1. 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 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 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 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  Subject –The first spirit amazed Scrooge. –Scrooge saw images of his past. –The images caused a change in Scrooge's attitude.  Predicate –The first spirit amazed Scrooge. –Scrooge saw images of his past. –The images caused a change in Scrooge's attitude.  Object –The first 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) –ScroogeMonkey –SpiritChair –ImagesBrick  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 –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 –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 –http://www.cyc.com/cycdoc/img/UpperOntology.gif –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 –http://www.cyc.com/cycdoc/img/UpperOntology.gifhttp://www.cyc.com/cycdoc/img/UpperOntology.gif –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 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)