Subject: AIList Digest V4 #65

From: vtcs1::in% ([email-masked])
Date: Mon, 31 Mar 86 06:43:24 est

AIList Digest            Monday, 31 Mar 1986       Volume 4 : Issue 65

Today's Topics:
  Seminars - Concurrent Processing with Result Sharing (SMU) &
    Planning by Procedural Inference (SRI) &
    Processes, Events, and the Frame Problem (CSLI) &
    Inexact Reasoning using Graphs (MIT),
  Conference - 1st Australian Applied AI Congress &
    Knowledge Representation Tools for Expert Systems &
    AI Impacts Workshop

----------------------------------------------------------------------

Date: WED, 10 JAN 84 17:02:23 CDT
From: [email-masked]
Subject: Seminar - Concurrent Processing with Result Sharing (SMU)

Concurrent Processing with Result Sharing: A Unified View
of Efficient Computaitons

Speaker: S. Krishnaprasad, Southern Methodist University
         (kp%smu@csnet-relay convex!smu!kp)
Location: 315SIC, Southern Methodist University
Time 2:00 PM
Date: April 3, 1986

Abstract

A major aspect of efficient problem solving is to avoid
reduandant recomputations,  This talk identifies the need for and ways
to incorporate both problem structure and problem dynamics, in the context
of concurrent processing, for fast and efficient problem solving.

The notion of horizontal locality and vertical locality are introduced
to capture the essence of problem dynamics.  Algorithms for decomposition
under dynamics are discussed for a special class of computations.

A new model of problem solving called Concurrent Processing with Result
Sharing (CPRS) is defined along with measures that characterize efficiency
of problem solving.  In a general setting, this model is related to the notion
of working set under concurrent processing environment.  A simulation
strategy is presented to prove the usefulness of CPRS model when multiple
concurrent computations compete for limited computational resources.

------------------------------

Date: Wed 26 Mar 86 18:30:50-PST
From: [email-masked]
Subject: Seminar - Planning by Procedural Inference (SRI)

                     PLANNING BY PROCEDURAL INFERENCE

                         Dan Carnese (CARNESE@SRI-KL)
                 AI Lab, Schlumberger Palo Alto Research (SPAR)

                   11:00 AM, MONDAY, March 31
         SRI International, Building E, Room EJ228 (new conference room)

The standard approach to plan construction involves applying a general planning
algorithm to a representation of a problem to be solved.  This approach will
fail on a given problem when the search space explored by the algorithm is too
large.  If this occurs, the only alternatives are to re-encode the problem or
to improve the general algorithm.

In this talk, I'll describe an alternative approach where control of the
planning process is provided by a procedure which constructs proofs from
premises characterizing the domain.  This approach allows arbitrary
procedures to be used for control, while retaining the desirable property
that unsound inferences cannot be made.

The technique will be illustrated with examples from the domain of
computer-aided manufacturing.

------------------------------

Date: 25 Mar 86  1134 PST
From: Vladimir Lifschitz <[email-masked]>
Subject: Seminar - Processes, Events, and the Frame Problem (CSLI)

                   PROCESSES, EVENTS, AND THE FRAME PROBLEM

                             Michael Georgeff

                Artificial Intelligence Center, SRI International
                                  and
                Center for the Study of Language and Information
                           Stanford University

                    Thursday, March 27, 3pm  (NB: New time!)
                                 MJH 252

In this talk we will consider various models of actions and events
suited to reasoning about multiple agents situated in dynamic
environments.  We will also show how the notion of process is
essential in multiagent domains, and contrast this with most
approaches in AI that are based solely on the allowable behaviors of
agents.  We will then consider how we might go about specifying the
properties of events and processes, and whether or not such
specifications require nonmonotonicity or circumscription.  Finally,
we will examine various views of the frame problem and see to what
extent some of the major difficulties can be overcome.

------------------------------

Date: Fri 28 Mar 86 11:38-EST
From: "Lisa F. Melcher" <[email-masked]>
Subject: Seminar - Inexact Reasoning using Graphs (MIT)

                          Wednesday, April 16, 1986
                          3:45 p.m.....Refreshments
                            4:00 p.m.....Lecture
                                 NE43 - 512A

                                 JUDEA PEARL

                         Computer Science Department
                                    UCLA

                      "Inexact Reasoning Using Graphs"

Probability theory is shunned by most researchers in Artifical Intelligence.
New calculi, claimed to better represent human reasoning under uncertainty,
are being invented and reinvented at an ever-increasing rate.  A major reason
for the emergence of this curious episode has been the objective of making
reasoning systems TRANSPARENT i.e., capable of producing PSYCHOLOGICALLY
MEANINGFUL explanations for the intermediate steps used in deriving the
conclusions.

