1. ICT619 Intelligent Systems Topic 2: Expert Systems

2. Expert Systems PART A (last week)
Introduction Applications of expert systems
Structure of an expert system
An example rule base
Reasoning in a rule-based expert system
Reasoning using forward and backward chaining
Dealing with uncertainty
PART B
Frame-based expert systems
Developing expert systems
Advantages and disadvantages of expert systems
Case studies
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3. PART B Frame-based expert systems Developing expert systems
Advantages and disadvantages of expert systems
Case Studies
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4. Frame-based expert systems – what is a frame? (Negnevitsky, 2005)
A frame is a data structure with typical knowledge about a particular object or concept.
Frames were first proposed by Marvin Minsky in the 1970s.
Each frame has its own name and a set of attributes associated with it. Carrier, Name, Flight, Date, … Gate are slots in the frame Boarding pass.
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5. Frame-based expert systems – what is a frame? (cont’d)
Frames provide a natural, concise way to represent knowledge.
Frames are an early application of object-oriented programming for expert systems.
A knowledge engineer refers to, what is an equivalent of an object in OOP, as a frame
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6. Object-oriented Programming
Object-Oriented Programming (OOP) is a programming method that uses instances (known as objects) of different classes of entities for problem solving
A class in OOP defines an abstraction of entities with a common set of characteristics (attributes) and behaviours
An example of an attribute is a variable type such as a string. An entity ('message') might have an attribute ('type::string') with value ("Hello to everybody listening!"). Or an entity ('cat') might have an attribute ('colour') with value ('white')
Entities often have a list of attribute-value pairs
Behaviours are represented by procedures applicable to objects of that class (called methods in OOP).
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7. Frames as a knowledge representation technique
The concept of a frame is defined by a collection of slots.
Each slot describes a particular attribute or operation of the frame.
Slots are used to store values.
A slot may contain a default value, a pointer to another frame, a set of rules or procedure by which the slot value is computed.
Such procedures are executed only when the slot in which they sit is accessed
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8. Classes and instances A group of similar objects belong to a class
The word frame may refer to a particular object or instance of a class
To be more precise, a class-frame describes a group of objects with common attributes.
Instance-frame refers to a particular object
- Animal, person, car and computer are all class-frames.
- Mammal, Jane Doe, Toyota, Dell Inspiron are all instance-frames.
Frame-based systems are characterized by inheritance.
Inheritance is the process by which all characteristics of a class-frame are assumed by an instance-frame as well as any sub-classes.
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9. A Class Frame
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10. Two Instance Frames
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11. Methods and Demons
Expert systems are required not only to store the knowledge but also to validate and manipulate this knowledge.
To add actions to frames, a procedures is associated with a frame attribute that is executed whenever requested.
Frame procedures are known as methods and demons
Demons are limited to IF-THEN statements, but methods can be more complex.
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12. Methods and Demons (cont’d)
Most frame-based expert systems use two types of methods: WHEN NEEDED and WHEN CHANGED
A WHEN NEEDED method is executed when information associated with a particular attribute is needed for solving the problem, but the attribute value is undetermined.
A WHEN CHANGED method is executed immediately when the value of its attribute changes.
The following simple example illustrates the use of WHEN CHANGED methods in a class at run-time.
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13. An ES to assist a loan officer in evaluating credit requests from small business ventures
A credit request is to be classified into one of three categories, “Give credit”, “Deny credit” or “Consult a superior”.
When a loan officer provides a qualitative rating of the expected yield from the loan, the expert system:
Compares the business collateral with the amount of credit requested
Evaluates a financial rating based on a weighted sum of
the business’s net worth to assets
last year’s sales growth
gross profit on sales and
short-term debt to sales
Determines a category for the credit request.
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14. Input display for the request selection
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15. The class Action Data and WHEN CHANGED methods
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16. Class Request
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17. Instances of the Class Request
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18. Inference in a frame-based system
Most frame-based expert systems allow the use of a set of rules to evaluate information contained in frames.
Similar to a goal-driven rule-based system, the inference engine in a frame-based system also searches for the goal.
But rules in such a system play an auxiliary role.
Frames represent here a major source of knowledge, and both methods and demons are used to add actions to the frames.
Thus, the goal in a frame-based system can be established either in a method or in a demon.
