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Date: Mon, 26 Aug 85 16:01:13 edt

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Subject: AIList Digest   V3 #104
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AIList Digest            Thursday, 8 Aug 1985     Volume 3 : Issue 104

Today's Topics:
  Seminars - Flexible Planning (SRI) &
    KADS Methodology for Knowledge Acquisition (BBN) &
    Parallelism in Logic Programs (SU) &
    Expert System Toolkit (SU) &
    Experiments with Belief Resolution (SRI) &
    Purpose-Directed Analogy (Rutgers)

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

Date: Wed 31 Jul 85 16:55:51-PDT
From: [email-masked]
Subject: Seminar - Flexible Planning (SRI)

               How to Plan an Action When You Don't Know What to Do:
                A Logic of Knowledge, Action, and Communication

                           Leora Morgenstern
                      New York University, SRI-AIC

                        11:00 AM, Monday, August 5
                 SRI International, Building E, Room EJ232

Most AI planners work on the assumption that they have complete knowledge
of their problem domain and situation, so that formulating a plan really
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.

   The talk will consist of 5 (hopefully short) parts:

1. Motivation for a flexible logic of knowledge, action, and communication,
2. Discussion of Bob Moore's modal logic of knowledge and action,
   its advantages, and its limitations with respect to a robust theory
   of planning,
3. A move towards a syntactic theory of knowledge, and a discussion of the
   resulting paradoxes (esp. the Knower Paradox),
4. A solution to the Knower Paradox based on Kripke's solution to the
   Liar Paradox,
5. A solution to the problem of knowledge preconditions.

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

Date: 5 Aug 1985 11:09-EDT
From: AHAAS at BBNG.ARPA
Subject: Seminar - KADS Methodology for Knowledge Acquisition (BBN)

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

   BBN-AI Seminar, 9 August 1985, 10.30 a.m.  10 Moulton St.,
Large Conference Room 2nd floor

   KADS: a structured methodology for knowledge acquisition

         Bob Wielinga University of Amsterdam

Current Expert System technology lacks a methodology and tools
which support a structured development of commercial Expert
Systems.  This is particularly the case for the knowledge
acquisition stage in E.S.  development.  KADS is the result of an
attempt to develop a structured methodology for knowledge
acquisition for E.S., and includes some preliminary support
tools.  The KADS methodology is based on the following
principles: 1) a decomposition of the knowledge acquisition task,
2) the use of a number of techniques for elicitation and
interpretation of verbal data, 3) the formalization of verbal
data in terms of epistemological models, independent of
implementation details, and 4) the use of generic models for
expert problem solving behaviour to guide the knowledge analysis.
The KADS methodology is being implemented in a system that will
support the knowledge engineer, both in performing the analysis
task and in the production of documentation.  In a number of case
studies KADS has been used in designing and implementing expert
systems.  A qualitative evaluation of these studies will be
presented.

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

Date: Fri, 2 Aug 85 16:33:25 pdt
From: Moshe Vardi <vardi@diablo>
Subject: Seminar - Parallelism in Logic Programs (SU)

          AND/OR PARALLELISM  IN LOGIC PROGRAMS

                             by

                        Simon Kasif
               Department of Computer Science
            University of Maryland, College Park

                        MJH 352
                    Aug. 14, 1:00pm

     The separation of logic and control in  logic  programs
has  been  shown  to  allow the programmer to write declara-
tively lucid programs whose execution is determined  by  the
interpreter. This appealing characteristic of logic program-
ming spurred  research  directed  towards  diversifying  the
means  for  controlling  the execution of logic programs. In
particular, parallelism in logic programs may  be  exploited
even  when it is impossible to state a priori that two goals
may be executed concurrently, but such an opportunity may be
detected during the course of the execution.

     This talk will address the problem of AND/OR  parallel-
ism in logic programming. We  describe a computational model
for AND/OR parallel execution of logic programs.  The  model
provides  the  primitives  to  describe and analyze parallel
interpreters, emphasizing the  data-flow  among  conjunctive
goals. The effectiveness of our computational model is esta-
blished through its ability to support both old and new com-
munication  paradigms  for  the  parallel  interpretation of
logic programs.

     Several methods  to  implement  AND/OR  parallelism  in
logic  programs are investigated based on notation developed
in the model. The methods are shown to define a spectrum  of
communication  schemes,  ranging  from  the set intersection
method  where   communication  is   eliminated   altogether,
through methods based on producers-consumers, where communi-
cation is uni-directional and finally ending at  a  flexible
bi-directional  scheme  introduced  in the paper, called the
Intelligent Channel.

     The primitives that comprise the model are used to syn-
thesize  two  new  parallel interpreters: Disjunctive System
(DS) interpreters and Intelligent Channel Interpreters.  The
Intelligent  Channel is a scheme we propose to constrain the
combinatorial explosion of active processes,  and  to  elim-
inate  the  need  to maintain a separate binding environment
for every active OR-branch.

