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AIList Digest Volume 5 Issue 031
AIList Digest Monday, 2 Feb 1987 Volume 5 : Issue 31
Today's Topics:
Seminar - Logic Programming: The Japanese Were Right (TI) &
A Logic of Knowledge, Action, and Communication (Rutgers) &
An Intelligent Modeling Environment (Rutgers) &
Knowledge-Based Inductive Inference (Rutgers) &
Spatial Objects in Database Systems (IBM) &
Induction in Model-Based Systems (SU) &
Influence Diagrams (CMU) &
The ISIS Project (CMU)
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Date: WED, 10 oct 86 17:02:23 CDT
From: leff%smu@csnet-relay
Subject: Seminar - Logic Programming: The Japanese Were Right (TI)
TI Computer Science Center Lecture Series
LOGIC PROGRAMMING: A TOOL FOR THINKING
(OR WHY THE JAPANESE WERE RIGHT)
Dr. Leon Sterling
Case Western Reserve University
10:00 am, Friday, 6 February 1987
Semiconductor Building Main Auditorium
Logic programming, or the design, study and implementation of logic
programs, will be significant in software developments of the future.
Logic programming links the traditional uses of logic in program
specification and database query languages with newer uses of logic as
a knowledge representation language for artificial intelligence and as
a general-purpose programming language. A logic program is a set of
axioms, or truths about the world. A computation of a logic program
is the use of axioms to make logical deductions. This talk will
discuss the value of logic programming for artificial intelligence
applications. It will demonstrate how a well-written logic program
can clearly reflect the problem solving knowledge of a human expert.
Examples will be given of AI programs in Prolog, the most developed of
the languages based on logic programming.
BIOGRAPHY
Leon Sterling received his Ph.D. in computational group theory from
the Australian National University in 1981. After three years as a
research fellow in the Department of Artificial Intelligence at the
University of Edinburgh, and one year as the Dov Biegun Postdoctoral
Fellow in the Computer Science Department at the Weizmann Institute of
Science, he joined the faculty at Case Western Reserve University in
1985. In 1986 he became Associate Director of the Center for
Automation and Intelligent Systems Research at Case Western. He is
co-author, with Ehud Shapiro, of the recent textbook on Prolog,
"The Art of Prolog."
Visitors to TI should contact Dr. Bruce Flinchbaugh (214-995-0349) in
advance and meet at the north lobby of the SC Building by 9:45 am.
------------------------------
Date: 26 Jan 87 23:03:47 EST
From: KALANTARI@RED.RUTGERS.EDU
Subject: Seminar - A Logic of Knowledge, Action, and Communication
(Rutgers)
RUTGERS COMPUTER SCIENCE AND RUTCOR COLLOQUIUM SCHEDULE - SPRING 1987
Computer Science Department Colloquium :
DATE: Thursday, January 29, 1987
SPEAKER: Leora Morgenstern
AFFILIATION: New York University
TITLE: Foundations of a Logic of Knowledge, Action, and Communication
TIME: 9:50 (Coffee and Cookies will be setup at 9:30)
PLACE: Hill Center, Room 705
Most AI planners work on the assumption that they have complete knowledge
of their problem domain and situation, so that formulating a plan consists
of searching through some pre-packaged list of action operators for an
action sequence that achieves some desired goal. Real life planning rarely
works this way because we usually don't have enough information to map out
a detailed plan of action when we start out. Instead, we initially draw up
a sketchy plan and fill in details as we proceed and gain more exact
information about the world.
This talk will present a formalism that is expressive enough to describe this
flexible planning process. We begin by discussing the various requirements
that such a formalism must meet, and present a syntactic theory of knowledge
that meets these requirements. We discuss the paradoxes, such as the Knower
Paradox, that arise from syntactic treatments of knowledge, and propose a
solution to these paradoxes based on Kripke's solution to the Liar Paradox.
Next, we present a theory of action that is powerful enough to describe
partial plans and joint-effort plans. We demonstrate that we can integrate
this theory with an Austinian and Searlian theory of communicative acts.
