LAWS@SRI-AI.ARPA (07/12/85)
From: AIList Moderator Kenneth Laws <AIList-REQUEST@SRI-AI>
AIList Digest Friday, 12 Jul 1985 Volume 3 : Issue 92
Today's Topics:
News - Center for Machine Intelligence,
Journals -- IEEE Software & SPIE AI Issue,
Book - A Vision of Knowledge Engineering
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Date: Thu 11 Jul 85 22:39:16-PDT
From: Ken Laws <Laws@SRI-AI.ARPA>
Subject: Center for Machine Intelligence
I have received an announcement of the formation of a new
Center for Machine Intelligence under the co-directorship
of Dr. Ashby Woolf and Prof. Lynn Conway (recently of DARPA).
The center is being formed by Electronic Data Systems Corporation
(subsidiary of GM) and the University of Michigan. Their address is
2001 Commonwealth Blvd., Ann Arbor, MI 48105, phone (313) 995-0900.
-- Ken Laws
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Date: Wednesday, 10 July 1985 08:20:16 EDT
From: Duvvuru.Sriram@cmu-ri-cive.arpa
Subject: Reminder -- IEEE Software
A reminder that the last date for the submission of papers for
the IEEE software special AI issue is July 15th. If anyone wants an
extension till August 1st, send mail to sriram@cmu-ri-cive.arpa.
Sriram
FOR YOUR INFORMATION:
The March 1986 issue of IEEE Software will address software aspects of
knowledge-based systems developed for engineering applications.
(IEEE Software is one of the prestigious magazines devoted to problems
in Software engineering).
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Date: Thu 11 Jul 85 22:32:33-PDT
From: Ken Laws <Laws@SRI-AI.ARPA>
Subject: SPIE AI Issue
Mohan Trivedi tells me that SPIE (Society of Photo and Instrumentation
Engineers) is planning a special AI issue of their journal early
next year. This seems to be a follow-on to their recent conference
on AI. Contact Dr. Trivedi at
Electrical Engineering Dept.
Louisiana State University
Baton Rouge, LA 70803
Phone (504) 388-6826
soon if you are interested in contributing. Submitted papers need not
be about optics.
-- Ken Laws
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Date: Sun, 7 Jul 85 14:36:30 edt
From: Tom Scott <scott%bgsu.csnet@csnet-relay.arpa>
Subject: A Vision of Knowledge Engineering
I'm writing a manuscript, "A Vision of Knowledge Engineering",
for Prentice-Hall. The intended audience of the textbook is
undergraduate students in philosophy, management information science,
and computer science.
The parts of the book correspond to the audience: Part I
(chapters 2-4) is concerned with the theoretical foundations of
knowledge engineering and will focus on symbolic logic and epistemology,
including both the empirical work of John Dewey, Rudolf Carnap, and
Isaac Levi as well as the transcendental work of Immanuel Kant and
Edmund Husserl. Part II (chapter 5) introduces topics from information
management such as managerial cybernetics, decision support systems, and
man/machine interaction (software engineering and human engineering).
Part III (chapter 6) outlines the techniques and concepts of artificial
intelligence that young knowledge engineers need to be on friendly terms
with in order to design and implement applications programs such as
knowledge-based expert systems. Here is an outline of the manuscipt:
A VISION OF KNOWLEDGE ENGINEERING
1. Introduction
2. Intelligent Resources
2.1 The Age of Knowledge
2.2 The System of Intelligent Resources
2.3 Revision of Intelligent Resources
3. Deductive Knowledge
3.1 Logic Programming
3.2 Automated Reasoning
3.3 Deductive Systems
4. Deep Knowledge
4.1 Deliberate Expansion of Knowledge Bases
4.2 Structures of Consciousness
4.3 What Is Inductive Logic?
5. Information Management
5.1 Managerial Cybernetics
5.2 Decision Support Systems
5.3 The Unix Development Environment
6. AI Techniques
6.1 Concepts and Techniques
6.2 Applications
6.3 The AI Development Environment
7. Vision of Possibilities
As I begin to close off the theoretical part (chapters 2-4), I'm
looking for concrete systems and applications to write about in chapters
5 and 6. If anyone has suggestions about systems that I can review,
please contact me at the address listed at the end of this article.
* * *
Here are three diagrams that summarize the approach to knowledge
engineering that I've taken in the manuscript. The first diagram
(figure 1) shows a canonical or ideal form that seems to underlie most
(all?!) interactive knowledge systems. Figures 2 and 3 summarize the
literature that I have found to be a good basis for teaching the theory
and practice of knowledge systems and intelligent databases to young
knowledge engineers.