While traditional probability theory, admittedly, has erected cultural
barriers against meeting this requirement, we shall show that these barriers
are superficial, and can be eliminated with the use of DEPENDENCY GRAPHS.
The nodes in these graphs represent propositions (or variables), and the arcs
represent causal dependencies among conceptually-related propositions.  We
further argue that the basic steps invoked while people query and update
their knowledge correspond to mental tracings of preestablished links in such
graphs, and it is the degree to which an explanation mirrors these tracings
that determines whether it is considered "psychologically meaningful".

The first part of the talk will examine what properties of probabilistic
models can be captured by graphical representations, and will compare the
properties of two such representations:  Markov Networks and Bayes Networks.

The second part will introduce a calculus for performing inferences in Bayes
Networks.  The impact of each new evidence is viewed as a perturbation that
propagates through the network via local communication among neighboring
concepts.  We show that such autonomous propagation mechanism leads to
flexible control strategies and sound explanations, that it supports both
predictive and diagnostic inferences, that it is guaranteed to converge in
time proportional to the network's diameter, and that every proposition is
eventually accorded a measure of belief consistent with the axioms of
probability theory.

In conclusion, we will show that the current trend of abandoning probability
theory is grossly premature--taking graph propagation as the basis for
probabilistic reasoning satisfies most computational requirements for
managing uncertainties in reasoning systems and, simultaneously, it exhibits
epistemological features unavailable in any competing formalism.

              Sponsored by TOC, Laboratory for Computer Science
                             Ronald Rivest, Host

------------------------------

Date: Wed, 26 Mar 86 15:20:02 est
From: [email-masked] (Brian J. Garner)
Subject: Conference - 1st Australian Applied AI Congress

Call for Papers:

   1
  11  st
 111  AUSTRALIAN
  11  ARTIFICIAL
  11  INTELLIGENCE
  11  CONGRESS
  11
 1111  Melbourne, November 18-20, 1986

                      CALL FOR PAPERS

Abstract of papers to be selected for presentation to the 1st Australian
Artificial Intelligence Congress are now invited. The three-part program
comprises:

i)   AI in Education

     - Intelligent tutors
     - Computer-managed learning
     - Course developers environment
     - Learning models
     - Course authoring software

ii)  Expert System Applications

     - Deductive databases
     - Conceptual schema
     - Expert system shells (applications and limitations)
     - Interactive knowledge base systems
     - Knowledge engineering environments
     - Automated knowledge acquisition

iii) Office Knowledge Bases

     - Document classification and retrieval
     - Publishing systems
     - Knowledge source systems
     - Decision support systems
     - Office information systems

Tutorial presenters are also sought. Specialists are required
in the areas of:

     - Common loops
     - Natural language processing
     - Inference engines
     - Building knowledge databases
     - Search strategies
     - Heuristics for AI solving

Format:

ACSnet address: brian!aragorn.oz
CSNET address:  [email-masked]
UUCP address:   seismo!munnari!aragorn.oz!brian
                decvax!mulga!aragorn.oz!brian
ARPA address:   [email-masked]
                decvax!mulga!aragorn.oz!brian@Berkeley

PC diskette to Division of Computing and Mathematics, Deakin University,
Victoria 3217, Australia. Attn. Dr. Brian Garner.

DEADLINES: All submissions by May 16, 1986. Notification by June 30.

Inquiries: Stephen Moore, Director, 1AAIC86, tel: (02)439-5133.

------------------------------

Date: 26 Mar 86 21:06:04 GMT
From: [email-masked]
      eley.edu  (Hubert Broze)
Subject: Conference - Knowledge Representation Tools for Expert
         Systems

=================================================================

Conference announcement :

 "KNOWLEDGE REPRESENTATION TOOLS FOR EXPERT SYSTEMS"

Louvain-la-Neuve (Belgium), April 21st, 1986.
Place des Sciences, Auditorium A01

Organized jointly by :
  L'Unite d'Informatique de l'Universite Catholique de Louvain
  The Belgian Association for Artificial Intelligence (BAAI)
  The ACM Student Chapter of Louvain-la-Neuve.

                   PROGRAM :

 9 H 30 Participants welcome & Opening of the industrial exhibition
 10 H 00 - 11 H 00 F. ARLABOSSE (Framentec, Paris) :
                      "The representation of Knowledge : the industrial phase"
 11 H 15 - 12 H 15 J. FERBER (LRI-Univ. Paris-Sud)
                       "Reflections in object-oriented languages"
 12 H 15 - 14 H 30  lunch
 14 H 30 - 15 H 30  P.Y.  GLOESS (CNRS & Graphael) :
                      "OBLOGIS : une implantation orientee objet de la logique
                      de Prolog et liaison de cette logique avec des objets"
 15 H 45 - 16 H 45 R. VENKEN (Bim)
                       "BIM-Prolog : A new implementation of Prolog"
 16 H 45 - 18 H 00 Cocktail (kindly offered by intersem-Sligos)

             _____________________________

  During the whole day, an industrial exhibition will be held with the
  participation of Apollo Computer, BIM (Sun), CPP (KES), Ferranti (ART),
  IBM, Rank Xerox, Symbolics, Tektronix, Texas Instrument etc.