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19. Example: Evaluation of credit request
The expert system is expected to begin the evaluation when the user clicks the Evaluate Credit pushbutton on the input display.
This pushbutton is attached to the attribute Evaluate Credit of the class Credit Evaluation.
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20. The Credit Evaluation class, WHEN CHANGED and WHEN NEEDED methods
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21. The rule-base
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22. Example: Evaluation of credit request (cont’d)
The WHEN NEEDED method attached to the attribute Evaluation is used to establish its value.
The inference engine executes this method when it needs to determine the value of Evaluation.
If based on the set of rules provided for credit evaluation, the inference engine cannot establish the value of the attribute Evaluation, the attribute Evaluation receives the value Consult a superior.
The task of developing a frame-based ES can be difficult for the knowledge engineer due to the complexity of inheritance.
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23. Developing an expert system - issues to be considered
Does the task require knowledge available from human experts?
Are human experts too expensive and scarce?
Is the problem domain well-structured?
Problem domain must be well-defined and formalised.
Amount of knowledge required to solve such problems should not be "too large".
No commonsense reasoning (that can't be reduced to rules)
Can the problem be solved by traditional computing methods? Numerical, statistical or operations research methods, if applicable, are likely to do better than heuristic solution
Are cooperative, articulate and enthusiastic human experts available?
Expert can articulate reasoning process with a high degree of confidence
Expert convinced of the benefit of the ES, and does not feel threatened
Expert has the time
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24. Developing an expert system - issues (cont’d)
Adequate management support (expert's boss has to be on board)
How big and complex is the problem?
Available technology must be able to handle required knowledge base efficiently
Is development cost justified by returns?
Is the solution found likely to remain valuable for several years to come?
The cost (and availability) of the human expert needs to be compared with the cost of developing the ES
Cost can be reduced by development tools like ES shells
Will there be adequate infrastructure and management support for ES maintenance in future?
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25. Developing an expert system - the people
The knowledge group (knowledge engineer + the domain expert)
The developer group
The user group
Knowledge engineer
Usually a system analyst with background in AI
extracts knowledge from domain expert and formalizes it into a knowledge base
decides on the
knowledge representation scheme
strategies for reasoning and searching the knowledge base
design of the user interface.
hardware and software.
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26. Knowledge engineer (cont’d)
May not initially have any experience of the ES problem domain (but tends to learn some during the project)
Sessions with the domain expert are the usual method used for knowledge gathering
Machine learning techniques such as decision trees (subject of a later topic) can also be used
Time taken by knowledge acquisition process – approx. 60 to 70% of overall ES development time
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27. The Developer and User groups
The developer group
Implements the expert system in close cooperation with the knowledge group and the user group.
Either programs the system from scratch or makes use of existing software products like an expert system shell
The user group
Can be a regular user, eg, an employee of the organization
May be an irregular user such as bank customer
Needs and characteristics of both categories of users need to be taken into account, particularly for the user interface design.
The user must feel confident and happy with the system
So a usability evaluation should be conducted with these people
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28. The expert system development cycle
Development strategy
Early prototyping and incremental development
Mistakes found during development lead to correction and addition to the knowledge base
Steps usually followed:
Knowledge engineer gains familiarity with the problem domain
Knowledge engineer and domain expert start knowledge extraction (knowledge elicitation) process
Difficult because
Experts don't know how they know
Experts may disagree
Experts may be wrong, or fuzzy on details
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29. The expert system development cycle (cont’d)
Knowledge engineer decides on representation scheme, search strategy, user interface
Prototype built
Knowledge engineer and domain expert test prototype, corrects, refines and redesigns if necessary
Final version built and evaluated.
ES knowledge base must be continuously updated by adding, deleting or changing rules to keep up with new knowledge and changed circumstances
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30. Expert system shells
A general expert system development product for the market
An expert system shell has all components of an expert system -except the knowledge-base
User
Interface:
- Menu-driven
- GUI
- Natural language
Knowledge-base editor
Inference engine
Explanation sub-system
Knowledge-base
Working memory
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31. Expert system shells (cont’d)
Good shells also provide facilities for communication with external sources including
Database management systems
Spreadsheets
Graphics packages.
Shell selected must match reasoning process (goal driven or data driven) characterising given problem domain
Other features to be considered
knowledge representation scheme and the knowledge base editor
user interface
explanation capabilities.
