Subject: From: AIList Moderator Kenneth Laws <AIList-REQUEST@SRI-AI>

From: [email-masked]
Date: Sat, 22 Jun 85 00:32:38 edt

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Subject: AIList Digest   V3 #80
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AIList Digest            Friday, 21 Jun 1985       Volume 3 : Issue 80

Today's Topics:
  Seminars - A General Machine-Learning Mechanism (GM) &
    Distributed Decision Procedures (IBM-SJ) &
    Organisms' Internal Models (CSLI) &
    Design Expert Systems (CMU) &
    Unification of Logic, Function and Frames (MIT) &
    Automatic Example Generation (UTexas) &
    Qualitative Process Theory (CSLI) &
    Architectures for Logic Programming (GE) &
    Reasoning about Programs via Constraints (MIT) &
    Planning as Debugging (SRI)

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

Date: Wed, 19 Jun 85 15:47 EST
From: "S. Holland" <[email-masked]>
Subject: Seminar - A General Machine-Learning Mechanism (GM)

             Towards a General Machine-Learning Mechanism

                        Paul Rosenbloom
                      Stanford University

                Thursday, June 27, 1985, 10:00 a.m.
                General Motors Research Laboratories
                   Computer Science Department
                        Warren, Michigan

Machine learning is the process by which a computer can bring about
improvements in its own performance.  A general machine-learning mechanism
is a single mechanism that can bring about a wide variety of performance
improvements (ultimately all required types).  In this talk I will present
some recent progress in building such a mechanism.  This work shows that the
combination of a simple learning mechanism (chunking) with a sophisticated
problem-solver (SOAR) can yield: (1) practice speed-ups, (2) transfer of
learning between related tasks, (3) strategy acquisition, (4) automatic
knowledge-acquisition, and (5) the learning of general macro-operators of
the type used by Korf (1983) to solve Rubik's cube.  These types of learning
are demonstrated for traditional search-based tasks, such as tic-tac-toe and
the eight puzzle, and for R1-SOAR (a reformulation of a portion of the R1
expert system in SOAR).

This work has been pursued in collaboration with John Laird (Xerox PARC) and
Allen Newell (Carnegie-Mellon University).

-Steve Holland

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

Date: Wed, 19 Jun 85 16:25:22 PDT
From: IBM San Jose Research Laboratory Calendar
      <[email-masked]>
Reply-to: IBM-SJ Calendar <[email-masked]>
Subject: Seminar - Distributed Decision Procedures (IBM-SJ)

         [Excerpted from the IBM-SJ Calendar by Laws@SRI-AI.]

                      IBM San Jose Research Lab
                           5600 Cottle Road
                         San Jose, CA 95193

  Tues., June 25 Computer Science Seminar
  11:15 A.M.  DECISION PROCEDURES
  Aud. A      Distributed artificial intelligence is the study of
            how a group of individual intelligent agents can
            combine to solve a difficult global problem.  This
            talk discusses in very general terms the problems of
            achieving this global goal by considering simpler,
            local subproblems; we drop the usual requirement that
            the agents working on the subproblems do not
            interact.  We are led to a single assumption, which
            we call common rationality, that is provably optimal
            (in a formal sense) and which enables us to
            characterize precisely the communication needs of the
            participants in multi-agent interactions.  An example
            of a distributed computation using these ideas is presented.

            M. Ginsberg, Stanford University
            Host:  J. Halpern (HALPERN@IBM-SJ)

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

Date: Wed 19 Jun 85 17:02:36-PDT
From: Emma Pease <[email-masked]>
Subject: Seminar - Organisms' Internal Models (CSLI)

         [Excerpted from the CSLI Newsletter by Laws@SRI-AI.]

                    *NEXT* THURSDAY, June 27, 1985

   2:15 p.m.            CSLI Seminar
     Redwood Hall       ``An Organism and Its Internal Model of the World''
     Room G-19          Pentti Kanerva, CSLI
                        Discussion led by Alex Pentland

                     ABSTRACT OF NEXT WEEK'S SEMINAR
           ``An Organism and Its Internal Model of the World''

      There is a glaring disparity in how children and computers learn
   things.  By and large, children are not instructed explicitly but
   learn by observation, imitation, and trial and error.  What kind of
   computer architecture would allow a machine to learn the way children
   do?
      In the model I have been studying, an organism is coupled to the
   world by its sensors and effectors.  The organism's mind-ware consists
   of a relatively small focus and a large memory.  The sensors feed
   information into the focus, the effectors are driven from the focus,
   the memory is addressed by the contents of the focus, the contents of
   the focus are stored in memory, and the memory feeds information into
   the focus.  The contents of the focus at a moment account for the
   subjective experience of the organism at that moment.
      The function of the memory is to store a model of the world for
   later reference.  The memory is sensitive to similarity in that
   approximate retrieval cues can be used to retrieve exact information.
   It is dynamic in that the present situation (its encoding) brings to
   focus the consequences of similar past situations.  The model sheds
   light on the frame problem of robotics, and it appears that a robot
   built according to this principle would learn by trial and error and
   would be able to plan actions and to perform planned sequences of
   actions.
     Reading: ``Parallel Structures in Human and Computer Memory,''
   available from Susi Parker at the Ventura Hall receptionist desk and
   on line as <PKANERVA>COGNITIVA.PAPER at SU-CSLI.ARPA.
                                                     --Pentti Kanerva