Finally, we give solutions to the Knowledge Preconditions and Ignorant Agent
Problems as part of our integrated theory of planning.
The talk will include comparisons of our theory with other syntactic and
modal theories such as Konolige's and Moore's. We will demonstrate
that our theory is powerful enough to solve classes of problems that these
theories cannot handle.
------------------------------
Date: 26 Jan 87 23:03:47 EST
From: KALANTARI@RED.RUTGERS.EDU
Subject: Seminar - An Intelligent Modeling Environment (Rutgers)
RUTGERS COMPUTER SCIENCE AND RUTCOR COLLOQUIUM SCHEDULE - SPRING 1987
Computer Science Department Colloquium :
DATE: Friday, January 30, 1987
SPEAKER: Dr. Axel Lehmann
AFFILIATION: University of Karlsruhe, Institute fur Informatik IV,
F.R. Germany
TITLE: An Interactive, Intelligent and Integrated
Modeling Environment
TIME: 11:00 AM
PLACE: Hill Center, Room 423
This paper describes an approach for interactive assistance of users
in the different phases of modeling processes for analysis of system
dynamics, especially regarding performance, reliability or
cost-benefit predictions of computer systems. The conceptual approach
is based on the assumptions that more and more experts out of various
domains, who are not familiar in detail with modeling techniques,
require supporting tools available on their PC or their workstation
for quantitative analysis of system dynamics as a basis for making
decisions.
Considering this situation, the global objective of the INT3 project
and the research involved is to provide system experts as well as
users supporting tools for problem specification, for interactive
selection and (graphical) construction of a problem-adapted
(simulation) model, for validation, experiment planning and for
interpretation of modeling results. Beside a detailed concept, we have
already implemented some graphical supporting tools for semi-automatic
model synthesis and for result interpretation, as well as prototypes
of expert systems as advisory systems for the selection of
problem-adapted modeling methods and of efficient solution techniques.
This paper summarizes the goals and our basic concept of INT3, an
interactive and knowledge-based modelling environment, including
actual restrictions and its initial implementation on IBM PC/XT or AT.
In addition, it is focused on the description and stepwise solution of
a typical computer performance analysis problem and a manufacturing
problem by means of these supporting tools. These examples will
demonstrate the applicability of this concept and of INT3, its actual
state of realization, experience and problems, as well, and future
plans.
------------------------------
Date: 26 Jan 87 23:03:47 EST
From: KALANTARI@RED.RUTGERS.EDU
Subject: Seminar - Knowledge-Based Inductive Inference (Rutgers)
RUTGERS COMPUTER SCIENCE AND RUTCOR COLLOQUIUM SCHEDULE - SPRING 1987
Computer Science Department Colloquium :
DATE: Friday, January 30, 1987
SPEAKER: Thomas G. Dietterich
AFFILIATION: Department of Computer Science, Oregon State University
TITLE: KNOWLEDGE-BASED INDUCTIVE INFERENCE
(or EBG: The wrong view)
TIME: 2:50 (Coffee and Cookies will be setup at 2:30)
PLACE: HILL 705
Explanation-based generalization (EBG) began as a reaction to such weak
syntactic inductive inference methods as AQ11, ID3, and the version space
approach. However, in its pursuit of "justifiable generalization", EBG has
been shown to be too strong--the system already knows (in Newell's knowledge
level sense) the knowledge it is trying to "learn." Despite this
shortcoming, the methods employed in EBG suggest ways that knowledge might
be incorporated into the inductive learning process. Using examples from
Meta-DENDRAL (Buchanan, et al.), Sierra (VanLehn), and WYL (Flann), it will
be argued that the process of forming "explanations" in EBG should be viewed
as knowledge-based representation change. Each of these systems can be
viewed as shifting the learning problem to an "explanation space" where
syntactic inductive inference methods are then applied. The conclusion is
that the "knowledge revolution," which has transformed most of the rest of
AI, has finally begun to affect machine learning research.
