expert systems infsy 540 dr. ocker. expert systems n computer systems which try to mimic human...
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Expert Systems
Infsy 540
Dr. Ocker
Expert Systems
computer systems which try to mimic human expertise
produce a decision that
does not require judgment assistants to decision makers rather
than substitutes for them
Expert Systems & AI
artificial intelligence (AI) - group of technologies that attempt to emulate certain aspects of human behavior, such as reasoning and communicating
Expert systems are the most important product of AI research to date
Systems and Types of decisions
Traditional computing systems deal with routine/structured problems
e.g. payroll system is structured - – can write down the formulas– calculations are very repetitive– requires little judgment– no creativity
Systems and Types of decisions
DSS deal with semi-structured problems
– processing less well-defined– no judgment inside system– decision maker applies assumptions to
problem, system calculates results– output must be interpreted by decision
maker
Example Problem
Use DSS to help predict effects of
early retirement plan vary assumptions about plan to forecast
financial impact system produces an answer for each set of
assumptions user judges the validity of the assumptions
and the value of the answer
Systems and Types of decisions
Expert System deals with semi-structured problems
– judgment incorporated into system– system produces a solution– system can “explain” how it reached its
conclusions
Example Problem
Use Expert System to develop an early retirement plan
system contains decision criteria (“rules”) established by decision makers
uses rules to frame a retirement program can trace rules used in developing the
retirement program
What is an expert system?
A knowledge-based system: provides specific knowledge about a
narrow problem domain knowledge stored in the knowledge base system uses knowledge and an
inferencing (reasoning) procedure to solve problems that would otherwise require human competence or expertise.
To use an expert system(1) gather input problem variables and criteria
(2) consult computerized base of knowledge
(3) system reasons out an answer
ES often assistants to decision makers and not substitutes for them
i.e. use ES to help DM with part of a larger problem
Example - Internist/Caduceus one of most knowledge-intensive expert
systems covered 85% of internal medicine - included
information on 500 diseases and more than 100,000 symptomatic associations
user inputs given patient information system uses its knowledge base to identify
a disease and recommend treatment
Components of expert systems
1) knowledge base
2) inference engine
3) knowledge acquisition module
4) explanatory interface
1. knowledge base
structure for saving facts and rules relevant to a specific application (problem domain)
2 types of info: (1) book knowledge about a domain (2) heuristic knowledge - rules of thumb
used by human experts who work in the domain
2. inference engine
that portion of the sw that contains the reasoning methods
expert system asks questions of the user to get info. it needs.
then inference engine, using knowledge base, searches for the sought-after knowledge
returns a decision/ recommendation to user
3. knowledge acquisition module
used by expert to enter rules or facts into the system
4. explanatory interface
system shows the trail of reasoning it used to reach a decision
explains the facts it used what rules it applied and in what order
2 environments of ES
Development Environment Consultation Environment
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Structure of an Expert SystemStructure of an Expert System
Consultation Environment(Use)
Development Environment(Knowledge Acquisition)
User Expert
User Interface
Inference Engine
ExplanationFacility
Working Memory
Facts ofthe Case
Recommendation,Explanation
Facts ofthe Case
KnowledgeEngineer
KnowledgeAcquisition
Facility
KnowledgeBase
Domain Knowledge(Elements ofKnowledge Base)
Knowledge Representation
knowledge represented in expert systems in variety of ways, including:
rules case-based reasoning
Rules
most common way to represent knowledge in expert system
rules called heuristics - obtained from experts number of rules determines complexity of
system rules most appropriate when knowledge can
be generalized into specific statements.
example of heuristic rule
if good customer
and credit requested < $5,000
and loan term < 1 year
then grant credit
Case-based reasoning
system draws inferences by comparing a current problem (case) with hundreds/thousands of similar past cases.
best used when situation involves too many nuances and variations to be generalized into rules
example of case-based reasoning
Sharon , 35 yrs. old, entered hospital with potentially fatal respiratory disease. Her vital stats. and medical history entered into expert system. System drew on records of over 17,000 previous intensive-care patients to predict whether Sharon would live or die.
example of case-based reasoning
First prediction - 15% chance of dying. Stats. entered daily - system compared
her progress to base of previous cases. 2 weeks later - prediction soared to 90%
chance of dying - alerted physicians and nurses to take corrective action.
How expert systems work
knowledge representation method used to organize knowledge production rules - most common
method– consist of an IF part and a THEN part
IF condition THEN action
How expert systems work
Inference Engine controls the order in which the
production rules are applied to solve the problem and
resolves conflicts if more than one rule applies– this is the "reasoning" process
How solution process works
user presents a set of facts describing a situation to the expert system.
inference engine compares facts of the case to the knowledge base
system then gives a recommendation asks for more information if needed
Inferencing strategies for rule-based system Forward chaining
– data driven– inferencing moves from facts of case to a
goal (conclusion) Backward chaining
– inferencing moves from a possible goal state to premises that would satisfy it
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Inferencing StrategiesInferencing Strategies
InputData
Few Items(For Example, UserSpecifications fora Computer System)
Conclusion(Goals)
Many Possibilities(For Example, a ComputerConfiguration)
(a) Forward Chaining: IF - Part Matches Shown
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Inferencing Strategies (Cont.)Inferencing Strategies (Cont.)
InputData
Extensive;Much of the DataObtained by theSystem Queryingthe User (ForExample,Investor’s Profile)
Conclusion(Goals)
Few Possibilities(Known in Advance((For Example, Investment Options)
(b) Backward Chaining: THEN - Part Matches Shown
Expert system shells Most common way to develop ES shell is an expert system without the
knowledge base– includes inference engine, user interface,
explanation and knowledge acquisition pieces– generic shells - used to develop ES in any domain– domain-specific shells - incomplete specific ES;
require much less effort - already includes many rules
©The McGraw-Hill Companies, Inc., 1998
11- 4
Irwin/McGraw-Hill
Expert Systems TechnologiesExpert Systems Technologies
Higher-LevelProgramming
Language
Expert SystemDevelopmentEnvironment
Generic Shell
Domain-SpecificShell
Specific ExpertSystem
GreaterFlexibility
GreaterEase of Use
GreaterComplexity ofProblem andEnvironment
Development of ES
Prototype-oriented iterative development
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11- 4
Irwin/McGraw-Hill
Development & Maintenance of ESsDevelopment & Maintenance of ESs
Problem Identification andFeasibility Analysis
System Design and ESTechnology Identification
Development ofPrototype
Testing and Refinementof Prototype
Is the PerformanceSatisfactory? Complete and
Field the ES
Maintain ES
No
Yes
ES Ready for Use
Benefits of Expert Systems
Quick consistent low error rate capture scarce expertise
Limitations of Expert Systems
Must have agreement among experts must have a willing expert most only support operational-level
tasks use can weaken human expertise
Appropriate Problem Space for Expert System
1.technical disciplines with large bodies of complex information
2.situations that require decisions
3.an expert can articulate the decision rules s/he uses