1980’s, the decade of expert
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
0 Successful applications that meet expectations are
1983-1993, Reactions to 5th
0 1982, MCC, Microelectronics and Computer
0 American computer manufacturers cooperate on
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
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.
3. Flexible to accommodate new knowledge.
0 Decoupling of reasoning and knowledge.
 Algorithm VS heuristic
Properties of algorithmic
0 Guaranteed to find a
0 The found solution is the
0 The solution is found in
Properties of heuristic
0 Probable to find a
0 The found solution is an
0 The solution is found in
 Quantitative VS qualitative
0 Numerical data
0 Conceptual data
 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.
 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
0 “Mapping an expert’s knowledge into a program’s
knowledge base.” [Buchanan & Shortliffe 1983, p. 5]
0 Assembles a comprehensive ontology and knowledge
base of everyday common sense knowledge.
0 Allowing machines to overcome the common-sense
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
General and specific
0 Production rules represent general knowledge.
0 Clinical parameters represent specific knowledge:
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
0 Query the user for unknown clinical parameters.
Production rules / Horn clauses
0 A literal is an atomic formula, or the negation of an atomic
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
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 ∧
Reasoning with production rules
0 Backward chaining: for each conclusion, find the
0 Forward chaining: for a set of parameters, find the
conclusions that follow.
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.
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