1. Done Done Course Overview What is AI? What are the Major Challenges?
What are the Main Techniques?
Where are we failing, and why?
Step back and look at the Science
Step back and look at the History of AI
What are the Major Schools of Thought?
What of the Future?
Done

2. Course Overview What are the Main Techniques? (How do we do it?)
What is AI?
What are the Major Challenges?
What are the Main Techniques? (How do we do it?)
Where are we failing, and why?
Step back and look at the Science
Step back and look at the History of AI
What are the Major Schools of Thought?
What of the Future?

3. Course Overview What are the Main Techniques? (How do we do it?)
What is AI?
What are the Major Challenges?
What are the Main Techniques? (How do we do it?)
Where are we failing, and why?
Step back and look at the Science
Step back and look at the History of AI
What are the Major Schools of Thought?
What of the Future?
Search
Logics (knowledge representation and reasoning)
Planning and acting
Bayesian belief networks
Neural networks
Evolutionary computation
Reinforcement learning
Language parsing and speech techniques
Statistical methods (language, learning)

4. Course Overview What are the Main Techniques? (How do we do it?)
What is AI?
What are the Major Challenges?
What are the Main Techniques? (How do we do it?)
Where are we failing, and why?
Step back and look at the Science
Step back and look at the History of AI
What are the Major Schools of Thought?
What of the Future?
Search
Logics (knowledge representation and reasoning)
Planning and acting
Bayesian belief networks
Neural networks
Evolutionary computation
Reinforcement learning
Language parsing and speech techniques
Statistical methods (language, learning)

5. How to represent knowledge for the computer?
Natural language is ambiguous
We gave the monkeys the bananas because they were hungry.
We gave the monkeys the bananas because they were over-ripe.
Mary threw a rock at the window and broke it.
purgé la totalité de sa peine
Need something precise for the computer
Want to represent
For communication
Meaning of natural language sentences
For robot to reason about what to do For Monkey-banana type problems
State of the world,
Effects of actions
For Vision
Things that are seen, and known objects

6. What are out Requirements? (for representing knowledge)
Expressive
John believes nobody likes brussels sprouts
Reasoning ability
Automatically deduce things
John believes Mary doesn’t like brussels sprouts
Don’t force us to represent loads of silly facts
If we know Gordon Brown is the Prime Minister Then we should know Joe Bloggs is not
We shouldn’t need to say
Joe Bloggs is not the Prime Minister
His brother Jimmy Bloggs is not the Prime Minister
Their dog Spot is not the Prime Minister …
Same for properties of a student
Tell the computer:
All students take courses and learn new subjects
If Mary is a student, we shouldn’t have to tell the computer
Mary takes courses and learns new subjects
Natural representation

7. Subject, Predicate, Object
The first spirit amazed Scrooge.
Scrooge saw images of his past.
The images caused a change in Scrooge's attitude.
Predicate
Object
Objects and subjects are just things (nouns)
Scrooge Monkey
Spirit Chair
Images Brick
Predicate describes relation among things (subject, object)
amazed(Spirit,Scrooge)
saw(Scrooge,images)
caused(images,change)

8. Predicate Logic Objects and subjects are just things
Monkey
Chair
Brick
Predicate is a relationship or property
Relationship like
amazes(spirit,Scrooge)
father(Vader,Luke)
bigger(Glasgow,Aberdeen)
Property like
red(car)
city(Aberdeen)
Logic deals with:
objects and relationships or properties

9. AND, OR, NOT, IF-THEN Some examples…
tall(Mary) AND beautiful(Mary)
sunny(day) OR rainy(day)
IF sunny(day) THEN go_for(Mary,walk)
IF rainy(day) THEN NOT (go_for(Mary,walk))
IF rainy(day) THEN (go_for(Mary,study) OR go_for(Mary,cinema))
Note “IF-THEN” does not necessarily mean “cause-effect”
IF depressed(Mary) THEN rainy(day)
Means that whenever she is depressed, it must be a rainy day
She can never be depressed on a fine day
Note “OR” is not exclusive
IF (sweet(sauce) OR salty(sauce)) Then likes(Mary,sauce)
“sweet(sauce) OR salty(sauce)”
true if the sauce is both salty and sweet
Logic is not natural language
it has a precise (sometimes unnatural) meaning

