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Program: European AI Training Program
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course-number: EY-A828E-L0
description In cooperation with AI-Consultancy Group of NSTC in Valbonne,
Inhouse-training of Educational Services, Munich,
is offering the training program for Artificial Intelligence
that is described in detail on the following pages.
Expert Systems Technology is being used increasingly to
solve a wide range of business issues and problems. Customers
increasingly expect DIGITAL to demonstrate that we can
deliver solutions which require the use of this technology.
The AI training program is designed to provide the student
with a sound theoretical and practical understanding of
Expert Systems Technology and thus contribute to the development
of a core of skilled specialists within DIGITAL Europe.
On completion of the training program the student will be
able to tackle projects of medium complexity AND identify
those projects which are beyond his/her experience or the
capability of the technology. It is a complete program which
not only addresses the theoretical but also the practical
aspects of knowledge acquisition and program management.
Expert Systems are NOT easy to successfully build and
implement. It is essential that the basic training provided
by this course is undertaken before embarking on Customer
Project Activity.
location Training Center, Munich, Germany
dates: shipment of prereading material : 4. 7.1988
start of course-string : 26. 9.1988
start of students project work : 31.10.1988
Project-End-Workshop : April 1989
enrolment: via local training center
contact person: Heinz Buerkert, Educational-Services,
Munich-Unterfoehring
telephone DTN 773-2068
DECmail @UFH
VAXmail MUNSBE::BUERKERT
student-profile
software specialist
- who has shown technical leadership,
- openness to new technology
- and can understand both, business and technical aspects
of SW-applications.
goal - identify what is beyond the current technology
- identify fields to use AI in
- know the areas of AI
- learn about the IMPORTANT languages IN AI
- learn one tool deeply
- be able to begin using Expert-System-Technology productively
in real business applications.
modules of 1) self-study
the program 2) classroom session
a) AI Introduction and Overview
b) Languages Overview
c) Knowledge Acquisition
d) NEXPERT basic training
e) Matching Problems to Tools
and Problem Sizing
f) Prototyping in NEXPERT
g) Expert System Project Management
h) AI-presentation workshop and Summary discussion
3) Project Work
4) Project-End-Workshop
format SELFSTUDY in the specialist's home office during
preparatory phase
FORMAL TRAINING in MUNICH in 2 sessions:
1st: 5 weeks-string
2nd: 1 week workshop after phase of project work
GUIDED PROJECT WORK in the specialiast's home office
length 5 days preparatory self-study in the home office
5 weeks course string in Munich
30% project work over a 5 month periode in home office
1 week course in Munich
It should be emphasized that the project component is essential
to success in the program. The candidate should have the
support of his/her manager in order to devote the required
30% time required.
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module SELF-STUDY
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description The student will be exposed to key terms/concepts/names
before actually attending the classroom session. This module
will encourage the student to open her/his mind towards the
understanding and relations of those topics.
The student will also be encouraged to identify possible
applications in the environment of her/his home office
(Digital or client applications) which need to be solved
and which seem to be solvable by AI technology in the
project work phase after the classroom string.
In order to help the student identify likely projects,
the material will contain a basic list of criteria
to help verify her/his ideas.
goal . know some parts of AI-history
. list the key fields and the key institutions/companies
in AI-business
. list some key ideas, concepts and terms being used
in AI-business
. pre-select a likely project to do after completion of
the classroom phase
format Self-study with a video-tape and books
length 5 days spent over a periode of 3 months
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module INTRODUCTION TO AI
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description AI is a wide field of research and application.
In this module the fields of AI are introduced and
the basic problems are shown. Further on, the
focus will be on KBS, the tools used and their concepts.
Since AI is integrated technology an overview of
the process of building knowledge based systems is also
included.
goal get an overview of the fields of AI
understanding of Expert-Systems
understand the key terms and their meaning
understand the process of building an Expert-System
format lecture
length 2 days
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module Languages overview
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description In this module the three basic 'languages' used in AI
are introduced. The students get hands on experience in
using the languages LISP, PROLOG and OPS5
on basic examples.
By actually using the run time environment, students get a good
feeling for the concepts, capabilities, strengths and weaknesses
of these languages.
These concepts will appear in many of the more sophisticated
tools and shells. So this module teaches the basics for
assessment and use of most tools being offered in the market.
goal know the basic syntactical elements of all 3 languages
know the run-time environment, like Editor, debugger etc.
understand the key concepts of all three languages
understand the knowledge-representation formalisms of
PROLOG and OPS5
describe the suitablility of each language for different
problem areas.
format lecture/ lab
length 6 days
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module Knowledge Acquisition
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description Knowlege acquisition is a crucial process in building
Expert Systems, the most widely used area of AI in industry.
