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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”
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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
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Example of “information fit”
Known information– John is Sam’s son– John is the eldest child– Mary is Sam’s daughter– John and Mary’s mother is called Anna– Sam has been married to Anna for 50 years
Derived information– If John is Sam’s 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
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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?
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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
So can computers simulate these processes of reasoning?
car bike
personal
Motor bike
commercial
Land transport
bus taxi
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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
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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 1st class degree
Declarative– If Antecedent THEN consequent– If student has mitigating circumstances THEN award
‘incomplete’ grade
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The CBR Cycle
N ew p ro b lem
A M e m oryK now le dge
B a s e
N ew p ro b lem
R etr iev e f r o mm em o r y
R etr iev a l
S u g g es ts o lu tio n
C o n f ir m s o lu tio n
R eu s e
R ev is e
N ew s o lu t io n
R eta in
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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)
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Neural Network
Train it? – supervisedOr just let it get on with it? - unsupervised
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Genetic Algorithm
mutationcrossover
Used in scheduling (timetabling?), design, marketing
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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
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Common Expert System Architecture
UserKnowledge Engineer
UserInterface
Inference Engine
Knowledge Base
User Environment
KE
Tool Kit
KEInterface
Development Environment
OrganizationSystemsInterface
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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
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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)
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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:1. Match rules with given facts
2. Select the rule that is to be executed
3. Execute the rule by adding the deduced fact to the working memory
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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 the initial content of the workspace and proceeds toward a final conclusion
Backward chaining: the engine starts with a goal and finds knowledge to support that goal
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Forward Chaining Example
Suppose we have three rules:
R1: If A and B then D
R2: If B then C
R3: If C and D then E
If facts A and B are present, we infer D from R1 and infer C from R2. With D and C inferred, we now infer E from R3.
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Backward Chaining Example
The same three rules:
R1: If A and B then D
R2: If B then C
R3: If C and D then E
If E is known, then R3 implies C and D are true. R2 thus implies B is true (from C) and R1 implies A and B are true (from D).
20
Real Chaining Example
Which method to choose? Backward or forward? Situation
– You wish to fly from Warsaw to Glasgow but all direct flights are full
Backward chain– Check which cities flights arriving in Glasgow are from
and keep going backwards
Forward chaining– Check destinations from Warsaw and then check their
destinations until you find a flight going to Glasgow
21
Expert Systems Shells
Expert System Shells: generic systems that contain reasoning mechanisms but not the problem-specific knowledge
Early shells were cumbersome but still allowed the user to avoid having to completely program the system from scratch
Modern shells contain two primary modules: a rule set builder (to construct the initial knowledge base) and an inference engine (as the vehicle for arriving at conclusions)
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Building an Expert System
An early step is to identify the type of tasks – Interpretation, predicting likely consequences– Diagnosis of faults, monitoring expected outcomes– User guidance
Key awareness is to recognise opportunities– E.g. to replace scarce expert resources– Provide an expert assistant– To free up experts onto other tasks– Look for activities where experts are:
Overburdened Undersupplied Expensive Unavailable
Candidates– Diagnosis, prediction, control, evaluation, prescription of solution
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Pre-design Activities
Another important step is choosing the experts who will contribute knowledge– Involved throughout the development– Not be threatened, rather enhanced
Development team should have a functional understanding of the knowledge domain
Unlike more general information systems design projects, the software tools and hardware platform are selected very early
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Benefits of Expert Systems
Increased timeliness in decision making• ES available on a 24 hour service
Increased productivity of experts• By taking some of the lesser important decisions• Replication at many sites
Improved consistency in decisions• Eliminates cognitive bias/limitation, memory loss
Improved understanding and explanation Improved management of uncertainty
• Fuzzy logic – e.g. how tall is tall? Formalization of knowledge within organisation
• And little amnesia! – knowledge is codified.
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Limitations Associated With ES’s
How do you code common sense? expertise is difficult to extract and encode. Expert “errors” transferred to model Another is that human experts adapt
naturally but an ES must be recoded. human experts better recognize when a
problem is outside the knowledge domain, but an ES may just keep working
ES’s can’t eliminate the cognitive limitations of the user
An ES is functional only in a narrow domain
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Key Point Summary
The Concept of Expertise Use of logic Methods of human reasoning
– Categorisation etc
AI methods of mimicking human reasoning– Case-based reasoning, pattern recognition etc
ES architecture– User interface, Knowledge base, inferencing
Designing and building ES’s Benefits and problems with ES’s
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Area of Research for Coursework
Case Based Reasoning– http://en.wikipedia.org/wiki/Case_based_reasoning
Neural Networks– http://www.cs.stir.ac.uk/~lss/NNIntro/InvSlides.html
Genetic Algorithms (http://cs.felk.cvut.cz/~xobitko/ga) Rule-based systems
– http://www.cee.hw.ac.uk/~alison/ai3notes/section2_4_4.html Pattern recognition
– http://cgm.cs.mcgill.ca/~godfried/teaching/pr-web.html Artificial life
– http://www.wisegeek.com/what-is-artificial-life.htm?referrer=adwords_campaign=artificiallife_ad=016171&_search_kw=what%20is%20artificial%20life
Automatic programming– http://www.cs.utexas.edu/users/novak/autop.html
Fuzzy Logic– http://www.seattlerobotics.org/encoder/mar98/fuz/flindex.html
Data Mining – Adriaans and Zantinge (library) Google found these with “What is …….?” queries You might find better ones to get you going