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Introduction to AI
6th Lecture
1980’s – Expert Systems
Wouter Beek
me@wouterbeek.com
13 October 2010
Part I
1980’s, The decade of
expert systems
1980’s, the decade of expert
systems
0 Funding in AI returned.
0 Applications become not wider but deeper.
0 Still within ...
1982, 5th generation project
0 FGCS, Fifth Generation Computer Systems project.
0 Japan's Ministry of International Trade ...
1983-1993, Reactions to 5th
generation project
0 1982, MCC, Microelectronics and Computer
Technology Corporation
0 America...
Expert systems
Main characteristics:
1. Provides expert-level solutions to complex problems.
0 Solutions are generated in ...
[1] Algorithm VS heuristic
Properties of algorithmic
problem-solving:
0 Guaranteed to find a
solution.
0 The found solutio...
[2] Quantitative VS qualitative
Quantitative reasoning:
0 Numerical data
0 Data-processing
0 Mathematical
0 Syntactical
Qu...
[3] Knowledge VS reasoning
0 Mutilated chessboard problem
0 Suppose a standard 8x8
chessboard has two diagonally
opposite ...
[3] Knowledge engineering
0 “KE is an engineering discipline that involves
integrating knowledge into computer systems in
...
Cyc (1984-present)
0 Assembles a comprehensive ontology and knowledge
base of everyday common sense knowledge.
0 Allowing ...
Part II
MYCIN
General and specific
knowledge
0 Production rules represent general knowledge.
0 Clinical parameters represent specific kn...
Production rules / Horn clauses
0 A literal is an atomic formula, or the negation of an atomic
formula.
0 E.g. p, q, r, ¬𝑟...
Reasoning with production rules
0 Backward chaining: for each conclusion, find the
matching parameters.
0 Forward chaining...
Explanation / transparency box
0 The trace of a reasoning task is also an explanation.
0 What rules were used to derive th...
Knowledge hypothesis
0 Expresses the relation between:
0 the complexity of the world
0 the required functionality
0 the ro...
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  • IC: silicon chip containing multiple transistors.
  • Transcript of "Introduction to AI - Sixth Lecture"

    1. 1. Introduction to AI 6th Lecture 1980’s – Expert Systems Wouter Beek me@wouterbeek.com 13 October 2010
    2. 2. Part I 1980’s, The decade of expert systems
    3. 3. 1980’s, the decade of expert systems 0 Funding in AI returned. 0 Applications become not wider but deeper. 0 Still within a very narrow domain. 0 But no longer toy problems. 0 Solutions for the common-sense knowledge problem were found. 0 Successful applications that meet expectations are realized.
    4. 4. 1982, 5th generation project 0 FGCS, Fifth Generation Computer Systems project. 0 Japan's Ministry of International Trade and Industry. Computer generations: 0 0th generation: 500 B.C., mechanical gears. 0 1st generation: 1940’s, vacuum tubes. 0 2nd generation: 1950’s, transistors. 0 3rd generation: 1960’s, integrated circuits (ICs). 0 4th generation: Microprocessors.
    5. 5. 1983-1993, Reactions to 5th generation project 0 1982, MCC, Microelectronics and Computer Technology Corporation 0 American computer manufacturers cooperate on research. 0 1983-1987, Alvey 0 British government project. 0 1983-1993, SCI, Strategic Computing Initiative 0 DARPA’s response to FGCS 0 $ 1.000.000.000 0 Remember the Sputnik launch in 1959, research funding is often reactive!
    6. 6. Expert systems Main characteristics: 1. Provides expert-level solutions to complex problems. 0 Solutions are generated in a heuristic way. 2. Give solutions that are understandable. 0 Solutions are couched in qualitative terms (i.e. concepts). 3. Flexible to accommodate new knowledge. 0 Decoupling of reasoning and knowledge.
    7. 7. [1] Algorithm VS heuristic Properties of algorithmic problem-solving: 0 Guaranteed to find a solution. 0 The found solution is the correct one. 0 The solution is found in finite time. Properties of heuristic problem-solving: 0 Probable to find a solution. 0 The found solution is an acceptable one. 0 The solution is found in practical time.