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

Date: 20 Jun 85 11:16:01 EDT
From: Mary.Lou.Maher@CMU-RI-CIVE
Subject: Seminar - Design Expert Systems (CMU)

            DESIGN RESEARCH CENTER BI-WEEKLY SEMINAR SERIES

                 COPS - A Concurrent Production System

                                BY
                     Luiz Alberto Villaca Leao
        Department Of Electrical and Computer Engineering

  Wednesday, June 26 at 1:30 pm in the Adamson Wing, Baker Hall

Existing tools for writing expert systems are most helpful when one wants to
emulate a single human expert working alone and without the aid of large
number crunching programs. Few engineering problems fit this template. Rather,
they tend to require multiple experts, working concurrently and supported by
numbers of CAD, CAM and other tools. COPS has been designed with these
requirements in mind. It is an interpreter of a superset of the OPS5 language.
It provides the means for implementing multiple blackboards that integrate
cooperating, concurrent expert systems, running in a distributed network of
processors.
-------

Refereshments will be served at 1:15

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

Date: Thu 20 Jun 85 10:49:56-EDT
From: Monica M. Strauss <[email-masked]>
Subject: Seminar - Unification of Logic, Function and Frames (MIT)

Date:  Friday 21 June, 1985
Time:  11:00AM
Place: 8th Floor Playroom

     The Uranus System -- Unification of Logic, Function and Frames

                        Hideyuki Nakashima
                    Electrotecnical Laboratory
                          Tsukuba, Japan

                             Abstract

Uranus is a knowledge representation system based on the concept of logic
programming.  The basic computational mechanism is the same as that of the
famous (or infamous!) logic programming language, Prolog, with several
extensions.

One important extension is the introduction of a multiple world mechanism.
Uranus consists of several independent definition spaces called worlds.
Worlds are combined at execution time to form a context for predicate
definitions.  Regarding a given world as a frame for a given concept, and
predicates as slots, you have a frame-like system in logic programming.

Another extension is along the lines of functional notations within the
semantics of logic.  Uranus has only one semantics, that of logic
programming.  At the same time, it has the expressive power, or
convenience, of functional programming.  Lazy execution of functional forms
follows naturally, since portions are computed only when they are necessary
for unification.

A brief demonstration of the system is scheduled following the talk.

Host: Gerald J. Sussman

REFRESHMENTS will be served.

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

Date: Thu, 20 Jun 85 15:11:31 cdt
From: [email-masked] (Ted Briggs)
Subject: Seminar - Automatic Example Generation (UTexas)

EGS: A Transformational Approach to Automatic Example Generation

                               by
                          Myung W. Kim

                      noon Friday June 28
                            PAI 5.60

In the light of the important roles of examples in AI, methods for
automatic example generation have been  investigated.   A  system
(EGS) has been built which automatically generates examples given
a constraint specified in the Boyer-Moore logic.  In EGS examples
are generated by successively transforming the constraint formula
into the form  of  an  example  representation  scheme.   Several
strategies  have  been  incorporated:  testing  stored  examples,
solving   equations,   doing   case-analysis,    and    expanding
definitions.   Global  simplification  checks  inconsistency  and
rewrites formulas to be easy to handle.

EGS has been tested for  the  problems  of  controlling  backward
chaining  and  conjecture  checking  in  the  Boyer-Moore theorem
prover.  It has proven to be powerful -- its power is mainly  due
to  combining  efficient  procedural knowledge and general formal
reasoning capacity.

In this talk I will present the operational  aspect  of  EGS  and
some underlying principles of its design.

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

Date: Wed 19 Jun 85 17:02:36-PDT
From: Emma Pease <[email-masked]>
Subject: Seminar - Qualitative Process Theory (CSLI)

         [Excerpted from the CSLI Newsletter by Laws@SRI-AI.]

                    *NEXT* THURSDAY, June 27, 1985

   4:15 p.m.            CSLI Colloquium
     Redwood Hall       ``Qualitative Process Theory''
     Room G-19          Ken Forbus, University of Illinois, Computer Science

                     ``Qualitative Process Theory''

   Things move, collide, flow, bend, stretch, break, cool down, heat up,
   and boil.  Intuitively we think of the things that cause changes in
   physical situations as processes.  Qualitative Process Theory defines
   simple notions of quantity, function, and process that allow
   interesting common-sense inferences to be drawn about dynamical
   systems.  This talk will describe the basics of the Qualitative
   Process Theory, illustrate how it can be used to capture certain
   aspects of different models of physical phenomena, and discuss the
   claims it makes about causal reasoning.              --Ken Forbus

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

Date: Tue, 18 Jun 85 10:04:54 EDT
From: coopercc@GE-CRD
Subject: Seminar - Architectures for Logic Programming (GE)

                    Computer  Science Seminar
                General  Electric  R & D  Center
                        Schenectady, N.Y.

