Expert Systems and Artificial Intelligence by lff30040

VIEWS: 101 PAGES: 20

									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?
                Expert Systems 1                           2
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!!
              Expert Systems 1                        3
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.



                 Expert Systems 1              4
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.




                 Expert Systems 1                    5
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)




                  Expert Systems 1                6
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




                   Expert Systems 1                       7
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



                Expert Systems 1                          8
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?


                Expert Systems 1                            9
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
                Expert Systems 1                        10
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,
                Expert Systems 1                           11
                               …
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.
                Expert Systems 1                      12
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
                Expert Systems 1                          13
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.

               Expert Systems 1                      14
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


               Expert Systems 1                         15
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, …



                Expert Systems 1                   16
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)




              Expert Systems 1                      17
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




               Expert Systems 1                       18
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




                Expert Systems 1                       19
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)


               Expert Systems 1                   20

								
To top