  Participation to the meeting is FREE OF CHARGE

  Additional Information may be obtained from :

   E. Gregoire, Unite Info, Place Ste Barbe, 2, B1348 Louvain-la-Neuve,
                Belgium.
                Tel : + 32 10 43 24 15
                UUCP : {prlb2,vmucnam}!lln-cs!eg

------------------------------

Date: 26 Mar 86 15:54:10 GMT
From: [email-masked]  (Ted Hermann)
Subject: Conference - AI Impacts Workshop

ARTIFICIAL INTELLIGENCE IMPACTS

            WORKSHOP

          PRESENTED BY

  AMERICAN COMPUTER TECHNOLOGIES, INC.

          June 4-6, 1986

        FAA Technical Center
   Atlantic City Airpot. New Jersey

    American Computer Technologies, Inc.
      237 Lancaster Avenue, Suite 255
          Devon, PA 19333

For Information call Carol Ward, A.C.T., Inc. (215) 687-4148 or write to above
address.

WORKSHOP OBJECTIVES:

describe the business opportunities of Artificial Intelligence technologies

examine the strengths and limitations of these technologies

identify current AI products and services on the market and their potential
applications

analyze companies at the fore-front of the AI market and those expected to
enter soon

analyze current and emerging international markets for AI technology

clarify the business growth opportunities and threats associated with AI
technology

provide an understanding of the potential impact Artificial Intelligence
will have on business

identify promising new frontiers in AI research with applications to the
commercial and military sectors

analyze software and hardware needs for emerging AI markets and assess the
impacts on U.S. business

WORKSHOP TOPICS:

I. Introduction

Opening Remarks
Creating Computers that Think
Emerging International AI Markets

II. Assessment of AI Opportunities

Expert Systems
Movement in Space
Vision
Natural Language Comprehension
Learning

III. Analyses of AI Products and Services

Current/Future Software Packages
Stand-Alone AI Hardware
AI in Personal Computers
Embedded AI Systems
Knowledge Expert Services

IV. Assessment of Competitive Issues

Strategic Computing/Defense Initiatives
New Japanese MITI-ICOT Perspectives
Western European Consortia
Emerging Eastern Bloc Cooperation
Established AI Firms
Emerging AI Ventures
Joint Ventures and R&D Partnerships
Mergers and Acquisitions

IV. Strategic Risks and Constraints

Financial Risks
Social/Legal Risks
Technological Constraints
Market Constraints

VI. Analyses of End-User Applications

Direct Military Applications
Software Engineering Applications
Non-Military Government Applications
Commercial Applications

VII. Analyses of Global Trends

Fifth-Generation Machine Architectures
Emerging Fourth-Generation Languages
Other Major Technological Thrusts
Near-Real Time Systems
Economic impact of International AI Markets
Growth of AI products and services

WORKSHOP LEADERS

T. S. Hermann, Ph.D.,  President of American Computer Technologies,
Inc., has served as the Manager, Plans and Programs at Burroughs' Paoli
Research Center; Director of R&D at Analytics, Inc.; Sr. VP Technology of Sun
Company; President of Franklin Research Center; and President of Mellon
Institute, Carnegie-Mellon University.

Ronald L. Krutz, Ph.D., Director, Computer Engineering Center, Carnegie
Mellon University.

Lewis J. Petrovic, Ph.D., President, Resource Engineering, Inc.

B.K. Wesley Copeland, MBA, President, International Science &
Technology

G. Richard Patton, Ph.D., Ex.VP, Resource Assessment, Inc., and Faculty
Member, Graduate School of Business, University of Pittsburgh

WHO SHOULD ATTEND?

The ARTIFICIAL INTELLIGENCE IMPACTS workshop has been established primarily
to address the needs of business persons who are interested in or are
responsible for Governmental Program planning, marketing and manufacturing.

WHAT ARE THE MAJOR ISSUES?

This workshop will assess major AI product opportunities, explore fundamental
trends and market concepts of Artificial Intelligence and will go beyond
conventional strategic assertions within an International business context.

WHAT ARE THE BENEFITS?

THE WORKSHOP will answer the hard business questions of Artificial
Intelligence.  Participants will learn of the emerging AI business growth
opportunities; become aware of the key players and their product strategies;
analyze the growing international markets and potential competitors; acquire
forecasts of important technological impacts and thrusts; and will scutinize
the constraints and risks of the AI products.

For Information call Carol Ward, A.C.T., Inc. (215) 687-4148 or write to above
address.

------------------------------

End of AIList Digest
********************