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AIList Digest Volume 4 Issue 270
AIList Digest Monday, 1 Dec 1986 Volume 4 : Issue 270
Today's Topics:
Course - AI in Design and Manufacturing (MIT),
Seminars - Computational Problems in Equational Theorem Proving (UPenn) &
LISP on a Reduced-Instruction-Set Processor (SU) &
RUM: Reasoning with Uncertainty (CMU) &
Graphical Access to an Expert System (UPenn) &
Disassembly Expert (CMU)
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Date: Sat, 29 Nov 86 14:15:29 EST
From: "Steven A. Swernofsky" <SASW@MX.LCS.MIT.EDU>
Subject: Course - AI in Design and Manufacturing (MIT)
From: Neena Lyall <LYALL at XX.LCS.MIT.EDU>
New Seminar Course Spring 1987
2.996 Advanced Topics in Mechanical Engineering (A); Section 2
ARTIFICIAL INTELLIGENCE IN
DESIGN & MANUFACTURING
Prerequisite: 1.00, 2.10, or 6.001
Units: 2-0-7
Date, time: Wednesday, 1-3 pm
Place: 37-212
Applications of artificial intelligence to selected domains of engineering.
Discussions will focus on the principles, strengths and limitations of existing
techniques as well as present and future applications. Topics of coverage
include: knowledge representation issues and techniques, logic programming,
expert systems, machine learning, and application areas. Format: class
discussions, midterm paper and final project.
For further information, contact Prof. S. H. Kim, x3-2249, Room 35-237.
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Date: Thu, 20 Nov 86 23:34 EST
From: Tim Finin <Tim@cis.upenn.edu>
Subject: Seminar - Computational Problems in Equational Theorem
Proving (UPenn)
CIS COLLOQUIUM
University of Pennsylvania
3pm November 15, 1986
216 Moore School
COMPUTATIONAL PROBLEMS IN EQUATIONAL THEOREM PROVING
Dr. Paliath Narendran
General Electric Research Laboratory
The area of Equational Reasoning has recently gained a lot of attention
and has been found to have applications in such diverse areas as program
synthesis and data base queries. Most of these applications are centered
around using the equations as "rewrite rules" and, in particular, using
the Knuth-Bendix completion procedure to generate a "complete" set of
such rewrite rules. The power of the completion procedure lies in the
fact that once a complete set of rewrite rules is obtained, we also have
a decision procedure for the equational theory. We discuss some of the
main computational problems involved in this area such as unification,
matching and sufficient-completeness testing and outline the decidability
and complexity results.
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Date: Mon, 24 Nov 86 10:44:46 PST
From: Peter Steenkiste <pas@mojave.stanford.edu>
Subject: Seminar - LISP on a Reduced-Instruction-Set Processor (SU)
Special Seminar: Ph.D. Orals
LISP on a Reduced-Instruction-Set Processor:
Characterization and Optimization
Peter Steenkiste
Computer Systems Laboratory
Department of Electrical Engineering
Stanford University
Abstract
As a result of advances in compiler technology, almost all programs
are written in high-level languages, and the effectiveness of a
computer architecture is determined by its suitability as a compiler
target. This central role of compilers in the use of computers has
led computer architects to study the implementation of high-level
language programs. This thesis presents profiling measurements for
a set of Portable Standard Lisp programs that were executed on the
MIPS-X reduced-instruction-set processor, examining what instructions
LISP uses at the assembly level, and how much time is spent on the
most common primitive LISP operations. This information makes it
possible to determine which operations are time critical and to
evaluate how well architectural features address these operations.
The second part of the thesis will discuss a number of optimizations
for LISP, concentrating on three areas: the implementation of the
tags used for runtime type checking, reducing the cost of procedure
calls, and inter-procedural register allocation. We look at methods
to implement tags, both with and without hardware support, and we
compare the performance of the different implementation strategies.
We show how the procedure call cost can be reduced by inlining small
procedures, and how inlining affects the miss rate in the MIPS-X
on-chip instruction cache. A simple register allocator uses inter-
procedural information to reduce the cost of saving and restoring
registers across procedure calls. We evaluate this register allocation
allocation scheme, and compare its performance with hardware register
windows.
Time: Monday, December 8, 1986, 4:15pm
Place: CIS Building, Room 101
Cookies will be served!
------------------------------
Date: 25 November 1986 0948-EST
From: Masaru Tomita@A.CS.CMU.EDU
Subject: Seminar - RUM: Reasoning with Uncertainty (CMU)
RUM: A Layered Architecture for Reasoning with Uncertainty
Piero P. Bonissone
General Electric Corporate Research and Development
P.O. Box 8, K1-5C32A, Schenectady, New York 12301
3:30pm, WeH5409
New reasoning techniques for dealing with uncertainty in Expert Systems
have been embedded in RUM, a Reasoning with Uncertainty Module. RUM is an
integrated software tool based on a frame system (KEE) that is implemented
in an object oriented language. RUM's capabilities are subdivided into
three layers: Representation, Inference, and Control.