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32. Testing expert systems
Correctness of ES can not be guaranteed due to its heuristic nature
Some test strategies
Use of historical data Data on existing cases fed to the system, and the outputs analysed
Inconsistency of human decisions taken into account in case of any difference between the data and ES
ES against Expert
ES tested against the expert by presenting both with hypothetical or actual cases Expert’s reasoning compared with that of the ES to make sure both arrived at the same conclusion for the same reasons
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33. Testing expert systems (cont’d)
Field tests
Users in the field use the system for a given period of time
Favourable results in the field add to ES validity
Serious problems in field tests indicate need for further improvement of the design
Turing test
ES and a human expert are subjected to blind user evaluation
User’s failure to differentiate between answers produced by the two enables the ES to pass
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34. Some limitations of expert systems
Knowledge acquisition remains the major bottleneck in applying ES technology to new domains.
Not adaptive Knowledge engineer responsible for revising and maintaining system.
Evaluating expert system correctness is difficult
Explanation of an outcome in terms of the reasoning process can be time consuming for large amounts of data
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35. Some limitations of expert systems (cont’d)
Self-explanatory individual rules, but difficult to relate to overall strategy due to lack of hierarchy
Maintenance and extension of a rule base can be difficult for a relatively large rule base (beyond 100 rules).
ES are not as compact as neural network and genetic algorithm systems. This makes them harder to embed in other systems, as the inference engine and working memory must be part of the system at run-time.
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36. Case Studies
Case 1: An Expert System for Pattern Directed Data Mining of Point-Of-Sale Data (see Dhar & Stein pp.244-).
Objective
Supermarkets need to know how different product categories performed over certain periods of time in particular regions and nationally
Some experts are able to detect significant trends in these data – what is happening in the market place?
A.C. Nielsen wanted to create an expert system with this expertise and make it available to its sales reps
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37. Case 1: An Expert System for Pattern Directed Data Mining
Requirements and constraints
Enable reps to put together a clear and unambiguous story for their customer
Use vocabulary commonly used by sales reps
Able to work with Nielsen’s databases
Be distributable on low-end PCs to sales reps across the country
Reports able to be produced under five minutes\
High explainability to enable reps to construct stories
Six months development time – Nielsen wanted to leap frog rival
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38. Case 1: An Expert System for Pattern Directed Data Mining (cont’d)
Objectives and constraints were clear, but not so outputs – eg, use of imprecise linguistic terms
Decision:- Produce outputs to focus attention on areas of unusual activity (eg, abnormal shift in sales volume, market share, price) accompanied by associated factors to explain observation
Problem domain not complex but universally (across all products, regions, seasons) applicable principles were difficult to identify
Decision:- System to be flexible with parameterizable rules to suit specific circumstances
Market data was of high quality
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39. Case 1: An Expert System for Pattern Directed Data Mining (cont’d)
Implementation
The ES SPOTLIGHT was implemented using a rule based control engine written in C
Sales database and rule base were 15 segments
Loaded one segment at a time due to memory constraints
Results of running each rule base on each segment of data integrated into final report
Results
Completed in 1991, SPOTLIGHT proved highly popular with sales reps and their clients
Demonstrated feasibility of expert systems based on conventional technology - written in C for a PC platform, rather than in an AI language such as LISP for AI workstation
First (?) successful data mining application of an expert system

40. Case 1: An Expert System for Pattern Directed Data Mining (cont’d)
Some limitations
Gave limited analyses, and not interactive as a decision support tool
Provided insight into what was happening in the market place, but not what action to take to follow up
Parameterizing rules was not a good long-term solution to making rules sensitive to market context
Nielsen developed an object-oriented version called Opportunity Explorer (OE) with a Windows-based GUI interface
Rules in OE could be attached to classes of consumer goods, so analysis became automatically sensitive to context
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41. REFERENCES
AI Expert, October 1991 – presents applications of expert systems
Dhar, V., & Stein, R., Seven Methods for Transforming Corporate Data into Business Intelligence., Prentice Hall 1997, Ch 7
Giarratano, J., & Riley, G. Expert Systems Principles and Programming, Thomson Course Technology, 2005.
Negnevitsky, M. Artificial Intelligence A Guide to Intelligent Systems, Addison-Wesley (Source of slides on frame-based systems used in this presentation )
Zahedi, F., Intelligent systems for Business, Wadsworth Publishing, Belmont, California, 1993.
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