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CHAPTER 14
Intelligent Systems Development
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Intelligent Systems Development
Overview of the expert system development process
Performed differently depending on the – Nature of the system being constructed– Development strategy – Support tools
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Prototyping: ES Development Life Cycle
6 phases
Nonlinear process
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Phases
I. Project Initialization
II. Systems Analysis and Design
III. Rapid Prototyping
IV. System Development
V. Implementation
VI. Postimplementation
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Phase I: Project Initialization
(Table 14.1) Problem Definition Need Assessment Evaluation of Alternative Solutions Verification of an Expert Systems Approach Feasibility Study Cost-benefit Analysis Consideration of Managerial Issues Organization of the Development Team
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Problem Definition andNeed Assessment
Write a clear statement and provide as much supporting information as possible
Conduct a formal needs assessment to understand the problem
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Evaluation ofAlternative Solutions
Using experts Education and training Packaged knowledge Conventional software Buying knowledge on the Internet
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Verification of an Expert Systems Approach
Framework to determine problem fit with an ES (Waterman [1985])
1. Requirements for ES Development
2. Justification for ES Development
3. Appropriateness of ES
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1. Requirements for ES Development (all necessary)
1. Task does not require common sense2. Task requires only cognitive, not physical, skills3. At least one genuine expert, willing to cooperate4. Experts involved can articulate their problem-
solving methods5. Experts involved can agree on the knowledge and
the solution approach
(continued)
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6. Task is not too difficult7. Task well understood and defined clearly8. Task definition fairly stable9. Conventional (algorithmic) computer solution
techniques not satisfactory10. Incorrect or nonoptimal results generated by the
ES can be tolerated11. Data and test cases are available12. Task's vocabulary has no more than a couple of
hundred concepts
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2. Justification for ES Development (Need at least one)
1. Solution to the problem has a high payoff2. ES can preserve scarce human expertise, so it will not be
lost3. Expertise is needed in many locations4. Expertise is needed in hostile or hazardous environments5. The expertise improves performance and/or quality6. System can be used for training7. ES solution can be derived faster than a human8. ES is more consistent and/or accurate than a human
Benefits Must Exceed Costs
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3. Appropriateness of the ES(Consider 3 Factors)
1. Nature of the problem: Symbolic structure and heuristics
2. Complexity of the task: Neither too easy nor too difficult for a human expert
3. Scope of the problem: Manageable size and practical value
Problem Selection is a Critical Factor
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Feasibility Study (Table 14.2)
Economic (financial): Should we build it?
Technical: Can we build it?
Organizational: If we build it, will they come?
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Cost-Benefit Analysis Determines project viability
Often very complicated
Difficult to predict costs and benefits– many are qualitative
Expert systems evolve constantly
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Cost-Benefit Analysis -Complicating Factors
Getting a handle on development costs– Consider (and revise) system scope– Estimate time requirements
Evaluating benefits– Some intangible– Hard to relate specifically to the ES– Benefits result over time– Not easy to assess quantity and quality– Multiplicity of consequences hard to evaluate
• (goodwill, inconvenience, waiting time and pain)
Key: identify appropriate benefits
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When to justify (very often!)– At the end of Phase I– At the end of Phase II– After the initial prototype is completed– Once the full prototype is in operation– Once field testing is completed (prior to deployment)– Periodically after the system is in operation (e.g.,
every six or twelve months) Reality checks– How to justify?
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Consideration of Managerial Issues
Selling the project Identifying a champion Level of top management support End user involvement, support, and training Availability of financing Availability of other resources Legal and other potential constraints
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Organizing Development Team
Team varies with the phases– Typical development team – Expert– Knowledge engineer – IS person
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Team May Also Include Vendor(s) User(s) System integrator(s)
Cooperation and communication required!!!
