Download Chapter 11: Artificial Intelligence & Expert Systems

Chapter 11
Artificial Intelligence and
Expert Systems
Artificial Intelligence (AI)
The ability to mimic or duplicate the functions
of the human brain
AI Systems: The people, procedures, hardware,
software, data, telecommunications, &
knowledge needed to develop computer
systems & machines that demonstrate the
characteristics of intelligence
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Intelligent Behavior
Learn from experience
Apply knowledge acquired from experience
Handle complex situations
Solve problems when important information is
missing
Determine what is important
React quickly & correctly to a new situation
Understand visual images
Process & manipulate symbols
Be creative & imaginative
Use heuristics
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Major Branches of AI (Figure 11.2)
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Expert Systems
Can explain their reasoning or suggested decisions
Can display intelligent behavior
Can draw conclusions from complex relationships
Can provide portable knowledge
Expert System Shell: A collection of software
packages & tools used to develop expert systems
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Limitations of Expert Systems
Not widely used or tested
Limited to relatively narrow problems
Cannot readily deal with “mixed” knowledge
Possibility of error
Cannot refine own knowledge base
Difficult to maintain
May have high development costs
Raise legal & ethical concerns
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Expert Systems’ Capabilities (Figure 11.5)
Explore impact of strategic
goals
Impact of plans on resources
Integrate general design
principles & manufacturing
limitations
Provide advise on decisions
Monitor quality & assist in
finding solutions
Look for causes & suggest
solutions
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When to Use Expert Systems
Provide a high potential payoff or significantly
reduced downside risk
Capture & preserve irreplaceable human expertise
Provide needed expertise (consistently) at a number
of locations at the same time or in a hostile
environment that is dangerous to human health
Provide expertise that is expensive or rare
Develop a solution faster than human experts can
Provide expertise needed for training & development
to share the wisdom of human experts with a large
number of people
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Expert Systems’ Components (Figure 11.7)
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More Definitions
Fuzzy Logic: Mathematics/computer science area
that allows shades of gray & does not require
everything to be simply yes/no, or true/false
Rule: A conditional statement that links given
conditions to actions or outcomes
Backward Chaining: A method of reasoning that
starts with conclusions & works backward to the
supporting facts (deductive)
Forward Chaining: A method of reasoning that
starts with the facts & works forward to the
conclusions (inductive)
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Yet More Definitions
Explanation Facility: Allows a user or decision
maker to understand how the expert system
arrived at its conclusions/results
Knowledge Acquisition Facility: Provides a
convenient, efficient means of capturing & storing
the components of the knowledge base
Domain: The limited area of knowledge addressed
by the expert system
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Expert Systems Development
(Figure 11.10)
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Participants in Expert Systems
Development & Use (Figure 11.11)
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Evolution of Expert Systems Software
(Figure 11.12)
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Advantages of Expert Systems
Easy to develop and modify
The use of satisficing
The use of heuristics
Development by knowledge engineers & users
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Expert Systems Development
Alternatives (Figure 11.13)
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Applications of Expert Systems & AI
Granting credit
Information management & retrieval
AI & expert systems embedded in products
Plant/facility layout
Hospitals & medical facilities, including diagnostic tools
Help desks & assistance
Employee performance evaluation
Loan analysis
Virus detection
Repair & maintenance
Shipping
Marketing
Warehouse optimization
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Chapter 11 Case
Use of fuzzy logic to predict length of patient
hospital stay, page 514
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