The knowledge engineer has to understand what an expert is
doing and how he does it. Then he creates a model of the
knowledge and he implements it by means of a tool.
In this module, the student will become aware of the importance
of nontechnical and psychological issues, such as
administrative issues, awards, meetings, questioning,
motivation of an expert, selection of an expert etc.
He will also learn about the technical aspects, including
modelling, concepts etc.
goal - understand interview strategy and when to apply it
- understand the psychological elements wich can help or
hinder the KA process
- knows how to analyse an interview
- describe what expertise means
format lecture/ workshop
length 2 days
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module Nexpert basic training
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DESCRIPTION
NEXPERT is a tool with an object-oriented/rule-based knowledge
representation scheme and a sophisticated user interface
designed for rapid knowledge acquisition and prototyping.
This module teaches the fundamentals of using Nexpert via
lecture and hands-on sessions, and gives an insight into
high-end tools with advanced development environments and
hybrid knowledge representations.
GOAL
-Use the main features of the NEXPERT interface:
editors, browsers etc.
-Understand and use the object-oriented/rule-based
knowledge-representation to represent simple problem spaces.
-Control Nexpert's reasoning mechanism.
-Describe Nexpert's "Open AI Architecture".
format lecture/ lab
length 3 days
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module matching problems to tools
and problem sizing
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description
When solving a complex real-life problem, engineers are
faced with the tasks of
. understanding the problem,
. choosing the right tools and techniques to use,
. deciding which part of the problem to tackle first, and
. estimating the size of prototype and full systems
In Expert-Systems the complexity of a whole problem is not
examined in detail before programming begins, so that picking
an appropriate subset out of the problem domain is essential
for successful development.
goal
- Be able to estimate problem and solution sizes for prototype
and full systems.
- Understand features of the available tools and how to match
these to problems.
- Understand problem characteristics
- Be able to determine an appropriate domain for prototyping.
format lecture/ workshop
length 2 days
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module Prototyping in NEXPERT
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description Prototyping is the most essential technique in
the process of building expert systems. It is
demonstrated by the method chosen for this module, to
guide students from a simple to an increasingly
complex task.
In this module students mainly program using one basic example,
which is complicated enough to fill a whole week.
goal
get experience in NEXPERT
experience the prototyping cycle
practise knowledge acquisition and representation techniques
format lab exercise
length 5 days
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module Project management
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description Management of expert system projects is similar to that
of conventional projects in many ways, and many of the
same techniques can be applied. At the same time, there
are features of the technology which call for different
approaches, e.g. the iterative prototyping method. This
module highlights the features of successful projects.
goal - understand similarities and differences between
conventional and expert system project management
- learn the importance of expectation setting for
users, experts, management, and other staff involved
in expert system projects.
- define the phases of an ES-project, including the
needs and difficulties in the various project phases
- understand the 3 important aspects of an
expert systems project: business, technical and
organizational
- understand the work of a 'change agent'
- be able to apply the above concepts to the project
selected by the student
format lecture - workshops
length 3 days
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module Ai-presentation workshop
and Summary discussion
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description 1 day will be spent to prepare, present and discuss a
short presentation about AI and Expert-System technology in
order to prepare students to do their own presentations in
their home office and to wrap up what they have learnt
during the preceeding weeks.
The last half-day will provide the student with a
overview of AI and Expert-System resources in Europe, as well
as product directions and marketing strategy.
goal - prepare a short AI presentation for use in the student's
home office
- understand the state of Digital's AI marketing strategy
in Europe
- understand the resources available to the student in
his/her home office
- be prepared to begin project work in the home office
format lecture/ workshop/ discussion
length 2 days
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module project work
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description Active work is crucial for the success of educational
effort. This training would miss its primary goal,
to enable the student to actively solve real-life problems,
if the teaching part were not followed by an intensive
practical phase.
Students are expected to work 30% of their time to develop
an demonstration prototype for an application in their home
office. It is expected that the students manager will
agree to this time investment in project work.
During their project work, students will get ongoing
support by Ed-Services and Valbonne, respectively:
- a telephone hotline will be established expressly
for problems encountered during the project work
- every student will be visited, at least, once during the
project phase by the course monitor,
in order to give help, support and advice.
goal - select an appropriate problem to be solved
- set up a project
- solve a subset of the problem with Expert-system
methodology
- experience project-management issues
- experience the capabilities of the tools and methods used
- understand the dependency of ES technology with
other technologies
format guided project work
length 30% of time in a 5 months period
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module Project-end-worksop
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description after the 5 months project work, students will return
to class in order to present the results and experiences of
their work.