    8. 8. [2] Quantitative VS qualitative Quantitative reasoning: 0 Numerical data 0 Data-processing 0 Mathematical 0 Syntactical Qualitative reasoning: 0 Conceptual data 0 Symbol-processing 0 Logical 0 Semantical
    9. 9. [3] Knowledge VS reasoning 0 Mutilated chessboard problem 0 Suppose a standard 8x8 chessboard has two diagonally opposite corners removed, leaving 62 squares. Is it possible to place 31 dominoes of size 2x1 so as to cover all of these squares? [Gamow & Stern 1958] 0 Representation (partially) obsoletes reasoning / knowledge (partially) captures reasoning.
    10. 10. [3] Knowledge engineering 0 “KE is an engineering discipline that involves integrating knowledge into computer systems in order to solve complex problems normally requiring a high level of human expertise.” [Feigenbaum & McCorduck 1983] 0 “Mapping an expert’s knowledge into a program’s knowledge base.” [Buchanan & Shortliffe 1983, p. 5]
    11. 11. Cyc (1984-present) 0 Assembles a comprehensive ontology and knowledge base of everyday common sense knowledge. 0 Allowing machines to overcome the common-sense problem. 0 Started in 1984 by Douglas Lenat at MCC. 0 Currently developed by Cycorp. 0 OpenCyc is the open-source spin-off. 0 Wordnet, 1985-present, lexical ontology
    12. 12. Part II MYCIN
    13. 13. General and specific knowledge 0 Production rules represent general knowledge. 0 Clinical parameters represent specific knowledge: 0 object 0 attribute 0 value 0 Monitoring method: match the conditions in a production rule with clinical parameters. 0 Find-out method: 0 Infer unknown clinical parameters by using other production rules. 0 Query the user for unknown clinical parameters.
    14. 14. Production rules / Horn clauses 0 A literal is an atomic formula, or the negation of an atomic formula. 0 E.g. p, q, r, ¬𝑟, walks(John), loves(John, Mary), ¬loves(John, Mary) 0 A clause is a disjunction of literals. 0 E.g. p ∨ 𝑞 ∨ 𝑟, p ∨ ¬𝑞 ∨ ¬𝑟, loves(John, Mary) ∨ ¬ loves(John, Mary) 0 A Horn clause is a clause with at most one positive literal. 0 A definite clause is a Horn clause with at least one positive literal. 0 E.g. ¬𝐻𝑒𝑎𝑑𝑎𝑐ℎ𝑒 𝐽𝑜ℎ𝑛 ∨ ¬𝐻𝑖𝑔ℎ𝑇𝑒𝑚𝑝 𝐽𝑜ℎ𝑛 ∨ ¬𝑆𝑙𝑒𝑒𝑝𝑙𝑒𝑠𝑠𝑛𝑒𝑠𝑠 𝐽𝑜ℎ𝑛 ∨ 𝑆𝑒𝑟𝑖𝑜𝑢𝑠𝐼𝑛𝑓𝑒𝑐𝑡𝑖𝑜𝑛(𝐽𝑜ℎ𝑛) 0 Definite clauses are logically equivalent to implications. 0 E.g. 𝐻𝑒𝑎𝑑𝑎𝑐ℎ𝑒 𝐽𝑜ℎ𝑛 ∧ 𝐻𝑖𝑔ℎ𝑇𝑒𝑚𝑝 𝐽𝑜ℎ𝑛 ∧
    15. 15. Reasoning with production rules 0 Backward chaining: for each conclusion, find the matching parameters. 0 Forward chaining: for a set of parameters, find the conclusions that follow.
    16. 16. Explanation / transparency box 0 The trace of a reasoning task is also an explanation. 0 What rules were used to derive the result? 0 In which order were the rules applied? 0 Which parameters were used? 0 The machine reasons like a human.
    17. 17. Knowledge hypothesis 0 Expresses the relation between: 0 the complexity of the world 0 the required functionality 0 the role of knowledge 0 To achieve a high level of problem-solving competence, a symbol system must use a great deal of domain-specific, task-specific, and case-specific knowledge.
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