10. Variables Do not specify the exact value Some examples…
Just like x + x = 2x
Some examples…
IF beautiful(Mary) THEN likes(John,Mary)
IF beautiful(X) THEN likes(John,X)
IF beautiful(X) THEN likes(Y,X)
IF sunny(day) THEN go_for(Mary,Z)
IF rainy(day) THEN NOT (go_for(Mary,Z))
IF (rainy(day) AND student(S)) THEN (go_for(S,study) OR go_for(S,cinema))

11. FOR-ALL, THERE-EXISTS Some examples… All students study
FOR-ALL S IF student(S) THEN study(S)
Nobody likes brussels sprouts
FOR-ALL X NOT likes(X, brussels_sprouts)
Anybody who likes brussels sprouts is weird
FOR-ALL X IF likes(X, brussels_sprouts) THEN weird(X)
Somebody likes brussels sprouts
THERE-EXISTS X likes(X, brussels_sprouts)
Everybody has a mother
FOR-ALL X THERE-EXISTS Y mother(y,x)
Everybody is loved by somebody
FOR-ALL X THERE-EXISTS Y loves(y,x)
Everybody loves somebody
FOR-ALL X THERE-EXISTS Y loves(x,y)

12. Reasoning Suppose we have and we have then we could deduce
FOR-ALL X IF p(X) THEN q(X)
and we have
p(X)
then we could deduce
q(X)
Try an example…
Suppose
FOR-ALL X IF tall(X) THEN strong(X)
and suppose somebody tells us
tall(john)
What could we deduce?
and what if somebody else tells us
FOR-ALL X IF X strong(X) THEN loves(mary, X)

13. Ontology Can use logic to represent a hierarchy of concepts
isa(Tweety, canary)
isa(canary, bird)
isa(bird, animal)
isa(animal, living_thing)
isa(living_thing, physical_thing)
isa(physical_thing, tangible_thing)
isa(tangible_thing, thing)
Lower ontologies / domain ontologies
Computer hardware
Medicine
Upper ontologies
Several incompatible upper ontologies exist

14. Example Ontology

15. Ontology Can use logic to represent a hierarchy of concepts
isa(Tweety, canary)
isa(canary, bird)
isa(bird, animal)
isa(animal, living_thing)
isa(living_thing, physical_thing)
isa(physical_thing, tangible_thing)
isa(tangible_thing, thing)
Lower ontologies / domain ontologies
Computer hardware
Medicine
Upper ontologies
Several incompatible upper ontologies exist

16. Ontology Represent a broad selection of objects and relations
Getting some common sense into the computer
Domain ontologies – for a limited domain
Cyc type projects – attempt to do all knowledge
Also try to apply it to the Internet
Make Web pages in Logic
So that computer can understand and deduce things
Example: for a travel itinerary
“Semantic Web”
Retreat from original goal of making computers understand Natural Language

17. Logic Success/Failure?
As with other areas
Success for limited domains
Medicine
Engineering
Still little progress towards general knowledge
Reasoning
Works well in a constrained domain
Takes too long if large domain
Hard to represent things like
Default/defeasible (i.e. usually/unless proved otherwise)
Time/events/changes
Beliefs, must be, could be, ought to be
Uncertainty – “IF X THEN probably Y”
These can be done… but reasoning becomes more difficult / slower
Ultimate problem…
Too much to write down
Too many consequences to deduce/infer
What is relevant and not relevant?
common sense would tell you, but how to get commonsense in the first place?!

18. Course Overview What are the Main Techniques? (How do we do it?)
What is AI?
What are the Major Challenges?
What are the Main Techniques? (How do we do it?)
Where are we failing, and why?
Step back and look at the Science
Step back and look at the History of AI
What are the Major Schools of Thought?
What of the Future?
Search
Logics (knowledge representation and reasoning)
Planning and acting
Bayesian belief networks
Neural networks
Evolutionary computation
Reinforcement learning
Language parsing and speech techniques
Statistical methods (language, learning)