Common problems, solutions and observations during the project
phase will be discussed.
A guest speaker will give a presentation about a technology
issue of common interest.
goal - share the experiences among the group
- get technology update
format lecture/ lab/ workshop
length 5 days
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| Here is a current list of advanced courses being offered by the AI Training
group in the U.S.. For registration information, please contact Louise
(expert::) King.
SCHEDULE
ADVANCED COURSES
Q1,Q2 FY`89
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Date Course Instructor Max. #
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Sept. 26-30 Advanced OPS Leslie Chesler 20
Tom Cooper
Oct. 3-5 Knowledge Dr. Raoul Smith 25-30
Representation
& Reasoning
Oct. 17-28 Knowledge Craft CGI 20
Nov. 2, 15, 22, Advanced Knowledge Dr. Edwina Rissland 15
29, Dec. 6, 13 Representation
Nov. 9-11 Object Oriented Dr. Stanley Zdonik 25-30
Jan. 16-20 Technology and its
Role in Database
Systems
*Nov. 14-18 Advanced LISP Dr. David Touretzky 20
Nov. 28 - Object Oriented Topher Cooper 20
Dec. 2 Programming Craig Schaffert
Dan Halbert
Steve Kirk
Kathy Chapman
Enrique Alvarez
Jan. 23 - Knowledge CGI 20
Feb. 3 Engineering
*New courses
Location and time: all courses will be held at DLB12,
295 Donald Lynch Boulevard, Marlborough, MA and,
except for Advanced Knowledge Represenation, will
start at 9:00 am.
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EY-D070E - PROGRAMMING IN VAX DECISION EXPERT - TRNG ANN.
COURSE SUMMARY & CHARACTERISTICS
Date : August 28/September 1 Duration : 5 days
Location : Munich, Germany Course type : Lec/lab, slot-controlled
Any slots not taken up by the 1st August will be offered on an open
enrolment (first-come-first-served) basis.
Target function : EDU, SWAS, FS
Deadline for enrolment : August 18, ENROLMENT TEC @UFH
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PROGRAMMING IN VAX DECISION EXPERT
TARGET AUDIENCE
Non-AI programmers and engineers who wish to develop expert or
knowledge-based applications within the VMS environment.
PREREQUISITES
Students should have some knowledge of a programming language such as VAX
C. A basic knowledge of expert system terms and concepts is helpful. The
basic understanding of expert system terms and concepts can be obtained
from the course "BUILDING AND PROTOTYPING EXPERT SYSTEMS" EY-A918E-L0.
COURSE OBJECTIVES
This course is designed to teach students to:
. Build diagnostic and maintenance expert systems
. Intelligent help systems
. Use if/then tables to represent heuristic knowledge
. Use decision trees for intelligent advisory systems
. Integrate a VAX decision expert application in an application solution
. Use the end-user interface to deliver developed applications.
COURSE DESCRIPTION
OVERVIEW
This course is designed to train non-AI programmers and engineers in the
use of VAX decision expert in building expert systems. The course will
focus on the use of VAX decision expert to build applications in
diagnostics, maintenancce and decision trees, although it can be used in a
variety of other areas. Working on Vaxstations students will have the
opportunity to develop practical skills using both VAX decision expert's
development and delivery environments.
TOPICS
. IF/THEN tables and rules
. Forward/backward chaining
. Linking modules
. AND/OR trees
. Logical operators: AND, OR, NAND, NOR, AND NOT
. Decision trees
. Utility language to display text menus, and access to external programs
and devices
. VAX decision expert end-user interface.
- DATE : August 28/September 1
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- LOCATION : Munich, Germany
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- ENROLMENT PROCEDURE :
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All enrolment request MUST BE SENT to ENROLMENT TEC @UFH with
the following information :
Course Corporate Nbr : EY-D070E
Course Title : PROGRAMMING IN VAX DECISION EXPERT
Course Dates : AUGUST 28/SEPTEMBER 1
Course Location : MUNICH, GERMANY
Complete Student name : <>
*** PREREQUISITE *** : YES or NO
Function : <>
Exact Job title : <>
Badge number : <>
Cost centre : <>
VAXmail or Email Addr. : <>
Manager's Name : <>
City & Country : <>
Arrival date : <>
Departure date : <>
Accommodation required : YES or NO
The agenda will be sent later on to the CONFIRMED participants
together with the final logistical and accommodation details.
DEADLINE FOR REGISTRATION :
AUGUST 18
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