The Representation layer is based on frame-like data structures that
capture the uncertainty information used in the inference layer and the
uncertainty meta-information used in the control layer. Linguistic
probabilities are used to describe lower and upper bounds of the certainty
measure attached to a Well Formed Formula (wff). The source and the
conditions under which the information was obtained represent the
non-numerical meta-information.
The Inference layer provides the uncertainty calculi to perform the
intersection, detachment, union, and pooling of the information. Five
uncertainty calculi, based on their underlying Triangular norms (T-norms),
are used in this layer.
The Control layer uses the meta-information to select the appropriate
calculus for each context and to resolve eventual ignorance or conflict in
the information. This layer enables the programmer to declaratively
express the local (context dependent) meta-knowledge that will substitute
the global assumptions traditionally used in uncertain reasoning.
RUM has been tested and validated in a sequence of experiments in naval
situation assessment (SA). These experiments consists in determining
report/track correlation, platform location, and platform typing. The
testbed environment for developing these experiments has been provided by
LOTTA, a symbolic simulator implemented in Zetalisp Flavors, the object
oriented language of the Lisp Machine. This simulator maintains
time-varying situations in a multi-player antagonistic game where players
must make decisions in light of uncertain and incomplete data. RUM has
been used to assist one of the LOTTA players to perform the SA task.
- - - - End forwarded message - - - -
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Date: Wed, 26 Nov 86 00:10 EST
From: Tim Finin <Tim@cis.upenn.edu>
Subject: Seminar - Graphical Access to an Expert System (UPenn)
COLLOQUIUM - UNIVERSITY of PENNSYLVANIA
3pm Tuesday, December 2, 1986
Room 216 Moore School
GRAPHICAL ACCESS TO AN EXPERT SYSTEM:
THE EVOLUTION OF THE ONCOCIN PROJECT
Ted Shortliffe
Visiting Professor of Computer and Information Science
University of Pennsylvania
and
Associate Professor of Medicine and Computer Science
Medical Computer Science Group
Knowledge Systems Laboratory
Stanford Medical School
The research goals of Stanford's Medical Computer Science group are directed
both toward the basic science of artificial intelligence and toward the
development of clinically useful consultation tools. Our approach has been
eclectic, drawing on fields such as decision analysis, interactive graphics,
and both qualitative and probabilistic simulation as well as AI. In this
presentation I will discuss ONCOCIN, an advice system designed to suggest
optimal therapy for patients undergoing cancer treatment, as well as to
assist in the data management tasks required to support research treatment
plans (protocols). A prototype version, developed in Interlisp and SAIL
on a DEC-20, was used between May 1981 and May 1985 by oncology faculty and
fellows in the Debbie Probst Oncology Day Care Center at the Stanford
University Medical Center. In recent years, however, we have spent much
of our time reimplementing ONCOCIN to run on Xerox 1100 series workstations
and to take advantage of the graphics environment provided on those
machines. The physician's interface has been redesigned to approximate the
appearance and functionality of the paper forms traditionally used for
recording patient status. The Lisp machine version of ONCOCIN was introduced
for use by Stanford physicians earlier this year.
In response to the need for an improved method for entering and maintaining
the rapidly expanding ONCOCIN protocol knowledge base, we have also developed
a graphical knowledge acquisition environment known as OPAL. This system
allows expert oncologists to directly enter their knowledge of protocol-
directed cancer therapy using graphics-based forms developed in the
Interlisp-D environment. The development of OPAL's graphical interface led
to a new understanding of the natural structure of knowledge in this domain.
ONCOCIN's knowledge representation was accordingly redesigned for the Lisp
machine environment. This has involved adopting an object-centered knowledge
base design which has provided an increase in the speed of the program while
providing more flexible access to system knowledge.
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Date: 26 Nov 86 22:29:56 EST
From: Sergio.Sedas@fas.ri.cmu.edu
Subject: Seminar - Disassembly Expert (CMU)
Master's Defense
Name: Sergio W. Sedas
Title:Disassembly Expert
Dept: ECE
Date: Dec. 3, 1986
Time: 2:00 DHA219 Engineering Design
Research Center (Demo)
2:30 DH1102 Chemical Engineering
Conference Room (Presentation)
An important part in a redesign for assembly expert is a module which will
autonomously disassemble mechanical objects. Although disassembly is a task
which is easily performed by human beings, it has been a very difficult task
for computers to perform. This paper describes an algorithm which mimics
the human experimental approach to disassemble mechanical assemblies. A
highlight in this approach is the ability to determine when a single part or
a group of parts (subassembly) must be removed.
We have divided the disassembly operation into two basic steps. The first
step selects a part to remove and identifies which parts whose connections
can not be severed must be removed with it. The second step, incorporated
in a path planner, attempts to remove the subassembly. During removal,
obstacles may be added or excluded from the subassembly.
A second contribution is the use of multiple representations for
problem solving. A number of geometric, connectivity and facilities models
are used simultaneously in both steps of the disassembly.
This algorithm has successfully disassembled a flashlight and a piston. By
modifying the objective we've managed to remove an object from a closed
drawer and a chosen part from a within a flashlight assembly.
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End of AIList Digest
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