# Expert Systems and Artificial Intelligence by lff30040

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```									Expert Systems and Artificial Intelligence

Gerard Tel
World War II as the AI Big Bang

Development in 1940-45:       Create Humanoid machines.

• AI is concerned with
programming computers to
perform tasks that are
presently better done by
humans. (Minsky)
• Games, Language, Vision,
Mathematics
What to do after WWII?
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Music classification by Compression
• Music interpretation problem:
Assign unknown symphony to
composer.
• Human approach: extract style,
emotion, era…
• Compression by Lempel-Ziv
(as in ZIP):
After “training”, compactly code common patterns.
• Compress file B after training with A:
DAB = | Cp(AB) | - | Cp(A) |
Measure for similarity between A and B.
• From known musical scores Ai, select the one that
minimizes DAiB.

• Hmmm… Is this intelligent?
Outperforms the best human music experts!!
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Mathematics: Triangle Theorem
T
Isosceles triangles are equiangular:
if AT = BT then <(A) = <(B).

B

Reasoning steps of geometry:
• If Δ(xyz) = Δ(uvw)
then <(y) = <(v)
• Line bisection:                          A
take m on xy st xm = my.
• Congruence rule SSS:
if xy=uv, yz=vw, zx=wu
then Δ(xyz) = Δ(uvw).

20-30 axioms, theorems, and steps.

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The Human Proof

Given AT = BT,                           T
Prove that <(A) = <(B).

Proof (Euclid):
1. Take M to bisect AB.                                  B
2. AT = BT                (Given)
3. TM = TM                (Trivial)
4. MA = MB                (From 1)               M
5. Δ(MAT) = Δ(MBT)        (SSS)
6. <(A) = <(B)            (congruence)       A

Found in all textbooks for 2500 years
since Euclid.

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The Computer Proof

Clever trick: Use possibility of a non-
T
trivial self-congruence.

Given AT = BT,
Prove that <(A) = <(B).                               B

Proof:
1. AT = BT                (Given)
2. TB = TA                (Given)
3. BA = AB                (Trivial)           A
4. Δ(BAT) = Δ(ABT)        (SSS)
5. <(A) = <(B)            (congruence)

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The search for a proof: State Space Exploration

General Problem Solver                Start state

• Model problem:
states and transitions.
• Search in induced graph.
• Search Strategies:
• Depth-First
• Breadth-First

• Best-First
• Heuristic
QED

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Where Computers and Humans differ

Why couldn’t a human find     Why couldn’t the computer find
the short proof?              the long proof?

• Tendency to overlook        • It it too complicated!!
simple and special cases
• Combinatorial Explosion
Branching factor 13
5 steps: 402,234 nodes
9 steps: 11,488,207,654

• Exploring exponential spaces
is intractible.

• But humans dont work like
this… they understand math

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What does it mean to understand?

• Jackson: The Romantic period in Artificial Intelligence
Overdragen: Chinese Room argument

• Understanding is INTENTIONAL:
Computers and Humans work IN THE SAME WAY
Argument: make the same mistakes, etc
• Understanding is BEHAVIORAL:
Computers and Humans
produce the same results
Turing test

• Why is it difficult to
mimic human behavior
in a computer?

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Understanding in Expert Systems?

Understanding requires:
• Representation and          Definition of Expert System:
manipulation of
Domain Knowledge            An Expert System is a
computer program that
• Perceive analogies
represents and reasons with
• Learn
knowledge of some
specialist subject with a
Pragmatic view:               view to solving problems or
• Intentional intelligence    giving advice
is not required
• Programs will work the      Expert Systems are a
better if more human        subfield of Artificial
domain knowledge is         Intelligence.
encoded in them
Normative Descriptive       Term:
Limitive                    Knowledge Based System
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Understanding Geometry: Represent knowledge