               Experimental Computer Architectures
                      for Logic Programming

                       Prof. John Oldfield
                       Syracuse University

                        Tuesday, June 25
              10:30 AM, Conference Room 2, Bldg. K1
                     (Refreshments at 10:15)

     ABSTRACT: Syracuse University is an established  center
     for  research  in logic programming languages and their
     applications. In the last few years research  has  com-
     menced  on  ways  of speeding-up the execution of logic
     programs by special-purpose computer architectures  and
     the incorporation of custom VLSI components.
     The  Syracuse  Unification  Machine  (SUM)  is  a   co-
     processor for a host computer executing LOGLISP. Unifi-
     cation is a fundamental and  common  operation  in  the
     execution of logic programs, and is highly recursive in
     nature. SUM speeds up unification by the combination of
     separate functional units operating concurrently, high-
     speed  pattern  matching  and  the  use   of   content-
     addressable  memory  (CAM) techniques. Unification fre-
     quently requires a variable to be  bound  to  something
     else, such as an expression, a constant or even another
     variable. The Binding Agent of SUM  holds  the  set  of
     current  bindings in the form of a segmented stack, and
     with the aid of a CAM made up of custom  nMOS  circuits
     it  is possible to check if a variable is already bound
     in under 150 nS. Binding is an operation which  may  be
     carried  out  concurrently  in  most situations, and an
     extra Binding Agent  may  be  used  to  advantage.  The
     Analysis  Agent  is another custom nMOS component which
     implements  the   pattern-matching   and   case-by-case
     analysis  required.  It is organized as a pipeline, and
     uses a state machine implemented as a PLA.

     (Note: the June issue  of  Byte  Magazine  contains  an
     informative article on this work by Phillip Robinson)

     Notice to Non-GE attendees: It is necessary that we ask
     you   to   notify   Marion  White  ((518)  387-6138  or
     WHITEMM@GE-CRD) at least two days  in  advance  of  the
     seminar.

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

Date: Thu, 13 Jun 1985  11:58 EDT
From: [email-masked]
Subject: Seminar - Reasoning via Constraints (MIT)

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

                               Tuesday, June 18
                              8th Floor Playroom
                                    4:00PM

                           REASONING ABOUT PROGRAMS
                                      VIA
                            CONSTRAINT PROPAGATION

                             Thomas G. Dietterich
                        Department of Computer Science
                            Oregon State University

  This talk describes a program reasoning system (PRE) and its application to
problems of incremental program development in machine learning.  PRE solves
the following problem: given a program (in a modified Programmer's Apprentice
notation) with tokens labeling some of the ports in the program, find the set
of possible "interpretations" of the program.  That is, compute the set of
executions consistent with the given information.  The characterization of
these executions should be succinct and their computation should be efficient.
To perform this task, PRE applies constraint propagation methods.  The talk
will focus on (a) modifications made to the P.A. notation, (b) techniques
introduced to handle failure of local propagation, and (c) strategies for
resolving the frame problem.  PRE is part of a larger system, EG, whose task is
to form (procedural) theories of the UNIX operating system through
experimentation and observation.  EG's theories take the form of programs, and
PRE is applied to perform tasks of data interpretation and goal regression.

Refreshments will be served.

Host:  Richard C. Waters

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

Date: Tue 18 Jun 85 19:27:09-PDT
From: [email-masked]
Subject: Seminar - Planning as Debugging (SRI)

                        PLANNING AS DEBUGGING

                   Reid Simmons -- MIT AI Lab / SPAR
                      11:00 am, Monday, June 24
		     Room EJ232, SRI International

    We are currently building a domain independent planner which can
represent and reason about fairly complex domains.  The first part of
the talk will focus on the representations used and the rationale for
choosing them.  The planner uses explicit temporal representations,
based on time points and the notion of "histories".  It also extends
the traditional precondition/postcondition representation of actions
to include quantification, conditionals and the ability to reason
about cumulative changes.
    The second part of the talk will focus on techniques to organize
and control the search for a plan.  We view planning as "debugging
a blank sheet of paper".  We correct a bug (ie. unachieved goal) by
changing one of the underlying assumptions in the plan which are
responsible for the bug.  This problem solving approach combines
backtracking with traditional planning techniques, giving the planner
the potential for finding a solution with much less search.  We also
present a simple, but effective, technique for choosing which plan
modification to pursue, based on maintaining a complete goal structure
of the plan.
    This planner has been partially implemented and tested on
traditional blocks-world and register-transfer examples.  It is
currently being applied to the problem of geologic interpretation and
to diagnosis of chip manufacturing problems.

-------

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

End of AIList Digest
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