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Lecture5 Expert Systems And Artificial Intelligence
Nov 22, 2014
- 1. Expert Systems and Artificial Intelligence Modified from Marakas 2003 Reading: Chapter 7 the expert is the one who has made the most mistakes in the domain of interest
- 2. The Concept of Expertise
- Expertise: extensive knowledge in a narrow field
- Gained by making mistakes
- Arrive at solutions through logic
- Logic establishes the True/Falsity of assertions
- IF-THEN to arrive at conclusions
- Derive new information from existing to arrive at conclusions
- Deductions and inference are used to establish how well facts fit a scenario
- Experts use this information fit to arrive at their decisions
- 3. Example of information fit
- Known information
- John is Sams son
- John is the eldest child
- Mary is Sams daughter
- John and Marys mother is called Anna
- Sam has been married to Anna for 50 years
- Derived information
- If John is Sams son, THEN John must be a boy
- If Sam and Anna have been married for 50 years, THEN John and Mary are their children by either birth or adoption
- 4. Expert Sytems & Artificial Intelligence
- Expert systems: a computer application that employs a set of rules based on human knowledge to solve problems that require human expertise
- Imitates reasoning of experts on information fit
- A non-expert simulates a dialog with an expert to solve complex problems
- Artificial Intelligence: practical mechanisms that enable computers to simulate the human reasoning process
- Interface of compute science/cognitive psychology
- the study of how to make computers do things which humans do better the Turing Test?
- 5. The Intelligence of Artificial Intelligence
- How do people reason? (the realm of cog. psych.)
- Categorization
- Can reason at levels of abstraction
- Rules derived from relationships among categories
- Specific Rules
- Heuristics
- Less rigorous rules of thumb
- Past Experience
- Use a similar scenario to model new one (benefit of 20/20 hindsight)
- Expectations
- Frequency, pattern recognition
- 6. How Do Computers Reason?
- Rule-based reasoning: IF-THEN statements represent knowledge encoded as rules
- If TRUE rule is instantiated, otherwise ignored
- Pattern recognition: detecting sounds, shapes or long sequences
- Analogous to Expectation, similar conditions
- Frames: Object-oriented approach of creating hierarchical data structures analogous to categorisation databasey!!
- Case-based reasoning: adapting previous solutions to a current problem
- 7. Types of IF Then
- Inferential
- If premise THEN conclusion
- If snowing THEN drive with caution
- Procedural
- If situation THEN action
- If Average grade =A THEN award 1 st class degree
- Declarative
- If Antecedent THEN consequent
- If student has mitigating circumstances THEN award incomplete grade
- 8. The CBR Cycle
- 9. Other Forms of AI
- Machine learning neural networks and genetic algorithms (learning mechanisms)
- Automatic programming mechanisms that generate a program to do a specific task (allows non-programmers to program)
- User describes inputs and generates a program
- Artificial life attempts to recreate biological phenomena within computer-based systems
- As opposed to dissecting frogs!
- Transfer to design of engineering projects (software, spacecraft, robotics etc)
- 10. Neural Network Train it? supervised Or just let it get on with it? - unsupervised
- 11. Genetic Algorithm mutation crossover Used in scheduling (timetabling?), design, marketing
- 12. The Concept and Structure of Expert Systems
- Basic structure of an ES follows the generic structure of a DSS
- User interface, Knowledge base, inference engine
- The knowledge base is specific to a particular problem domain associated with the ES
- The main difference between an ES and DSS is that the ES contains knowledge acquired from experts in the application domain
- 13. Common Expert System Architecture User Knowledge Engineer User Interface Inference Engine Knowledge Base User Environment KE Tool Kit KE Interface Development Environment Organization Systems Interface
- 14. The User Interface in an ES
- Design of the UI focuses on human concerns such as ease of use, reliability and reduction of fatigue
- Critical to its success
- Balance with storage capacity / hardware constraints
- Design should allow for a variety of methods of interaction (input, control and query)
- UI should allow for a variety of interactive mechanisms:
- touch screen, keypad, light pens, voice command, hot keys
- 15. The Knowledge Base the Brains
- Contains the domain-specific knowledge acquired from the domain experts
- Can consist of object descriptions, problem-solving behaviors, constraints, heuristics and uncertainties
- The success of an ES relies on the completeness and accuracy of its knowledge base
- Distinguish a database (data facts) from a knowledge base (experts rules, cases, etc)
- 16. The Inference Engine the brawn!
- Here, the knowledge is put to use to produce solutions
- The engine is capable of performing deduction or inference based on rules or facts
- Also capable of using inexact or fuzzy reasoning based on probability or pattern matching
- Cycle consists of:
- Match rules with given facts
- Select the rule that is to be executed
- Execute the rule by adding the deduced fact to the working memory
- 17. Chaining
- Simple methods used by most inference engines to produce a line of reasoning
- Two methods are possible depending on the direction of reasoning
- Forward chaining: the engine begins with th
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