Mathematician solves proof:   Formalization of strategy:
• To conclude <(A) = <(B),    • Consider triangle pairs
find Δ(xAy) and Δ(uBv);          xAy and uBv
prove congruence.
• Select a promising pair
• To conclude <(A) = <(B),
construct <(C) = <(A);      • Select a promising rule R
prove <(C) = <(B).          • Prove antecedents of R
• Conclude congruence
• To prove congruence, use    • Conclude equality of
SSS, ASA, SAS, …              angles
• To construct …
Strategies can be
• Triangle 1 is better than     represented in STRIPS
triangle 2 because …
• I usually try SAS first
when…                        Backtracking,
Ordering of alternatives,
Pruning,
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…
STRIPS Operator Tables

Robot moves object X from location Y to location Z:
Operator Table push(X, Y, Z)
• Pre:          at(robot, Y), at(X, Y)
• Post:         at(X, Z)
• Add list:     at(robot, Z), at(X, Z)
• Delete list:  at(robot, Y), at(X, Y)

STRIPS maintains a goal list.
Action:
• Pick goal G from goals
• Pick OT with G in Post
• Throw Pre of G in goals

Planning is at higher level than
individual actions.
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Uniform representation of facts

Synonyms:                      SHRDLU World:
1. Sam is the father of Bill
2. Sam is Bill’s father
3. Bill is Sam’s son
4. Sam is Bill’s mother’s
husband

Perceiving analogies
requires uniform             color(block1, red)
representation:              color(block2, green)
• sonOf(Sam, Bill)             supports(table, block1)
supports(block1, block2)

Represent many facts with
simple fact structure
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Data representation: Relational Database

Define a relation in CLIPS
(deftemplate errand
(field name (type SYMBOL))
(field duration (type INTEGER) ) )
Relation errand with attributes name and duration.

Add tuples to relation errand:
(deffacts the-facts
(errand (name hospital) (duration 200) )
(errand (name doctor) (duration 100) ) )

CLIPS actions and rules as Database operations.

Update fact base when state is changed.

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Explicitation of Expert knowledge

Problem 1: What does it stand for in
• The ball hit the vase and it broke
• The vase hit the wall and it broke

Problem 2: Using hammer, string, and wrench, liberate
ball from tube in floor.
(Solution could never be found by computer!!)

Application of XS requires:
• restriction to a well-understood domain (ball?)
• someone who can perform the task
• knows how it is performed (it?)
• can explain how it is performed
• is willing to cooperate

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Characteristics of Expert Systems:

Expert systems: Knowledge Based Systems

• Separation of facts, knowledge, and inference
knowledge is explicit, not hidden in algorithm
• Simulates Human reasoning
Built from approach of Human Expert
• Uses approximate or heuristic search

Not the only approach to solving AI problems!
(Music classification, chess…)

Use: Legal, medical, scientific,
tech support, language, …

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Parties in XS world

• Human Expert
Can solve problems;
we desire to solve the problems without her.
• Knowledge Engineer
Can communicate with HE to obtain and
model the knowledge that we need in the system
• Programmer
Builds and maintains all the necessary computer
programs
• User
Wants to use expertise to solve problems
(better, cheaper)

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Explanation facilities

System makes explicit why the conclusion is reached
• For User:
increases confidence
increases transparance (legal domain!)
• For Human Expert/Knowledge Engineer:
see how knowledge is used, debug
• For Programmer:
Debug, Test, Improve
• For trainee:
have better understanding of expertise

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Main challenges in Expert Systems field

• Acquiring knowledge
Expert is unaware, uncommunicative, busy, unwilling
• Representing knowledge
Facts, Relations, Conclusions, Meta-knowledge
• Controlling reasoning
Selection between alternatives is guided by higher order
knowledge (meta rules)
• Explanation
• Sequence of reasoning steps?
• Interpretation at higher level
• Why were other steps NOT chosen?
• Quality evaluation; acceptance

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The Expert Systems course

• www.cs.uu.nl/docs/vakken/exp/
• Teacher: Gerard Tel
Practicals: Johan Kwisthout

• One or two papers per lecture
• Optional reading:
Peter Jackson, Introduction to Expert Systems
• Formal obligations: two exams,
two computer projects in teams of four.
MIN ≥ 4, WeightedAVG, 2nd chance.
• Prepare for working class:
read literature, try exercises (website)
• Prepare for tests:
Working class, previous tests (website)

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