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AIList Digest Volume 4 Issue 076
AIList Digest Thursday, 10 Apr 1986 Volume 4 : Issue 76
Today's Topics:
Seminars - NL Interfaces to Expert Systems (Villanova) &
Minsky (SIU-Edwardsville) &
Frames and Objects in Modeling and Simulation (SU) &
Machine Inductive Inference (UPenn) &
Conditionals and Inheritance (CMU) &
Knowledge Retrieval as Specialized Inference (CMU) &
Ontology and Efficiency in a Belief Reasoner (UPenn) &
Probabilistic Inference: Theory and Practice (SMU),
Conference - Southern California AI Conference Program
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Date: Fri, 4 Apr 86 13:09 EST
From: Tim Finin <Tim%upenn.csnet@CSNET-RELAY.ARPA>
Subject: Seminar - NL Interfaces to Expert Systems (Villanova)
I got an announcement in the mail this week about the first meeting of the
DELAWARE VALLEY AI ASSOCIATION. It will be held at Villanova University
(Tolentine Hall, room 215) on April 21st at 7:30pm. The meeting will
discuss the organizational structure of the association, introduce the
current officers, and feature a talk by Bonnie Webber on "Natural Language
Interfaces to Expert Systems".
DIRECTIONS: from rt. 320 North turn right onto route 30. At the first
light, turn right into the parking lot. Walk across route 30 and proceed
along the walkway towards the chapel. Turn left at the Chapel to Tolentine
Hall, which is about 50 yards to the right.
For more information, call 215-265-1980.
------------------------------
Date: 8 Apr 1986 13:30-EST
From: ISAACSON@USC-ISI.ARPA
Subject: Seminar - Minsky (SIU-Edwardsville)
Marvin Minsky will be in the St. Louis area on Tuesday and Wednesday,
April 22, 23. He'll give a talk at Southern Illinois University at
Edwardsville on:
THE SOCIETY OF MIND
Science Labs Bldg., Room 1105
Tuesday, 7:30 pm
April 22, 1986
Admission is free and people in the St. Louis area are welcome.
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Date: Tue 8 Apr 86 16:27:21-PST
From: Christine Pasley <pasley@SRI-KL>
Subject: Seminar - Frames and Objects in Modeling and Simulation (SU)
CS529 - AI In Design & Manufacturing
Instructor: Dr. J. M. Tenenbaum
Title: Frames and Objects: Application to Modeling And Simulation
Speaker: Richard Fikes and Marilyn Stelzner
From: Intellicorp
Date: Wednesday, April 9, 1986
Time: 4:00 - 5:30
Place: Terman 556
We will describe the characteristic features of frame-based knowledge
representation facilities and indicated how they can provide a
foundation for a variety of knowledge-system functions. We will focus
on how frames can contribute to a knowledge sytem's reasoning
activities and how they can be used to organize and direct those
activities. Application to engineering modelling and simulation will
be discussed.
Visitors welcome.
------------------------------
Date: Tue, 8 Apr 86 12:00 EST
From: Tim Finin <Tim%upenn.csnet@CSNET-RELAY.ARPA>
Subject: Seminar - Machine Inductive Inference (UPenn)
Forwarded From: Dale Miller <Dale@UPenn> on Tue 8 Apr 1986 at 8:35
UPenn Math-CS Logic Seminar
SOME RECENT RESEARCH ON MACHINE INDUCTIVE
INFERENCE
Scott Weinstein
Tuesday, 8 April 1986, 4:30 - 6:00, 4N30 DRL
The talk will survey some recent (and not so recent) results on the inference
of r.e. sets and first-order structures.
------------------------------
Date: 8 Apr 1986 1416-EST
From: Lydia Defilippo <DEFILIPPO@C.CS.CMU.EDU>
Subject: Seminar - Conditionals and Inheritance (CMU)
Speaker: Rich Thomason
Date: Thursday, April 17
Time: 3:00 pm
Place: 4605
Topic: CONDITIONALS AND INHERITANCE
This talk will provide motivation and an overview of an
NSF-sponsored research project that has recently begun here, involving
David Touretzky, Chuck Cross, Jeff Horty, and Kevin Kelly. The portion
of the project on which I will concentrate aims at bringing logical work
on conditionals to bear on nonmonotonic reasoning, and in particular on
inheritance theory.
Some of the background for the theory consists in the need for a
qualitative approach to "belief kinematics" (or knowledge revision, or
database update), as opposed to a quantitative approach such as the
Bayesian one. The logic of conditionals provides some principles for
such an approach, where the conditionals are interpreted as indicative
expressions of willingness to make belief transitions.
Although we have many firm intuitions about inheritance in
particular cases, it is difficult to establish a correct general
definition of nonmonotonic inheritance for arbitrary semantic nets.
I will show how a definition of inheritance generates a definition
of validity for simple conditional expressions, and will suggest that
this can be used as a criterion to judge inheritance definitions.
I will present some results relating particular inheritance definitions
to conditional logics.
