Subject: From: AIList Moderator Kenneth Laws <AIList-REQUEST%sri-ai.arpa@CSNET-RELAY>

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Date: Fri, 20 Dec 85 02:19:16 est

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Subject: AIList Digest   V3 #173
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AIList Digest            Tuesday, 19 Nov 1985     Volume 3 : Issue 173

Today's Topics:
  Seminars - Adaptive Planning (UCB) &
    Sparse Distributed Memory (BBN) &
    Explanation-Based Learning (BBN) &
    Learning Search Control Knowledge (CMU) &
    MED2 Diagnostic Expert (MIT) &
    Truth Maintenance, Multiple Worlds in KEE (SU) &
    Representation of Natural Form (SU) &
    Setting Tables and Illustrations With Style (CSLI),
  Course - Connectionist Models (CMU)

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Date: Thu, 14 Nov 85 16:57:53 PST
From: [email-masked] (Cognitive Science Program)
Subject: Seminar - Adaptive Planning (UCB)

                      BERKELEY COGNITIVE SCIENCE PROGRAM
                                      Fall 1985
                        Cognitive Science Seminar - IDS 237A

                         Tuesday, November 19, 11:00 - 12:30
                        240 Bechtel Engineering Center
                 Discussion: 12:30 - 1:30 in 200 Building T-4

                 ``Adaptive Planning is Commonsense Planning''
                               Richard Alterman
                       Computer Science Division, U.C.B.

        A  characteristic  of  commonsense  planning  is  that  it   is
        knowledge  intensive.   For  most  mundane  sorts of situations
        human planners have access to, and are capable  of  exploiting,
        large quantities of knowledge.  Commonsense planners re-use old
        plans under their normal circumstances.  Moreover,  commonsense
        planners  are  capable  of  refitting  old  plans to novel cir-
        cumstances.  A commonsense planner can plan about a wide  range
        of phenomena, not so much because his/her depth of knowledge is
        consistent throughout that range, but because s/he  can  re-fit
        old plans to novel contexts.

             This talk is about an  approach  to  commonsense  planning
        called adaptive planning. An adaptive planner plans by exploit-
        ing planning knowledge in a manner that delays the reduction of
        commonsense planning to problem-solving.    Key elements in the
        theory of adaptive planning are  its  treatment  of  background
        knowledge  and  the  introduction  of  a  notion of planning by
        situation matching.  This talk will describe adaptive  planning
        as  it  applies to a number of commonsense planning situations,
        including: riding the NYC subway, trading  books,  transferring
        planes at JFK airport, and driving a rented car.

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

Date: 14 Nov 1985 11:48-EST
From: BGOODMAN at BBNG.ARPA
Subject: Seminar - Sparse Distributed Memory (BBN)

           [Forwarded from the MIT bboard by SASW@MIT-MC.]

                    BBN Labs SDP AI Seminar

Speaker:  Dr. Michael R. Raugh
          Research Institute for Advanced Computer Science
          NASA Ames Research Center

Title:    Kanerva's Sparse Distributed Memory: A RIACS Project

Date:     Friday, November 22nd, 2:00pm

Location: Main Seminar Room (2nd floor)
          Bolt Beranek and Newman Inc.
          50 Moulton Street
          Cambridge, MA.

  An exciting new concept in which information is stored in a large number
of neighboring addresses determined by "content," produces a memory
that retrieves causal relationships as well as sequences of episodes and
is sensitive to similarity.  It is also forgetful and reinforcable:  a
memory much like yours and mine.

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

Date: 14 Nov 1985 11:48-EST
From: BGOODMAN at BBNG.ARPA
Subject: Seminar - Explanation-Based Learning (BBN)

           [Forwarded from the MIT bboard by SASW@MIT-MC.]

                    BBN Labs SDP AI Seminar

Speaker:  Professor Gerald DeJong
          Coordinated Science Laboratory
          University of Illinois at Urbana-Champaign

Title:    Explanation Based Learning

Date:     Monday, November 25th, 10:30am

Location: 2nd Floor Large Conference room
          BBN Laboratories Inc.
          10 Moulton Street
          Cambridge, MA.

    Machine learning is one of the most  important  current
areas  of artificial intelligence.  With the trend away from
"weak methods" and toward a more knowledge intensive approach
to  intelligence,  the  lack of knowledge in an AI system becomes
one of the most serious limitations.

