Download File - Computers in Manufacturing Enterprise (MEL 423)

Computers in Manufacturing
Enterprises
Expert Systems
October 5, 2015
Vandana Srivastava
Introduction: Expert Systems (ES)
 one of the application field of artificial intelligence (AI)
 most promising for commercial applications
 computer programs that solve difficult problems that are
traditionally solved by human experts
 cost effective consultant
 explains reasoning behind any solutions it finds
 able to learn from experience
•http://ie.emu.edu.tr/development/dosyalar/%7Bi6A-ec9-Tbe%7DIEand.ppt
Expert Systems?
Knowledge-based systems are concerned with applying
knowledge to solve problems that usually require human intelligence
and/or expertise
Expert systems are the knowledge-based systems that consist of
computer programs design to represent and apply factual knowledge of
specific areas of expertise to solve problems
They emphasize domain-specific problem solving strategies and
employ self-knowledge to reason about their own interference
process and provide explanation or justifications for conclusions
reached
http://ie.emu.edu.tr/development/dosyalar/%7Bi6A-ec9-Tbe%7DIEand.ppt
Expert Systems: Features
 expert systems are assistants to decision makers and not
substitutes for them
 power of expert systems lies in the specific knowledge
about a narrow domain stored in the expert
system's knowledge base
 knowledge base of an ES also contains heuristic
knowledge - rules of thumb used by human experts who
work in the domain
Example of Decision Problem
 Mortgage application for a loan for $100,000 to $200,000
 IF
 If there are no previous credits problems, and
 If month net income is greater than 4x monthly loan payment, and
 If down payment is 15% of total value of property, and
 If net income of borrower is > $25,000, and
 If employment is > 3 years at same company
 THEN
 Then accept the applications
 ELSE
 Else check other credit rules
NASA Example
 NASA has developed a fleet of intelligent space probes that
autonomously explore the solar system
 To achieve success through years in the harsh conditions of space travel,
a craft needs to be able to radically reconfigure its control regime in
response to failures and then plan around these failures during it
remaining flight
 NASA expects that the set of potential failure scenarios and possible
responses will be much too large to use software that supports preflight
enumeration of all contingencies
Example: Livingstone2
• reusable artificial intelligence (AI) software system
• designed to assist spacecraft, life support systems, chemical plants or other
complex systems in operating robustly with minimal human supervision, even
in the situation of hardware failures or unexpected events
http://ti.arc.nasa.gov/opensource/projects/livingstone2/
Components of Expert Systems
Components of Expert
Systems
A knowledge
base
consists of all
facts, rules,
relations etc used
by the experts
inference systems
contains the lines of
reasoning followed by the
expert to organize and
control the steps taken to
solve problems in a given
domain.
http://ie.emu.edu.tr/development/dosyalar/%7Bi6A-ec9-Tbe%7DIEand.ppt
A dialog system:
user interface
which communicates
with the knowledgebase through the
inference system
Expert Systems Representation
http://www.igcseict.info/theory/7_2/expert/
Need for Expert Systems
•
human expertise is a scarce and unique resource
•
inconsistency in humans in day to day decisions for
the same set of data
•
expert systems are needed because of certain
inherent human characteristics that can impair the
optimality of decisions
http://ie.emu.edu.tr/development/dosyalar/%7Bi6A-ec9-Tbe%7DIEand.ppt
Generic Application of Expert Systems
Classification - identify an object based on stated characteristics
Diagnosis Systems - infer malfunction or disease from observable data
Monitoring - compare data from a continually observed system to prescribe
behaviour
Process Control - control a physical process based on monitoring
Design - configure a system according to specifications
Scheduling & Planning - develop or modify a plan of action
Generation of Options - generate alternative solutions to a problem
http://www.umsl.edu/~joshik/msis480/chapt11.htm
Roles in Expert System Development
Three fundamental roles in building expert systems are:
 Expert  success of the ES depends on the experience and application of knowledge
of the people responsible of its development
 large systems generally require multiple experts
 Knowledge engineer
 elicits knowledge from the expert, gradually gaining an understanding of
an area of expertise
 Intelligence, tact, empathy, and proficiency in specific techniques of
knowledge acquisition
 Knowledge-acquisition techniques include conducting interviews with varying
degrees of structure, protocol analysis, observation of experts at work, and analysis
of cases
 must also select a tool appropriate for the project and use it to represent
the knowledge with the application of the knowledge acquisition facility
 User
http://www.umsl.edu/~joshik/msis480/chapt11.htm
How Expert Systems Work ?
