csm10 intelligent information systems · 2007. 1. 16. · ¥software tools (4d), project and r...
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CSM10
Intelligent Information Systems
Introductions
Content and coursework
What is intelligence?
CSM10 Spring Semester 2007
Intelligent Information Systems
Professor Ian Wells
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Welcome …
… now introduce ourselves!
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Useful details
• Professor Ian WellsConsultant Computer Scientist, Department of Medical Physics
Royal Surrey County Hospital, Guildford GU2 7XX
• at UniS on Tuesdays only: 19 BB 02
• shares with Dr Terry Hinton + Mr Peter Ainsley
• student hour: Tuesdays from 6pm to 7pm
• email: [email protected]
• or: [email protected] (forwarded to above)
• hospital direct line (if important): 01483 464 039
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Web resources
• course web site (new site only!)
• www.cs.surrey.ac.uk/teaching/csm10/
• lecture notes will be posted after lecture
• notices and other handouts
• ULearn (as soon as feasible!)
• training only last week!
• access from hospital uncertain
• how many of you use it and how do you find it?
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Buzzword prevention ...
www-lib.usc.edu/~karl/Bingo/
… make sure you interrupt and ask!!
Philosophy
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• from NHS so: patients students first!
• teach from practical experience over 25 years
• update and enliven lectures each year
• engage despite spectrum of technical preference
• improve 4D expert system shell
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Course history
• from ‘Intelligent Decision Support Systems’
to ‘Intelligent Information Systems’
• 14 students in 2000 to 25 in 2004 to ?? in 2007
• from IS only to IS & IC & maths & EE students
• from broad coverage to greater depth
• from one lecturer (+ guest speakers) to two
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Teaching methods
• two lecturers teaching their prime research subject
• Rule-based systems (Prof Ian Wells)
• Neural networks (Dr Tony Browne)
• lectures, case studies, tutorials and videos
• laptops needed for coursework
• heuristic and dynamic approach
• participation and feedback essential!
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Assessment
• coursework (50%)
• group project and individual report
• terminology and creativity
• focus on rule-based systems
• written examination (50%)
• problem solving
• focus on neural networks
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Deliverables
• understand how humans solve problems
• learn how to replicate this on a computer
• build and evaluate a working expert system
• discuss the wider implications of the concepts
• discover how to ‘think differently’
• and above all have fun!
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Demands
• read the course book!
• then read wider in areas that you find attractive
• participate during the discussions
• feed back both during and after the course
• form yourselves into balanced groups
• start your coursework in good time
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Book review
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Book review
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Book review
Content and conceptsCoursework
Course contentIntroduction to basic concepts
Coursework projectSoftware tools
Report
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Rule-based systems
• introduction, concepts and intelligence
• cognitive processes and problem solving
• semantic networks and production rules
• knowledge representation (in databases)
• software tools (4D), project and reports
• frames, cases, uncertainty and ubiquity
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Coursework
• group development + individual report
• start forming into groups as soon as possible
• dynamics of group composition
• all programmers or no programmers a disaster!
• need coding, content and control
• choice of suitable subject and domain expert
• must be non-trivial and not already attempted
• must be agreed with Prof Wells before work commences
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From CLIPS to 4D
• CLIPS - expert system construction tool
• developed by NASA for internal use
• written in C, portable, free, good books
• http://www.ghg.net/clips/CLIPS.html
• 4D - mid range database development
• modern, powerful and free to students
• http://www.4duk.com/index.html
• Penny expert system shell developed for this course!
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Advantages of 4D
• modern RAD / database software
• used and supported worldwide
• developed in France in 1985
• principles can be used in other environments
• level playing field for all groups
• runtime licence free so all students can install it
• student licence available from 4D UK (? free ?)
• register at www.4duk.com/academic.html
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Penny expert system shell
• written by Lo Farnan as OU MSc project
• version 3 much improved, version 4 shortly
• feedback, bug reports and suggestions please
• download at:
• www.4dcoop.com/penny/pennydownload.htm
• will be updated in response to your input
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What is intelligence?
Case study from medicine:
Are doctors intelligent?
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When did you last see a doctor?
• what processes took place?