    This talk advances a technique called explanation based
learning.   It  is  a  method  of learning from observation.
Basically, it involves endowing  a  system  with  sufficient
knowledge  so  that  intelligent planning behavior of others
can be recognized.  Once recognized, these observed plans are
generalized as far as possible while preserving the underlying
explanation of their success.  The approach supports one-trial
learning.  A new general concept can be acquired from an observation
of just one observed example.  The  approach  has  been  applied
to  three  diverse areas:  natural language processing, robot
task planning, and proof of propositional calculus theorems.  The
approach holds promise for solving the knowledge collection
bottleneck in the construction of current knowledge-based systems.

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

Date: 14 Nov 85 23:39:59 EST
From: [email-masked]
Subject: Seminar - Learning Search Control Knowledge (CMU)

           [Forwarded from the CMU bboard by Laws@SRI-AI.]

On Wednesday, November 20, at 12:00 I will present my thesis
proposal in 5409.  My thesis is concerned with the use of
explanation-based generalization in the PRODIGY system, a learning
apprentice that (among other things) acquires search control rules.
The title is: "Analytic Techniques for Learning Search Control Knowledge".
Copies are in the lounge.

                        ABSTRACT

Compression analysis, the subject of the proposed thesis, is a method for
analyzing search spaces to produce effective search control rules.
As with previous explanation-based learning techniques, an example problem
focuses the analysis process so that the entire search space need not
be analyzed.  The key idea behind compression analysis is that
many alternative explanations can be produced to justify a search control
decision; therefore it is appropriate to search for an explanation that
produces the most effective generalized control rule.  In practice this is
achieved by proposing an initial explanation which is then improved
using a set of heuristic transformation strategies.

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

Date: Sun, 17 Nov 85 16:28:06 EST
From: "Steven A. Swernofsky" <[email-masked]>
Subject: Seminar - MED2 Diagnostic Expert (MIT)

Wednesday  20, November  4: 00pm (4:15 Refreshments) Room: NE43-512A

"MED2: An Expert System Shell for Diagnosis and Therapy in Complex Domains"

                   Frank Puppe
               Kaiserlautern University
                   Germany

Concentrating on the medical domain, MED2 is a shell combining a wide
variety of important aspects of clinical reasoning.  It's
"Working-Memory" control structure involves investigating a set of
hypotheses simultaneously, avoiding the shortcomings of focussing on
the top-hypothesis only.  This concept allows using differential
diagnosis techniques and exploiting relationships among patho-concepts
in an efficient manner.  Other interesting features of MED2 include
separation of database and diagnostic reasoning, temporal reasoning,
and belief revision.

HOST: Prof. Peter Szolovits

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

Date: Mon 18 Nov 85 08:32:01-PST
From: Anne Richardson <[email-masked]>
Subject: Seminar - Truth Maintenance, Multiple Worlds in KEE (SU)

DAY         December 3, 1985
EVENT       Computer Science Colloquium
PLACE       Skilling Auditorium
TIME        4:15
TITLE       "Truth Maintenance and Multiple Worlds in KEE"
PERSON      Paul Morris, Robert Nado, Richard Fikes
FROM        IntelliCorp

              TRUTH, MAINTENANCE AND MULTIPLE WORLDS IN KEE

We describe the integration of an assumption-based truth maintenance
system (ATMS) into the frame-based representation facilities of the
KEE system, and the use of the ATMS to implement a multiple-world
context graph system for KEE.  Integration into the frame system
involves associating with potential slot values ATMS nodes that are
used to determine in which worlds (contexts) the slot values are
believed.  Built-in inferences provided by the frame system, such as
inheritance and the checking of value class and cardinality
constraints, are recorded, when needed, as explicit justifications in
the ATMS.  In addition, the default reasoning capabilities of KEE have
been refined and extended to take advantage of the ATMS.  Tradeoffs in
the integration between flexibility of use and run-time efficiency are
examined.  We describe the multiple-world context graph system with
particular attention to an interpretation of the graph as a network of
actions.  In this framework, the semantics of graph merges are
investigated and restrictions to ensure valid action sequences are
discussed.