RUTCOR Colloquium : (Discrete Mathematics Seminar)
--------------------------------------
DATE: Tuesday, January 27, 1987
SPEAKER: Professor P.P. Palfy
AFFILIATION: Dept. of Mathematics, University of Hawaii at Manoa
a
TITLE: Applications of finite simple groups in combinatorics
TIME: 1:30
PLACE: Hill Center, Room 705
------------------------------
Date: Wed, 28 Jan 87 17:30:16 PST
From: IBM Almaden Research Center Calendar <CALENDAR@IBM.COM>
Subject: Seminar - Spatial Objects in Database Systems (IBM)
IBM Almaden Research Center
650 Harry Road
San Jose, CA 95120-6099
February 2-6, 1987
ACCESS STRUCTURES FOR SPATIAL OBJECTS IN NONTRADITIONAL DATABASE SYSTEMS
H.-P. Kriegel, University Wuerzburg, West Germany
Computer Science Seminar Monday, Feb. 2 1:00 P.M. Room: B3-247
Database systems must offer storage and access structures for spatial
objects to meet the needs of nontraditional applications such as
computer-aided design and manufacturing (CAD/CAM), image processing
and geographic information processing. First, we will show that
access methods for spatial objects should be based on multidimensional
dynamic hashing schemes. However, even for uniform object
distributions, previous schemes of this type do not exhibit ideal
performance; for nonuniform object distributions which are common in
the above mentioned applications, the retrieval performance of all
known schemes is rather poor. In this talk, we will present new
schemes which exhibit practically optimal retrieval performance for
uniform and nonuniform object distributions. We will underline this
fact by the results of experimental runs with implementations of our
schemes.
Host: D. Ruland
Visitors, please arrive 15 minutes early. IBM's new Almaden Research
Center (ARC) is located adjacent to Santa Teresa County Park, between
Almaden Expressway and U.S. 101, about 10 miles south of Interstate
280. From U.S. 101, exit at Bernal Road, and follow Bernal Road west
past Santa Teresa Blvd. into the hills (ignoring the left turn for
Santa Teresa Park). Alternatively, follow Almaden Expressway to its
southern terminus, turn left onto Harry Road, then go right at the ARC
entrance (about a quarter of a mile later) and go up the hill. For
more detailed directions, please phone the ARC receptionist at (408)
927-1080.
------------------------------
Date: 29 Jan 1987 1206-PST (Thursday)
From: Valerie Ross <ross@pescadero.stanford.edu>
Subject: Seminar - Induction in Model-Based Systems (SU)
CS 500 Computer Science Colloquium
Feb. 3, 4:15 pm, Skilling Auditorium
THE PROVISION OF INDUCTION AS A PROBLEM SOLVING METHOD
IN MODEL BASED SYSTEMS
DAVID HARTZBAND, D.Sc.
Artificial Intelligence Technology Group
Digital Equipment Corporation, Hudson, MA
Much research in artificial intelligence and cognitive science has focused on
mental modeling and the mapping of mental models to machine systems. This is
especially critical in systems which provide inference capabilities in order to
enhance peoples' problem solving abilities. Such a system should present a
machine model that is homomorphic with a human perception of knowledge
representation and problem solving. An approach to the development of such a
model has allowed a model-theoretic approach to be taken toward machine
representation and problem solving. Considerable work done in psychology,
cognitive science and decision analysis in the past 20 years has indicated that
human problem solving methods are primarily comparative (that is analogic) and
proceed by successive refinement of comparisons among known and unknown
entities (e.g. Carbonell, 1985; Rummelhart and Abrahamson, 1973; Simon, 1985;
Tversky, 1977).
A series of algorithms has been developed to provide analogic (Hartzband et al.
1986) and symmetric comparative induction methods (Hartzband and Holly, in
preparation) in the context of the homomorphic machine model previously
referred to. These general methods can be combined with heuristics and
structural information in a specific domain to provide a powerful problem
solving paradigm which could enhance human problem solving capabilities.
This paper will:
a. describe the characteristics of this model-theoretic approach,
b. describe (in part) the model used in this work,
c. develop both the theory and algorithms for comparative induction in
this context, and
d. discuss the use of these inductive methods in the provision of effective
problem solving paradigms.
