1 / 12

How to represent knowledge for the computer?

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

marnin
Download Presentation

How to represent knowledge for the computer?

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  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?!

More Related