Knowledge Representation and the Knowledge Base
 contains both factual and heuristic knowledge
 Knowledge representation is the method to organize the
knowledge of the knowledge base
 two of the knowledge representation methods are:
 Frame-based systems: frame specifies the attributes of a complex system
 Rule-based expert systems: knowledge is represented by production rules
(consists of an IF part (a condition or premise) and a THEN part (an action or
conclusion))
 explanation facility
 explains how the system arrived at the recommendation
http://www.umsl.edu/~joshik/msis480/chapt11.htm
How Expert Systems Work
Inference Engine
 combines the facts of a specific case with the knowledge contained in
the knowledge base to come up with a recommendation
 directs the user interface to query the user for any further
information it needs
 facts of the given case are entered into the working memory, which
acts as a blackboard, storing all the knowledge about the case
 inference engine repeatedly applies the rules to the working
memory, adding new information (obtained from the rules
conclusions) to it, until a goal state is produced or confirmed
 any one of the several strategies can be used by an inference engine
to reach a conclusion
http://www.umsl.edu/~joshik/msis480/chapt11.htm
How Expert Systems Work ?
Backward chaining
Forward chaining
 the inference engine attempts to match the
 data-driven strategy
 inferencing process moves from the facts of
the case to a goal (conclusion)
 inference engine attempts to match the
condition (IF) part of each rule in the
knowledge base with the facts currently
available in the working memory
 If several rules match, a conflict resolution
procedure is invoked; for example, the
lowest-numbered rule that adds new
information to the working memory is fired.
The conclusion of the firing rule is added to
the working memory.
 commonly used to solve difficult problems of
a design or planning nature, such as, for
example, establishing the configuration of a
complex product.
assumed (hypothesized) conclusion - the goal or
subgoal state- with the conclusion (THEN) part
of the rule
 If such a rule is found, its premise becomes the
new subgoal. In an ES with few possible goal
states, this is a good strategy to pursue.
 If a hypothesized goal state cannot be supported
by the premises, the system will attempt to
prove another goal state. Thus, possible
conclusions are review until a goal state that can
be supported by the premises is encountered.
 best suited for applications in which the possible
conclusions are limited in number and well
defined
eg: Classification or diagnosis type systems, in
which each of several possible conclusions can
be checked to see if it is supported by the data
http://www.umsl.edu/~joshik/msis480/chapt11.htm
ES in Production and Operations Management
http://www.it.iitb.ac.in/~palwencha/ES/J_Papers/ES_APP_OPR.pdf
ES in Production and Operations Management -- 2
Capacity
planning
• decision area
central to the longterm growth of an
organization
• a capacityplanning ES for
the IBM system
called a
performance
expert prototype
(PEP)
• uses a blackboard
as a means of
communication
between modules,
each of which is a
specialist in some
sub-task
Facility Location
and Design
Facility Layout
• decision involves
both quantitative
factors (eg. labour
costs,
transportation
costs) and
qualitative factors
(community
attitudes, legal
problems)
• used a group
technology-based
approach for finding
the initial part
machine groupings
and laying out the
machining areas in
these groups to
minimize materialhandling costs
• facilities design
expert system
(FADES) is used
to combine
judgemental rules
of human experts
with quantitative
tools
• WORKPLACE
DESIGNER
evaluates layouts
and recommends
design changes to
improve labour
productivity
http://www.it.iitb.ac.in/~palwencha/ES/J_Papers/ES_APP_OPR.pdf
Project
Management
• deals with the
scheduling and
control of large
projects
• application of an ES
using project
management was
developed by Levitt
and Kunz
• they used a
prototype which
applied AI
techniques to modify
activity lists and
schedules
• model was applied
to offshore oil
platform
construction
ES in Production and Operations Management -- 3
Aggregate
planning
• done on a monthly
basis over a 6-18month horizon
• N. PATRIARCH is a
multilevel planning,
scheduling and
control system that
was developed for
manufacturing
• uses AI knowledge
modelling, multilevel networking
and advanced
economic costing to
plan on strategic
forecasting and
planning
Process choice /
design
• selection of inputs,
