Chapter 06
Transcript of Chapter 06
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Chapter 6:
Design of Expert Systems
Expert Systems: Principles and Programming, Fourth Edition
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Considerations when Building an Expert system
• We will describe the necessary prerequisites when building an expert system.
• The system should be a quality product.• The development should be cost effective and
timely.• Designing of expert systems of part of a general
field known as Knowledge Management (KM).
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Selecting the Appropriate Problem
• We need to answer the questions, “Why are we building this expert system?”.
– Intellectual Property Agreement must be considered
– Clearly identify the problem
– Clearly identify the expert
– Clearly identify the users
• We need to know the payoff – money, efficiency, etc.
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Selecting the Appropriate Problem
• What tools will be available to build the expert system?– Check the Web for applications in existence
– Know the language necessary to create a semantic net of relationships on which the system will be based
• How much will the expert system cost?– A function of people, resources, time, etc.
– How available is the knowledge?
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Figure 6.1 Project Management Tasks
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Stages in the Development
• How will the system be developed?– This will depend on the resources provided
Stages:
1. Feasibility Study – see if the project is feasible
2. Rapid Prototype – demonstrate ideas / impress
3. Refined System – verification by knowledge engineers
4. Field Testable – system tested by selected users
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Stages in the Development
5. Commercial quality system – validation / testing
6. Maintenance and evolution – repair bugs, enhance capabilities
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Other Considerations
• How will the system be delivered?– Should be considered in earliest stages of
development
– Integration with existing programs
• How will the system be maintained and evolve?– Performance is dependent on knowledge / expertise
– Performance must be maintained
– New knowledge will be acquired
– Old knowledge will be modified
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Figure 6.2 General Stages in the Development of an Expert System
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Errors in Development Stages
• Expert’s knowledge may be erroneous, propagating errors throughout the entire development process.
– Formal procedures may be necessary to certify expert
– Technique panels can scrutinize expert’s knowledge
– Focus groups can also be used
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Errors in the Development Stages
• Knowledge may not be properly communicated to knowledge engineer, or knowledge may be misinterpreted.
• Knowledge base may be corrupted by entering incorrect form of a rule or fact.
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Errors in the Development Stages
• Inference engine errors may result from errors in pattern matching, conflict resolution, and execution of actions.
• Inference chain errors may be caused by erroneous knowledge, semantic errors, inference engine bugs, incorrect specifications of rule priorities, and strange interaction among rules.
• Limits of ignorance – a problem common to all previous stages
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Figure 6.3 Major Errors in Expert Systems and Some Causes
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Software Engineering and Expert Systems
• Expert systems are products like any other software product and require good standards for development.
• Expert systems may have serious responsibilities – life and death.
• High standards are a necessity and can be measured by “mean time between failures”.
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Figure 6.4 Software Engineering Methodology
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Expert System Life Cycle
• Begins with the initial concept of the software and ends with its retirement from use.
• Expert systems require more maintenance because they are based on knowledge that is:– Heuristic– Experiential
• A number of life cycle models have been developed.
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Waterfall Model
• Each stage ends with a verification and validation activity to minimize any problems in that stage.
• Arrows go back and forth only one stage at a time.
• It is assumed that all information necessary for a stage is known.
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Figure 6.5 Waterfall Model of the Software Life Cycle
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Code-and-Fix Model
• Some code is written and then fixed when it does not work correctly.
• Usually the method of choice for new programming students in conventional and expert systems
• This eventually led to the do-it-twice concept where a prototype then a final system was built.
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Incremental Model
• This is a refinement of the waterfall and top-down-approach.
• The idea is to develop software in increments of functional capability.– Major increment – assistant colleague
expert– Minor increment – expertise w/in each level– Microincrement – add/refining individual rules
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Spiral Model
Each circuit of the spiral adds some functional capability to the system.
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Detailed Life Cycle ModelLinear Model
1. Planning Stage
The purpose of this stage is to produce a formal work plan for the expert system development – documents to guide and evaluate the development.
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Table 6.2 Planning Stage Tasks
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Linear Model
2. Knowledge Definition
The objective of this stage is to define the knowledge requirements of the expert system, which consists of two main tasks:
• Knowledge source identification and selection• Knowledge acquisition, analysis, and extraction
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Table 6.3 Knowledge Source / Identification
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Table 6.4 Knowledge Acquisition, Analysis, and Extraction Tasks
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Linear Model
3. Knowledge Design
The objective is to produce the detailed design for an expert system and involves:
• Knowledge definition
• Detailed design
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Table 6.5 Knowledge Definition Tasks
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Table 6.6 Detailed Design of Knowledge Tasks
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Linear Model
4. Code and Checkout
This begins the actual code implementation
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Table 6.7 Code and Checkout Tasks
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Linear Model
5. Knowledge Verification
The objective here is to determine the correctness, completeness, and consistency of the system.
• Formal tests
• Test Analysis
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Table 6.8 Formal Test Tasks of Knowledge Verification Stage
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Test Analysis Tasks
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Linear Model
6. System Evaluation
This stage is for summarizing what has been learned with recommendations for improvements and corrections.
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Table 6.10 System Evaluation Stage Tasks
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Figure 6.7 Linear Model of Expert System Development Life Cycle
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Summary
• In this chapter, we have discussed a software engineering approach to the construction of an expert system.
• Principles about good interviewing techniques were mentioned.
• Now that expert systems are widely used, they must be quality products due to the sensitive nature of their applications.
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Summary
• Factors to be considered in the design of expert systems include problem selection, cost, and payoff.
• Both managerial and technical aspects must be considered.
• By consistently following the outlined “life cycle” it should be possible to construct quality software.