How to represent knowledge for the computer?

1. 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 doFor Monkey-banana type problems • State of the world, • Effects of actions • For Vision • Things that are seen, and known objects

2. 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 MinisterThen 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

3. Subject, Predicate, Object • Subject • The first spirit amazed Scrooge. • Scrooge saw images of his past. • The images caused a change in Scrooge's attitude. • Predicate • Thefirst spirit amazed Scrooge. • Scrooge saw images of his past. • The images caused a change in Scrooge's attitude. • Object • Thefirst spirit amazed Scrooge. • Scrooge saw images of his past. • The images caused a change in Scrooge's attitude. • 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)

4. 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

5. 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

6. Variables • Do not specify the exact value • 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))

7. 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)

8. Reasoning • Suppose we have • 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)

9. Example Ontology

10. 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 • http://www.cyc.com/cycdoc/img/UpperOntology.gif • Several incompatible upper ontologies exist

11. 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

12. 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?!