHANDRASEKARAN
Laboratory for Artificial Intelligence Research
Department of Computer and Information Science
The Ohio State University
Columbus, Ohio 43210
We outline the elements of a framework for expert system design that
we have been developing in our research group over the last several years.
This framework is based on the claim that complex knowledge-based reasoning
tasks can often be decomposed into a number of @i(generic tasks each
with associated types of knowledge and family of control regimes). At
different stages in reasoning, the system will typically engage in
one of the tasks, depending upon the knowledge available and the state
of problem solving. The advantages of this point of view are manifold:
(i) Since typically the generic tasks are at a much higher level of abstraction
than those associated with first generation expert system languages,
knowledge can be acquired and represented directly at the level appropriate to
the information processing task. (ii) Since each of the generic tasks
has an appropriate control regime, problem solving behavior may be
more perspicuously encoded. (iii) Because of a richer generic vocabulary
in terms of which knowledge and control are represented, explanation of
problem solving behavior is also more perspicuous. We briefly
describe six generic tasks that we have found very useful in our
work on knowledge-based reasoning: classification, state abstraction,
knowledge-directed retrieval, object synthesis by plan selection and
refinement,
hypothesis matching, and assembly of compound hypotheses for
abduction.
Host: Prof. Peter Szolovits
------------------------------
Date: Fri, 26 Sep 86 12:41:26 CDT
From: forbus@p.cs.uiuc.edu (Kenneth Forbus)
Subject: Conference - Workshop on Qualitative Physics
Call for Participation
Workshop on Qualitative Physics
May 27-29, 1987
Urbana, Illinois
Sponsored by:
the American Association for Artificial Intelligence
and
Qualitative Reasoning Group
University of Illinois at Urbana-Champaign
Organizing Committee:
Ken Forbus (University of Illinois)
Johan de Kleer (Xerox PARC)
Jeff Shrager (Xerox PARC)
Dan Weld (MIT AI Lab)
Objectives:
Qualitative Physics, the subarea of artificial intelligence concerned with
formalizing reasoning about the physical world, has become an important and
rapidly expanding topic of research. The goal of this workshop is to
provide an opportunity for researchers in the area to communicate results
and exchange ideas. Relevant topics of discussion include:
-- Foundational research in qualitative physics
-- Implementation techniques
-- Applications of qualitative physics
-- Connections with other areas of AI
(e.g., machine learning, robotics)
Attendance: Attendence at the workshop will be limited in order to maximize
interaction. Consequently, attendence will be by invitation only. If you
are interested in attending, please submit an extended abstract (no more
than six pages) describing the work you wish to present. The extended
abstracts will be reviewed by the organizing committee. No proceedings will
be published; however, a selected subset of attendees will be invited to
contribute papers to a special issue of the International Journal of
Artificial Intelligence in Engineering. There will be financial assistance
for graduate students who are invited to attend.
Requirements:
The deadline for submitting extended abstracts is February 10th. On-line
submissions are not allowed; hard copy only please. Any submission over 6
pages or rendered unreadable due to poor printer quality or microscopic font
size will not be reviewed. Since no proceedings will be produced, abstracts
describing papers submitted to AAAI-87 are acceptable. Invitations will be
sent out on March 1st. Please send 6 copies of your extended abstracts to:
Kenneth D. Forbus
Qualitative Reasoning Group
University of Illinois
1304 W. Springfield Avenue
Urbana, Illinois, 61801
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End of AIList Digest
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