These results depend on a kind of ad hoc update procedure for
semantic nets. I will suggest that a better procedure might be
obtained by considering nets with both monotonic and nonmonotonic
links.
If time permits, I will develop some analogies between semantic
nets and Gentzen systems or natural deduction.
------------------------------
Date: 8 April 1986 1615-EST
From: Betsy Herk@A.CS.CMU.EDU
Subject: Seminar - Knowledge Retrieval as Specialized Inference (CMU)
Speaker: Alan M. Frisch, University of Rochester
Date: Tuesday, April 22
Time: 3:30 - 5:00
Place: 5409 Wean Hall
Title: Knowledge retrieval as specialized inference
Artificial intelligence reasoning systems commonly contain a large
corpus of declarative knowledge, called a knowledge base (KB), and
provide facilities with which the system's components can retrieve
this knowledge.
Consistent with the necessity for fast retrieval is the guiding
intuition that a retriever is, at least in simple cases, a pattern
matcher, though in more complex cases it may perform selected
inferences such as property inheritance.
Seemingly at odds with this intuition, the thesis of this talk is that
the entire process of retrieval can be viewed as a form of inference
and hence the KB as a representation, not merely a data structure. A
retriever makes a limited attempt to prove that a queried sentence is
a logical consequence of the KB. When constrained by the no-chaining
restriction, inference becomes indistinguishable from pattern-matching.
Imagining the KB divided into quanta, a retriever that respects this
restriction cannot combine two quanta in order to derive a third.
The techniques of model theory are adapted to build non-procedural
specifications of retrievability relations, which determine what
sentences are retrievable from what KB's. Model-theoretic
specifications are presented for four retrievers, each extending
the capabilities of the previous one. Each is accompanied by a
rigorous investigation into its properties, and a presentation of
an efficient, terminating algorithm that can be proved to meet the
specification.
------------------------------
Date: Wed, 9 Apr 86 15:01 EST
From: Tim Finin <Tim%upenn.csnet@CSNET-RELAY.ARPA>
Subject: Seminar - Ontology and Efficiency in a Belief Reasoner (UPenn)
Forwarded From: Bonnie Webber <Bonnie@UPenn>
Forwarded From: Glenda Kent <Glenda@UPenn>
ONTOLOGY AND EFFICIENCY IN A BELIEF REASONER
Anthony S. Maida
Department of Computer Science
Penn State University
This talk describes the implementation of, and theoretical influences
underlying, a belief reasoner called the "Belief Space Engine." A belief
reasoner is a program that reasons about the "beliefs" of other agents. The
Belief Space Engine uses specialized data structures, called belief spaces, to
compute a certain class of inferences about the beliefs of other agents
efficiently. Theoretically, the architecture is motivated by a syntactic
simulation ontology, which is an alternative to the possible-worlds ontology.
In order to encode this ontology, a meta description facility has been
implemented.
This talk is organized as follows. First, we explain the semantic difficulties
with belief reasoning that stem from interactions between belief, equality, and
quantification. Next, we argue for the sufficiency of the syntactic simulation
ontology to address the difficulties we described. Then we show how the
ontology is partially embodied in the Belief Space Engine. Finally, we show
that the Belief Space Engine is robust in this domain by programming several
examples.
Thursday, April 10, 1986
Room 216 - Moore School
3:00 - 4:30 p.m.
Refreshments Available
------------------------------
Date: WED, 10 JAN 84 17:02:23 CDT
From: E1AR0002%SMUVM1.BITNET@WISCVM.WISC.EDU
Subject: Seminar - Probabilistic Inference: Theory and Practice (SMU)
Title: Probabilistic Inference: Theory and Practice
Speaker: Won D. Lee
University of Illinois at Urbana- Champaign
Location: 315SIC
Time: 2:00 PM
This talk presents a system and a methodology for probabilistic learning
from examples.
First, I present a new methodology, Probabilistic Rule Generator
(PRG), of variable-valued logic synthesis which can be applied
effectively to noisy data. Then a new system, Probabilistic
Inference, which can generate concepts with limited time and/or
resources is defined. It is discussed how PRG can be a practical tool
for Probabilistic Inference.
A departure from the classical viewpoint in logic minimization, and in
knowledge acquisition is reported.