operations, work
flows and methods
used to produce
goods and services
• input selection consist
of desired
contribution of human
skills, raw materials
and equipment
• first knowledge-based
system to deal with
process design was
GARI
• Technostructure of
machining (TOM)
was used for
planning drilled holes
Product design
• eXpert CONfigur
er (XCON) used in
the product design
specifications of
all major Digital
Equipment
Corporation (DEC)
systems
• considered factors
such as cablelength constraints,
sizes of cabinets
and power
requirements in
the configuration
of systems
http://www.it.iitb.ac.in/~palwencha/ES/J_Papers/ES_APP_OPR.pdf
Scheduling
• Intelligent
scheduling and
information systems
(ISIS) was the first
application of ES to
job-shop scheduling
• opportunistic
scheduler (OPIS) ,
a factory-scheduling
system generates
constraint-satisfying
shop schedules
• management
analysis resource
scheduler (MARS)
schedule resources
for the space
transportation
system
ES in Production and Operations Management -- 4
Quality
management
• Statistical
process control
(SPC) deals with
quality control by
performing
repeated
inspection and
gather data
• data are
statistically
analysed to
suggest
modifications in
manufacturing
process and to
optimize the input
or anticipate
malfunctions
Inventory
control
• US Navy used a
retail inventory
management
(RIM) to assist
inventory
analysts at the
distribution
centre and in the
processing of
critical stock
items
Maintenance
• diesel-electric
locomotive
troubleshooting
aid/computeraided
troubleshooting
system
(DELTA/CATS1) was
developed by
General Electric
Company
• IVAN is an ES
used for
planning raw
material safety
stock
requirements
http://www.it.iitb.ac.in/~palwencha/ES/J_Papers/ES_APP_OPR.pdf
Forecasting
• EMEX is a
business
forecasting system
by Expert System
International
• NOSTRADAMUS
is another ES to
select an
appropriate
forecasting
technique for a
specific
forecasting task
• when a technique
is selected, the
system provides
suggestions to
manager for its
correct usage
ES in Production and Operations Management -- 5
Distribution
Purchasing
• different criteria includes transportation
costs, fixed costs for opening of
warehouses, processing cost to handle
different products at the warehouses and
scheduling
• a strategy to distribute the products to
customers as part of its approach
• national dispatch router (NDR) assist
in truck routing,
• responsibility for procuring materials,
supplies and services
• Lockheed’s expert system ASAP
simplifies procurement requests for
the end user and ensures
http://www.it.iitb.ac.in/~palwencha/ES/J_Papers/ES_APP_OPR.pdf
Benefits of Expert Systems
 no substitute for a knowledge worker's overall performance of the
problem-solving task. But these systems can dramatically reduce
the amount of work the individual must do to solve a problem,
and they do leave people with the creative and innovative aspects
of problem solving.
Some of the possible organizational benefits of expert systems are:
 can complete its part of the tasks much faster than a human expert
 error rate is low, sometimes much lower than the human error
rate for the same task
 make consistent recommendations
 can capture the scarce expertise of a uniquely qualified expert
 company can operate an ES in environments hazardous for humans
Limitations of Expert Systems
 Problems with knowledge acquisition
 Maintaining human expertise in organizations
 can't easily adapt to new circumstances (e.g. if they are presented with
totally unexpected data, they are unable to process it)
 can be difficult to use (if the non-expert user makes mistakes when
using the system, the resulting advice could be very wrong)
 no 'common sense' (a human user tends to notice obvious errors,
whereas a computer wouldn't)
Expert System Applications
Vandana Srivastava
Expert System in Agriculture

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help the farmers to have a well planning for before start to do
anything on their land
design an irrigation system for their plantation use
select the most suitable Crop variety or market outlet
diagnosis or identification of the livestock disorder
interpret the set of financial accounts
prediction for extreme weather such as thunderstorms and
frost
Rice crop doctor :
 developed by National Institute of Agricultural Extension
Management
 diagnose pests and diseases for rice crop and suggest
preventive/curative measures

AGREX
 developed by Center for Informatics Research and Advancement,
Kerala
 help the Agricultural field personnel give timely and correct advice
to the farmers.