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

Date: Mon 18 Nov 85 08:29:15-PST
From: Anne Richardson <[email-masked]>
Subject: Seminar - Representation of Natural Form (SU)

DAY         November 19, 1985
EVENT       Computer Science Colloquium
PLACE       Skilling Auditorium
TIME        4:15
TITLE       Perceptual Organization and the Representation of Natural Form
PERSON      Alex P. Pentland
FROM        AI Center, SRI Int'l and CSLI, Stanford

  PERCEPTUAL ORGANIZATION AND THE REPRESENTATION OF NATURAL FORM

To understand both perception and commonsense reasoning we need a
representation that captures important physical regularities and
that correctly describes the people's perceptual organization of
the stimulus.  Unfortunately, the current representations were
originally developed for other purposes (e.g., physics, engineering)
and are therefore often unsuitable.

We have developed a new representation and used it to make
accurate descriptions of an extensive variety of natural forms
including people, mountains, clouds and trees.  The descriptions
are amazingly compact.  The approach of this representation is to
describe scene structure in a manner similar to people's notion
of ``a part,'' using descriptions that reflect a possible
formative history of the object, e.g., how the object might have
been constructed from lumps of clay.

For this representation to be useful it must be possible to
recover such descriptions from image data; we show that the
primitive elements of such descriptions may be recovered in an
overconstrained and therefore reliable manner.  An interactive
``real-time'' 3-D graphics modeling system based on this
representation will be shown, together with short animated
sequences demonstrating the descriptive power of the
representation.

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

Date: Mon 18 Nov 85 11:46:58-PST
From: Fred Lakin <[email-masked]>
Subject: Seminar - Setting Tables and Illustrations With Style (CSLI)

Pixels and Predicates:

        SETTING TABLES AND ILLUSTRATIONS WITH STYLE

Who:            Rick Beach, Xerox PARC
Where:          CSLI trailers
When:           1:00pm - Wednesday, November 20, 1985

Abstract:

Two difficult examples of incorporating complex information within
electronic documents are illustrations and tables.  The notion of style,
a way of maintaining consistency, helps manage the complexities of
formatting both tables and illustrations.  The concept of graphical
style extends document style to illustrations.  Observing that graphical
style does not adequately deal with the layout of information leads to
the study of formatting tabular material.  A grid system for describing
the arrangement of information in a table, and a constraint solver for
determining the layout of the table are key components of this research.
These ideas appear to extend to formatting other complex material,
including mathematical typesetting and page layout.  Several typographic
issues for illustrations and tables will be highlighted during the talk.

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

Date: 18 Nov 85 23:29 EST
From: [email-masked]
Subject: Course - Connectionist Models (CMU)

                    CONNECTIONIST MODELS:  A SUMMER SCHOOL

                       Sponsored by the Sloan Foundation

ORGANIZERS:  Geoffrey Hinton (Carnegie-Mellon University)
             Terrence Sejnowski (The Johns Hopkins University)
             David Touretzky (Carnegie-Mellon University)

DATE:  June 20 through 29, 1986

PLACE:  Carnegie-Mellon University, Pittsburgh, Pennsylvania

PURPOSE  OF  THE  PROGRAM:   The purpose of the summer school is to familiarize
young researchers with current techniques in the area of  connectionist  models
of  intelligence.    This  includes search procedures, learning procedures, and
methods  for  representing  knowledge  in  massively   parallel   networks   of
neuron-like  units.    Application  areas  include  vision, speech, associative
memory, natural language and motor control.

FACULTY:  There will be six full time Tutors plus several Guest Lecturers.

    Tutors:                             Guest Lecturers:
    James Anderson, Brown University    Jerome Feldman, U. of Rochester
    Dana Ballard, U. of Rochester       Christof Koch, MIT
    Andrew Barto, U. Mass. Amherst      David Rumelhart, UCSD
    Geoffrey Hinton, CMU                David Touretzky, CMU
    James McClelland, CMU               others to be announced
    Terrence Sejnowski, Johns Hopkins

WHO MAY ATTEND:  Participation is limited to graduate students and recent PhD's
who  are or will be working on connectionist models.  About 40 students will be
accepted.   Persons who have already completed a Ph.D. degree must have done so
no earlier than January 1985 to be eligible to attend.

EXPENSES:   There is no tuition charge.  Funding from the Sloan Foundation will
provide dormitory accommodations and breakfast and  lunch  for  each  attendee,
plus reimbursement for a substantial portion of travel expenses.

HOW  TO  APPLY:  By March 1, 1986, send your curriculum vitae and a copy of one
relevant paper, technical  report,  or  research  proposal  to:    Dr.    David
Touretzky, Computer Science Department, Carnegie-Mellon University, Pittsburgh,
PA, 15213.  Applicants will be notified of acceptance by April 15, 1986.

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

End of AIList Digest
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