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

Date: Tue 6 Aug 85 16:07:30-PDT
From: Carol Wright/Susie Barnes <[email-masked]>
Subject: Seminar - Expert System Toolkit (SU)

                            SIGLUNCH

DATE:              Friday,  August 9, 1985
LOCATION:          Chemistry Gazebo,  betweeen Physical and
                   Organic Chemistry
TIME:              12:05

SPEAKER:           Peter Jackson,
                   Department of Artificial Intelligence,
                   University of Edinburgh

TITLE:             A Flexible Toolkit for Building Expert Systems.

This presentation describes the progress to date of a three-year
Alvey-funded project to study, design and implement tools for building
knowledge-based systems.  The parties involved are Edinburgh University's
Department of Artificial Intelligence and GEC's Artificial Intelligence
Group at Great Baddow.  The aim of the seminar is not to present finished
work (the project is only six months old!), but rather to air our ideas and
prejudices in the hope of attracting criticism and other kinds of feedback
from the expert systems community.

Our survey of current expert systems technology has led us to believe that
neither shells such as EMYCIN (and derivatives) nor high-level programming
languages (such as LOOPS) represent the last word in expert system building
tools.  The former are generally restrictive with respect to both the
representation of knowledge and the specification of control, while the
latter present the average programmer with a bewildering array of
possibilities with little indication of how one combines different
programming styles in the construction of an expert systems architecture.
Thus, although there are groups of users for whom shells and AI programming
languages are well-suited, we feel that there is a substantial gap in the
market between the relative beginner or non-programmer, for whom the
majority of commercial shells are intended, and the veteran hacker, for and
by whom systems like LOOPS were developed.

The alternative approach that we are currently exploring can be summed up by
a number of slogans:

(1) The process of choosing a logically adequate representation language and
a heuristically adequate control regime and embedding these into a suitable
architecture should be guided by some analysis of the task one wants one's
system to perform.

(2) It is worth attempting to establish a correlation between a taxonomy of
expert systems tasks and representational schemes based on logical
languages, with respect to both the expressiveness required by the task
(e.g. modal and temporal notions, fuzziness, etc) and the control of
inference (e.g. different problem solving strategies).

(3) It is worth attempting to establish a similar correlation between tasks
and problem solving paradigms (such as ends-means analysis, hypothesize and
test, etc), with a view to helping the user decide on an architecture within
which he can embed the interpretation of this chosen logical language.

The problems we are currently considering include the following:

(1) Is it possible to provide, along with the toolkit, an abstract
architecture that can be instantiated in different ways to implement
different problem solving paradigms?

(2) Could one then embed different interpretations of different logics in
this architecture as part of the instantiation process?

(3) Could one get the behaviour associated with different problem solving
paradigms from this instantiated architecture by running the logical
language under different meta-level regimes?

(4) Will knowledge bases created for use with one instantiation of the
architecture have to be 'recompiled' for use with another instantiation?

(5) How can we help a user to make the 'right' design decisions (assuming we
know what these are)?

We feel that this research program raises a number of very difficult issues,
many of which will not be solved within the scope of the present project.
Nevertheless, we also feel that practical advances in expert systems
development ultimately depend upon theoretical issues of this kind being
addressed, however inadequately.  We still have open minds with regard to
the kinds of utility that a toolkit should provide, and are always ready to
talk to both the builders and users of tools in order to try and gain new
insights into the problem.

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

Date: Wed 7 Aug 85 11:26:38-PDT
From: [email-masked]
Subject: Seminar - Experiments with Belief Resolution (SRI)

                   Experiments with Belief Resolution

                  Kurt Konolige and Christophe Geissler
                              SRI AI Center

                        11:00 AM, Monday, August 12
                 SRI International, Building E, Room EJ232

In recent work, Konolige developed a resolution rule for a quantified
modal logic of belief.  However, the rule is difficult to apply in
practice, because it takes an arbitrary number of input clauses, and
some instances of the rule may subsume others.  In this talk we
describe a solution to this problem based on a generalization of
semantic attachment, that controls the growth of the search space.  We
have implemented the resulting version of belief resolution with
Stickel's first-order connection-graph theorem prover.  We present
several examples of automatic reasoning about belief using this
system, including a solution to the wise man problem.

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

Date: 7 Aug 85 09:37:41 EDT
From: [email-masked]
Subject: Seminar - Purpose-Directed Analogy (Rutgers)

                       MACHINE LEARNING SEMINAR

Title:          Purpose-Directed Analogy**

Speaker:        Smadar Kedar-Cabelli

Date:           Monday, August 12, 11:00 AM
Place:          Hill Center, room 423

        Recent artificial intelligence models of analogical  reasoning
are based  on  mapping some  underlying  causal network  of  relations
between analogous situations.  However, causal relations relevant  for
the purpose of  one analogy may  be irrelevant for  another.  In  this
talk,  I  will   introduce  a   technique  which   uses  an   explicit
representation of the purpose of  the analogy to automatically  create
the relevant causal network.  I will illustrate the technique with two
case studies in which  concepts of everyday  artifacts are learned  by
analogy.

** This  is a  dry-run for  a talk  being presented  at the  Cognitive
Science Society Conference in Irvine, CA, August 15-17.

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

End of AIList Digest
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