------------------------------
Date: 26 Jan 87 17:54:08 EST
From: Charles.Wiecha@isl1.ri.cmu.edu
Subject: Seminar - Influence Diagrams (CMU)
Influence Diagrams: Graphical Representations for Uncertainty
Ross D. Shachter
Department of Engineering-Economic Systems
Stanford University
Wednesday, January 28
2:30-4:00 PM
Porter Hall 223D
The influence diagram is a network for structuring bayesian decision analysis
problems. The nodes represent uncertain quantities, goals, and decisions, and
the arcs indicate probabilistic dependence and the observability of
information. The graphical heirarchy promotes discussion by emphasizing the
structure of a problem and the relationships among variables, while allowing
the details of assessment to be completed later. Because the components have a
basic mathematical interpretation, even a qualitative diagram has a precise
meaning. When the quantitative information is complete, the influence diagram
can be evaluated in a generalization of decision tree solving. Examples using
influence diagrams will be drawn from decision analysis, information theory,
dynamic programming, Kalman filtering, and expert systems. In the latter, we
ask the question "Why do probabilists insist on looking at everything
backwards?"
------------------------------
Date: 27 Jan 87 10:39:13 EST
From: Patty.Hodgson@isl1.ri.cmu.edu
Subject: Seminar - The ISIS Project (CMU)
AI SEMINAR
TOPIC: THE ISIS PROJECT: AN HISTORICAL PERSPECTIVE OR LESSONS LEARNED
AND RESEARCH RESULTS
SPEAKER: MARK S. FOX, CMU Robotics Institute
PLACE: Wean Hall 5409
DATE: Tuesday, January 27, 1987
TIME: 3:30 pm
ABSTRACT:
ISIS is a knowledge-based system designed to provide intelligent
support in the domain of job shop production management and control.
Job-shop scheduling is a "uncooperative" multi-agent (i.e., each
order is to be "optimized" separately) planning problem in which
activities must be selected, sequenced, and assigned resources and
time of execution. Resource contention is high, hence closely
coupling decisions. Search is combinatorially explosive; for
example, 85 orders moving through eight operations without
alternatives, with a single machine substitution for each and no
machine idle time has over 10@+[880] possible schedules. Many of
which may be discarded given knowledge of shop constraints. At
the core of ISIS is an approach to automatic scheduling that provides
a framework for incorporating the full range of real world
constraints that typically influence the decisions made by human
schedulers. This results in an ability to generate detailed schedules
for production that accurately reflect the current status of the shop
floor, and distinguishes ISIS from traditional scheduling systems
based on more restrictive management science models. ISIS is capable
of incrementally scheduling orders as they are received by the shop
as well as reactively rescheduling orders in response to unexpected
events (e.g. machine breakdowns) that might occur.
The construction of job shop schedules is a complex constraint-directed
activity influenced by such diverse factors as due date requirements, cost
restrictions, production levels, machine capabilities and substitutability,
alternative production processes, order characteristics, resource
requirements, and resource availability. The problem is a prime candidate
for application of AI technology, as human schedulers are overburdened by
its complexity and existing computer-based approaches provide little more
than a high level predictive capability. It also raises some interesting
research issues. Given the conflicting nature of the domain's constraints,
the problem differs from typical constraint satisfaction problems. One
cannot rely solely on propagation techniques to arrive at an acceptable
solution. Rather, constraints must be selectively relaxed in which case
the problem solving strategy becomes one of finding a solution that best
satisfies the constraints. This implies that constraints must serve to
discriminate among alternative hypotheses as well as to restrict the number
of hypotheses generated. Thus, the design of ISIS has focused on
o constructing a knowledge representation that captures the requisite
knowledge of the job shop environment and its constraints to support
constraint-directed search, and
o developing a search architecture capable of exploiting this
constraint knowledge to effectively control the combinatorics of
the underlying search space.
This presentation will provide an historical perspective on the development
of ISIS family of systems. It will focus on the evolution of its
representation of knowledge and search techniques. Performance data for
each version will be presented.
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End of AIList Digest
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