Possible functions and roles in an ES team (Table 14.4)
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Important Players
Project champion
Project leader
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Phase II: Systems Analysis and Design
Conceptual design and plan Development Strategy Knowledge sources Computing resources
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Conceptual Design General Idea of the System General capabilities of the system Interfaces with other CBIS Areas of risk Required resources Anticipated cash flow Composition of the team Other information for detailed design later Determine the development strategy after design is
complete
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Development Strategy andMethodology
In-house development Outsourcing Blended approach
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In-house Development
End-user computing Centralized computing End-user computing with centralized
control High-technology islands Information centers
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Outsourcing
Hire a consulting firm Become a test site Partner with a university Join an industry consortium Buy into an AI firm
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Blended Approach
Mix both
In-house Outsourcing
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Selecting an Expert
Experts Expertise is based on experience and can
be expressed by heuristics Selection Issues– Who selects the expert(s)?– How to identify an expert– What to do if several experts are needed– How to motivate the expert to cooperate
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Software Classification:Technology Levels
(Figure 14.2)
Expert System Applications (Specific ES)
Shells Hybrid Systems
Support Tools, Facilities,and Construction Aids
Programming Languages
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Software Classification:Technology Levels
(Figure 14.2) Specific expert systems Shells Support tools Hybrid Systems (environments) Programming languages NEW– Object-oriented Programming (OOP) – Internet/Web/Intranet-based Tools
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Building Expert Systemswith Tools
1. The builder employs the tool's development engine to load the knowledge base
2. The knowledge base is tested on sample problems using the inference engine
3. The process is repeated until the system is operational
4. The development engine is removed and the specific expert system is ready for its users (using a separate runtime (executable) component of the tool)
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Shells and Environments Expert systems components
1. Knowledge acquisition subsystems2. Inference engine3. Explanation facility4. Interface subsystem 5. Knowledge base management facility 6. Knowledge base
Shell: Components 1-5 (Figure 14.3)
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Shell Concept for Building Expert Systems (Figure 14.3)
Knowledgebase
(rules)
Consultationmanager
Knowledge baseeditor and debugger
Inferenceengine
Knowledge basemanagement facilities
Explanationprogram
Shell
Tip of theiceberg
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Rule-Based Shells Exsys InstantTea XpertRule KBS G2 Guru K-Vision CLIPS JESS
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Domain-Specific Tools
Designed to be used only in thedevelopment of a specific area
Diagnostic systems Shells for configuration Shells for financial applications Shells for scheduling
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Development Environments
Support several different knowledge representations and inference methods
Examples– ART-IM– Level5 Object– KAPPA PC
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Software Selection
Complex problem– Frequent technology changes – Many criteria
First check out:– “PC AI Buyer’s Guide”– “Expert Systems Resource Guide” in AI Expert– Newsgroup FAQs on ES
Major Issues in Selecting ES Development Software (Table 14.7)
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Shells vs. Languages
How to select which Try the Analytic Hierarchy Process
(Chapter 5)
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Hardware Support Software Choice Usually Depends
on the Hardware
PCs Unix workstations Web servers AI workstations Mainframes
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Phase III: Rapid Prototypingand a Demonstration Prototype Build a small prototype
Test, improve, expand
Demonstrate and analyze feasibility
Complete design
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Rapid Prototyping Crucial to ES development Small-scale system Includes knowledge representation Small number of rules For proof of concept
Rapid prototyping process (Figure 14.4)
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Phase IV: System Development Develop the knowledge base Define the potential solutions Define the input facts Develop an outline Draw a decision tree Create a knowledge map (matrix) Create the knowledge base Test, evaluate, and improve (knowledge base) Plan for integration
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Use a System Development Approach
Continue with prototyping (yes)
Use the structured life cycle approach (rare)
Do both (rare)
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Develop the Knowledge Base Acquire and Represent
Knowledge Appropriately Define potential solutions Define input facts Develop outline Draw decision tree Map matrix Create knowledge base
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Test, Validate and Verify, and Improve
Test and evaluate the prototype and improved versions of the system– In the lab– In the field
– Initially - evaluate in a simulated environment
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Modified Turing Test: Compare ES performance to an accepted criterion (human expert's decisions)
Experimentation Iterative Process of evaluation: – Refine the ES in the field– Use new cases to expand the knowledge base
Validation - determine whether the right system was built
• Does the system do what it was meant to do and at an acceptable level of accuracy?
Verification - confirms that the ES has been built correctly according to specifications
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Phase V: Implementation
Acceptance by users Installation, demonstration, deployment Orientation, training Security Documentation Integration, field testing
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ES Implementation Issues Acceptance by the user Installation approaches Demonstration Mode of deployment Orientation and training Security Documentation Integration and field testing
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Phase VI: Postimplementation
Operations
Expansion: maintenance and upgrades
Includes periodic evaluation
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The environment changes More complex situations arise Additional subsystems can be added (e.g.,
LMS)
Upgrading (Expansion)
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Maintenance costs versus benefits Is the knowledge up-to-date? Is the system accessible to all users? Is user acceptance increasing? (feedback)
Evaluation (Periodically)
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The Future of Expert Systems Development Processes
Expect Advances In
Flexible toolkit capabilities, including inferencing hybrids Improved languages and development systems Better front ends to help the expert provide knowledge Improved GUIs via Windows-based environments Further use of intelligent agents in toolkits Better ways to handle multiple knowledge representations
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Use of intelligent agents to assist developers Use of blackboard architectures and intelligent agents
in ES Advances in the object-oriented approach, for
representing knowledge and ES programming Improved and customized CASE tools to manage ES
development Increased hypermedia use and development (Web) Automated machine learning of databases and text