------------------------------
Date: Wed, 9 Apr 86 19:52:30 PST
From: cottrell@nprdc.arpa (Gary Cottrell)
Subject: Conference - Southern California AI Conference Program
Southern California Conference on Artificial Intelligence
Saturday, April 26, 1986
Peterson Hall
UCSD
Sponsored by San Diego SIGART and SCAIS
9:00am Registration Desk Opens
10:00am-12:00pm Invited Overviews
10:00am-10:25am AI Environment and Research at UCLA
Michael G. Dyer and Josef Skrzypek, UCLA AI Lab
10:30am-10:55am Ai Research at USC
Peter Norvig, USC
11:00am-11:25am Parallel Distributed Processing:
Explorations in the Microstructure of Cognition
David E. Rumelhart, Institute for Cognitive Science, UCSD
11:30am-11:55am Human Computer Interaction: Research at the
Intelligent Systems Group
Jim Hollan, Intelligent Systems Group, UCSD
12:00-1:00 Buffet Lunch
1:00pm-3:00pm SCAIS Session I: Expert Systems
1:00pm-1:15pm RAMBOT: A connectionist expert system that
learns by example
Michael C. Mozer, Institute for Cognitive Science, UCSD
1:20pm-1:35pm A small expert system that learns
George S. Levy, Counseling and Consulting Associates, San Diego
1:40pm-1:55pm A knowledge based selection system
Xi-an Zhu, Dept. of Electrical Engineering, USC
2:00pm-2:15pm STYLE Counselor: An expert system to select ties
Jeffrey Blake, Peter Tenereillo, and Jeff Wicks
Department of Mathematical Sciences, SDSU
2:20pm-2:35pm A health and nutrition expert system
Marwan Yacoub, Department of Mathematical Sciences, SDSU
2:40pm-2:55pm An inexact reasoning scheme based on intervals
of probabilities
Koenraad Lecot, Computer Science Dept., UCLA
1:00pm-3:00pm SCAIS Session 2: Vision and Natural Language
1:00pm-1:15pm A Scheme-based PC vision workstation
Michael Stiber and Josef Skrzypek, CS Dept., UCLA and CRUMP Inst.
1:20pm-1:35pm Early Vision: 3-D silicone solution to
lightness constancy
Paul C. H. Lin and Josef Skrzypek, CS Dept., UCLA and CRUMP Inst.
1:40pm-1:55pm A connectionist computing architecture for
textural segmentation
Edmond Mesrobian and Josef Skrzypek, CS Dept., UCLA and CRUMP Inst.
2:00pm-2:15pm ANIMA: Analogical Image Analysis
Arthur Newman, Computer Science Dept., UCLA
2:20pm-2:35pm Representing pragmatic knowledge in lexical
memory
Michael Gasser, Artificial Intelligence Laboratory, UCLA
2:40pm-2:55pm The role of mental spaces in establishing
universal principles for the semantic interpretation of
cliches
Michelle Gross, Linguistics Dept., UCSD
1:00pm-3:00pm SIGART Session 1
1:00pm-1:25pm Using commonsense knowledge for prepositional
phrase attachment
K. Dahlgren, IBM
1:30pm-1:55pm Social Intelligence
Les Gasser, Computer Science Dept., USC
2:00pm-2:25pm A unified algebraic theory of logic and
probability
Philip Calabrese, LOGICON
2:30pm-2:55pm Learning while searching in constraint-
satisfaction problems
Rina Dechter, & Hughes AI Center Cognitive Systems Lab, UCLA
3:00-3:30 Coffee Break
3:30pm-5:30pm SCAIS Session 3: Connectionist Models & Learning
3:30pm-3:45pm Toward optimal parameter selection in the
back-propagation algorithm
Yves Chauvin, Institute for Cognitive Science, UCSD
3:50pm-4:05pm Inverting a connectionist network mapping by
back-propagation of error
Ron Williams, Institute for Cognitive Science, UCSD
4:10pm-4:25pm Learning internal representations from gray scale images
Gary Cottrell and Paul Munro, Institute for Cognitive Science, UCSD
4:30pm-4:45pm Decomposition in perceptron systems
Rik Verstraete, Computer Science Dept., UCLA
4:50pm-5:05pm Adaptive Self-Organizing Logic Networks
Tony Martinez, ***
5:10pm-5:25pm Human understanding in diverse environments
Louis Rossi, Harvey Mudd College
3:30pm-5:30pm SCAIS Session 4: Miscellaneous
(HMI, Planning, Problem Solving, Knowledge Representation)
3:30pm-3:45pm Producing coherent interactions in a tutoring system
Balaji Narasimhan, Computer Science Dept., USC
3:50pm-4:05pm AQUA: An intelligent UNIX advisor
Alex Quilici, Artificial Intelligence Laboratory, UCLA
4:10pm-4:25pm Errors in parsing problem descriptions
Eric Hestenes, Problem Solving Group, UCSD
4:30pm-4:45pm Constraint based problem solving
Mitchell Saywitz, Computer Science Dept., USC
4:50pm-5:05pm An approach to planning and scheduling for
robot assembly lines
Xiaodong Xia, Computer Science Dept., USC
5:10pm-5:25pm Changes of mind: Revision of "interpretation"
in episodic memory
Antoine Cornuejols, Computer Science Dept. UCLA
3:30pm-5:30pm SIGART Session 2
3:30pm-355pm Facilitating parametric analyses with AI
methodologies
N. T. Gladd, JAYCOR
4:00pm-4:25pm Computer Chess: Arguments and examples for a
knowledge-based approach
Danny Kopec, Dept. of Mathematical Sciences, SDSU
4:30pm-4:55pm Artificial Intelligence applications in
information retrieval
Mark Chignell, Dept. of Industrial & Systems Engineering, USC
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End of AIList Digest
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