+---------+
________ |knowledge|_________
| | base | |
| +---------+ |
| | |
| | |
+---------+ +---------+ +---------+ +---------+ +---------+
| expert |____| inter- |____|inference|____| inter- |____| user |
| | | face | | engine | | face | | |
+---------+ +---------+ +---------+ +---------+ +---------+
| | |
| | |
| +---------+ |
________ | black- | ________
| board |
+---------+
Figure 1. An interactive knowledge system. Without much
difficulty most block diagrams of knowledge systems can fit into
this framework.
5. Rule-Based Expert Systems (Buchanan & al. 1984)
=======================================================
4. Principles of Artificial Intelligence (Nilsson 1980)
3. Automated Reasoning (Wos & al. 1984)
2. Automated Theorem Proving (Loveland 1978)
=======================================================
1. Mathematical Logic (Shoenfield 1967)
Figure 2. The five levels of knowledge engineering as seen
from the theoretical perspective. The concept of a production
system and the concept of representation-inference-control can
be traced on each of the five levels.
5. Concepts and Terminology for the Conceptual Schema
and the Information Base (van Griethuysen 1982)
=====================================================
4. Advances in Data Base Theory (Gallaire & al. 1981)
3. Towards a Logical Reconstruction of Relational
Database Theory (Reiter 1984)
2. Automated Theorem Proving (Loveland 1978)
=====================================================
1. Mathematical Logic (Shoenfield 1967)
Figure 3. The five levels of knowledge engineering and automated
reasoning as seen from the practical perspective of deductively-
augmented (intelligent) relational databases. The diagram is
motivated by the practical need for intelligent deductions in
large commercial databases.
One more diagram, figure 4, helps round out this preliminary
Vision. I drew figure 4 after using Argonne's Interactive Theorem
Prover (ITP). ITP is a beautiful tool for giving young knowledge
engineers experience and understanding of how to build a production
system consisting of entities (the knowledge base of atomic and
molecular clauses), operators (rules of inference), and control
(strategies such as forward and backward demodulation and forward and
backward subsumption).
+------------+
----------->| Choice |------------
| | strategies | |
| +------------+ |
| || |
| || |
| +---------------+ V
+------------+ | | +------------+
| Backward |----| Knowledge |----| Inference |
| strategies |----| base |----| rules |
+------------+ | | +------------+
A +---------------+ |
| || |
| || |
| +------------+ |
| | Forward | |
------------| strategies |<-----------
+------------+
Figure 4. Argonne National Laboratory's Interactive Theorem
Prover (ITP). This diagram is adapted from Allen and Luckham
1970 and Wos et al. 1984 (section 4.4, "Order of Operations").
The flow of operations is clockwise, beginning with the choice
strategies. ITP stops when the knowledge base exhibits an appropriate
termination condition such as the null clause in refutation proofs. The
corrpesondonce between ITP and an abstract production system is:
ITP Production system
-------------- -----------------
knowledge base entities
inference rules operations
strategies control
That is why we ITP addicts are so excited about using ITP in knowledge
engineering. ITP serves as an example of a production system and a
production-system-building tool. If you've ever tried using Yaps in the
University of Maryland Lisp environment, I think you'll agree that ITP
is a lot simpler. I don't mean of course to tell people not to use the
Maryland systems or other production-system-building tools. My
intention here is only to recommend the Argonne system as the easiest
way that I know of for new knowledge engineers to effortlessly gain full
knowledge of production systems through ITP-based experience and
understanding.
Note the nice picture that results when we combine figure 1 (the
interactive knowledge system) and figure 4 (ITP). Simply replace the
middle section of figure 1 by figure 4. The knowledge bases in each
diagram correspond well; the inference engine and blackboard of the
interactive knowledge system correspond to the inference-rule module and
strategy modules of ITP. The resulting picture is clean and neat. What
more could a young knowledge engineer ask for?
* * *
I look forward to hearing your replies to these ideas. We are
children of the cybernetic revolution and we are witnessing the rising
sunshine of the Age of Enlightenment.
Jai Guru Dev,
Tom Scott
Department of Mathematics and Statistics
Bowling Green State University
Bowling Green OH 43403-0221
Csnet: scott@bgsu
UUCP: ...!cbosgd!osu-eddie!bgsuvax!scott
Day phone: 419-372-2636
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End of AIList Digest
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