• how were the outcomes decided?
• was the doctor intelligent?
• if so - on what grounds?
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A visit to the hospital or GP
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Collecting information
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Physical evidence
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Data or information?
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Interpreting the evidence
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What is taking place?
• medical history (notes and/or computer)
• questions -> history update
• examination -> observations -> information
• application of intelligence?
• what type of reasoning and which direction... ?
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Outcomes ...
• do nothing or play for time
• 80% of patients will get better regardless!
• advice, therapy or referral to specialist
• high volume low yield problem
• doctors spend most of their time circulating information
• … but all the time making decisions
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The problem …
“Medicine is the art of making
acceptable decisions in an imperfectly
understood problem space using
insufficient and often erroneous
information”
With acknowledgment to Edward Shortliffe
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How do doctors reason?
• what type of reasoning and which direction... ?
• deductive approach to diagnosis
• reason backwards from selection of hypotheses
• pure inductive reasoning not used
• working forwards from all the facts
• so intelligence is ... ?
• knowledge + perception + reasoning skills
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Conclusion
Elstein 1978 p 111
“The (medical) deductive process
is not automatic, even though
experienced practitioners
can make it seem to be”
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Early computer model
Strategic LayerStrategic rules
Foreign clauses and rules
User input
Deductive LayerDeductive rules
Management of uncertainty
StaticFacts
DynamicFacts
ConclusionsExplanations
Actions
PROSE: Ahmad & Wells 1985
DeductionsUnknowns
What is intelligence?
Tutorial discussion
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Drive a car Wise Paint a picture
Compose music WealthyControl air
traffic
Perform an operation
Intelligence Win at chess
Minister of religion
University lecturer
Tell a story
Appreciate art Diagnose disease School teacher
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Where to put intelligence?
Wisdom
Knowledge
Information
DataNoise
Value
Volume
Difficulty
Meta-knowledge
Character
?
???
Intelligence = appropriate application of knowledge?
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Some definitions
• data
• information
• knowledge
• wisdom
• intelligence
• algorithm
• heuristic
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Some definitions
• data - identifiable and reproducible (not noise)
• information - data understood and in context
• knowledge - apply information to actions
• wisdom - appropriate use of knowledge
• intelligence - knowledge + perception to action
• algorithm - always succeeds but may take too long
• heuristic - quick result but may not be correct
Intelligence ... ?
• ability to understand and learn things
• reason about actions instead of automatic response
• ability to acquire and apply knowledge and skills
• vary state or action in response to current situations and past experience
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Artificial Intelligence (AI) ...
• emulate human intelligence on a computer
• teach computers how to learn and solve problems
• make computers appear less stupid
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Definitions of AI
• Nobel Prize contender - understand intelligence
• computer scientist - make computers faster
• accountant - make computers more useful
• doctor - make computers more helpful
• consumer - make computers more reliable
Detection of AI
• Turing Test
• Alan Turing - statue in front of AP building
• 1950 - article in Computing Machinery and Intelligence
• “if a computer could think how could we tell?”
• Loebner Prize
• $100,000 and gold medal
• held annually since 1990
• see www.loebner.net
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Computeror Human
Human
Judge
Observer
Some buzz words ...
• expert system and expert system shell
• KBS and IKBS - knowledge-based system
• knowledge engineer / engineering
• intelligent or ‘smart’ system or device
• AI and ‘fifth generation’ computing
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Intelligence in information systems
• understanding - the domain
• formulating - the problem
• relating - the problem to the user
• interpreting - the results or outcome
• explaining - questions, reasoning, decisions
• adjusting - questions and responses to suit user
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How What
Shallow knowledge
Millionaire (game show)
Bottom up
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Perceptual model
Environment
Top downExpected features
Deductive
Bottom upFeature analysisInductive
Neisser’s cyclic model of perception
Intelligence
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“Behaviour which is admired but not understood”
Marvin Minsky (c 1963)
Intelligence = Perception + Cognition + Motor Control
Professor Khurshid Ahmad (1999)
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Artificial Intelligence
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Next week ...
What do you do all the time and not realise?
Do you know the connection between women, fire and dangerous things?
Can you solve a problem quicker than a white rat?
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