 extensive use in fertilizer application, crop protection, irrigation
scheduling, and diagnosis of diseases in paddy and post harvest
technology of fruits and vegetables
 Farm Advisory System
developed by Punjab Agricultural
University, Ludhiana to support agribusiness management
 The conversation between the system and
the user is arranged in such a way that the
system asks all the questions from user
one by one on which it needs to give
recommendations

http://www.generation5.org/content/2005/Expert_System.asp
Expert System in Accounting
 Auditing
 used for screening and verifying transactions susceptible to fraud
 Airline and auto industries also use expert systems to verify the correct value of transactions prior to execution
 authorization and processing of claims
 Taxation
 tax treatment of stocks, investments and dividends
 guidance for corporate tax accrual and the planning process, value-added tax, tax preparation system, and
corporate tax planning for the oil and gas industry
 for international tax planning and optimization of international corporation tax position
 TAXMAN, TAXADVISOR , CORPTAX etc
 Financial Accounting
 cash flow evaluation, analysis of mergers, acquisitions and other investment decisions
 determination of financial status by ratios, leases,
 analysis of financial reports filed with the SEC
 CASHVALUE , FINANCIAL ADVISOR , PURPOOL etc
http://raw.rutgers.edu/miklosvasarhelyi/resume%20articles/chapters%20in%20books/c10.%20expert%20systems%20app%20in%
20act.pdf
Expert System in Accounting
 Personal Financial Planning
 Management Accounting
 International Business Machines' FAME
 FAME is used to assist customers with mainframe capacity decisions and
financial planning for the acquisition of mainframe computers by purchase,
conditional purchase or lease.
 Texas Instruments' capital investment system
 Exxon's revenue recognition, transfer pricing, cost flow and
accumulation, and evaluation of credit worthiness
http://raw.rutgers.edu/miklosvasarhelyi/resume%20articles/chapters%20in%20books/c10.%20expert%20systems%20app%20in%
20act.pdf
Expert Systems in Education
computer animation study
 used as a guide to developer to design 2D and 3D modelling
package
 web based Intelligent Tutoring System (ITS) for teaching
new internet technologies to high school teachers
 Leonardo tutoring system is a useful tool for teaching fault
analysis in power system
http://www.generation5.org/content/2005/Expert_System.asp
Expert Systems in Medicine
 Generating alerts and reminders
 attached to a monitor can warn of changes in a patient's condition
 scan laboratory test results or drug orders and send reminders or warnings through
an e-mail system
 Diagnostic assistance
 When a patient's case is complex, rare or the person making the diagnosis is simply
inexperienced, an expert system can help come up with likely diagnoses based on
patient data
 Therapy critiquing and planning
 can either look for inconsistencies, errors and omissions in an existing treatment plan,
or can be used to formulate a treatment based upon a patient's specific condition and
accepted treatment guidelines
 Image recognition and interpretation
 Many medical images can now be automatically interpreted, from plane X-rays through to
more complex images like angiograms, CT and MRI scans
 CaDet is a computer-based clinical decision support system for Early
Cancer Detection
 DXplain is a clinical decision support systems, developed at the Massachusetts General
Hospital
http://www.generation5.org/content/2005/Expert_System.asp
Expert Systems in Environmental Management
 Computer-Aided System For Environmental Compliance Auditing
 Geographic Information Systems (GIS) And Simulation Models
For Water Resources Management
 WaterWare: used
a graphical interface to provide interactive decision
support information for water resources planners and policy makers as it
is accessible in a local area network from a central server, and alternatively
through the Internet for remote clients
• NO Lab on Wednesday, October 7, 2015
• Minor 2 exam on October 8, 2015 between 5:30 – 6:30 pm at LH 316