Expert Systems and Artificial Intelligence

Gerard Tel

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World War II as the AI Big Bang

Development in 1940-45:

What to do after WWII?

Create Humanoid machines.

• AI is concerned with programming computers to perform tasks that are presently better done by humans. (Minsky)

• Games, Language, Vision, Mathematics

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Music classification by Compression

• Music interpretation problem:Assign unknown symphony tocomposer.

• 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

Isosceles triangles are equiangular:if AT = BT then <(A) = <(B).

Reasoning steps of geometry:• If Δ(xyz) = Δ(uvw)

then <(y) = <(v)• Line bisection:

take m on xy st xm = my.• Congruence rule SSS:

if xy=uv, yz=vw, zx=wuthen Δ(xyz) = Δ(uvw).

20-30 axioms, theorems, and steps.

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A

B

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The Human Proof

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

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

(congruence)

Found in all textbooks for 2500 years since Euclid.

T

A

B

M

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The Computer Proof

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

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

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

(congruence)

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A

B

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The search for a proof: State Space Exploration

General Problem Solver

• Model problem:states and transitions.

• Search in induced graph.• Search Strategies:

• Depth-First

• Breadth-First

• Best-First

• Heuristic

Start state

QED

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Where Computers and Humans differ

Why couldn’t a human find the short proof?

• Tendency to overlook simple and special cases

Why couldn’t the computer find the long proof?

• It it too complicated!!

• Combinatorial ExplosionBranching 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 IntelligenceOverdragen: Chinese Room argument

• Understanding is INTENTIONAL:Computers and Humans work IN THE SAME WAYArgument: make the same mistakes, etc

• Understanding is BEHAVIORAL:Computers and Humans produce the same resultsTuring test

• Why is it difficult tomimic human behaviorin a computer?

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Understanding in Expert Systems?

Understanding requires:• Representation and

manipulation of Domain Knowledge

• Perceive analogies• Learn

Pragmatic view:• Intentional intelligence

is not required• Programs will work the

better if more human domain knowledge is encoded in themNormative DescriptiveLimitive

Definition of Expert System:

An Expert System is a computer program that represents and reasons with knowledge of some specialist subject with a view to solving problems or giving advice

Expert Systems are a subfield of Artificial Intelligence.

Term: Knowledge Based System

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Understanding Geometry: Represent knowledgeMathematician solves proof:• To conclude <(A) = <(B),

find Δ(xAy) and Δ(uBv); prove congruence.

• To conclude <(A) = <(B), construct <(C) = <(A); prove <(C) = <(B).

• To prove congruence, use SSS, ASA, SAS, …

• To construct …

• Triangle 1 is better than triangle 2 because …

• I usually try SAS first when…

Formalization of strategy:• Consider triangle pairs

xAy and uBv• Select a promising pair• Select a promising rule R• Prove antecedents of R• Conclude congruence• Conclude equality of angles

Strategies can be represented in STRIPS

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 thanindividual actions.

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Uniform representation of facts

Synonyms:1. Sam is the father of Bill2. Sam is Bill’s father3. Bill is Sam’s son4. Sam is Bill’s mother’s

husband

Perceiving analogies requires uniform representation:

• sonOf(Sam, Bill)

SHRDLU World:

color(block1, red)color(block2, green)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 inferenceknowledge is explicit, not hidden in algorithm

• Simulates Human reasoningBuilt 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 ExpertCan solve problems; we desire to solve the problems without her.

• Knowledge EngineerCan communicate with HE to obtain and model the knowledge that we need in the system

• ProgrammerBuilds and maintains all the necessary computer programs

• UserWants 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 confidenceincreases 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 knowledgeExpert is unaware, uncommunicative, busy, unwilling

• Representing knowledgeFacts, Relations, Conclusions, Meta-knowledge

• Controlling reasoningSelection 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)