Artificial Intelligence Representing Commonsense Knowledge

1. Artificial IntelligenceRepresenting Commonsense Knowledge L. Manevitz L. Manevitz Lecture 7

2. Definitions • Representation – a set of syntactic and semantic conventions that make it possible to describe things. • Syntax – specifies the symbols that may be used and the ways those symbols may be arranged. • Semantics – specifies how meaning is embodied in the symbol arrangements allowed by the syntax. L. Manevitz Lecture 7

3. Semantic Network Approach • Nodes and Slots: Nodes are objects, or classes, or properties. Slots are of different types. L. Manevitz Lecture 7

4. Mammal Nose Brooklyn-Dodgers Person Blue Pee-Wee-Reese A Semantic Network Is-a has-part instance uniform-color team L. Manevitz Lecture 7

5. Representing Nonbinary Predicates • Unary Predicates can be rewritten as binary ones. man(Marcus) could be rewritten as instance(Marcus,Man) L. Manevitz Lecture 7

6. Game 5-3 G23 Cubs Dodgers Representing Nonbinary Predicates cont. • N-Place Predicates score(Cubs,Dodgers,5-3) becomes visiting-team Is-a score home-team L. Manevitz Lecture 7

7. Give BK23 Book EV7 John Mary A Semantic Net Representing a Sentence “John gave the book to Mary.” instance instance agent object beneficiary L. Manevitz Lecture 7

8. 72 H1 H2 John John Bill Some Important Distinctions First try: Second try: height height height greater-than L. Manevitz Lecture 7

9. 72 H1 H2 John Bill Some Important Distinctions cont. Third try: height height greater-than value L. Manevitz Lecture 7

10. Bite m Mail-carrier b d Dogs Partitioned Semantic Nets • The dog bit the mail carrier. Is-a Is-a Is-a assailant victim L. Manevitz Lecture 7

11. Bite m Mail-carrier b d g Dogs GS Partitioned Semantic Nets cont. • Every dog has bitten a mail carrier. SA S1 Is-a Is-a Is-a Is-a form assailant victim L. Manevitz Lecture 7

12. Bite c Constables b d g Town-Dogs GS Dogs Partitioned Semantic Nets cont. • Every dog in town has bitten the constable. SA S1 Is-a Is-a Is-a Is-a form assailant victim L. Manevitz Lecture 7

13. Bite m Mail-carrier b d g Dogs GS Partitioned Semantic Nets cont. • Every dog has bitten every mail carrier. SA S1 Is-a Is-a Is-a assailant victim form Is-a L. Manevitz Lecture 7

14. Inheritance • Is-a slot – appears between objects and classes. • ako slot – appears between subsets. L. Manevitz Lecture 7

15. Inheritance -Procedure F the given node; S the given slot; • Form a Queue of F and all class nodes connected to F via Is-A node. F is at top of Queue. • Until Queue is empty or default has been found determine if the first element of Queue has a value in its S slot: • Yes – a value has been found. • No – remove the first element from Queue and add the nodes related to the first element by AKO slots to the end of Queue. • If a value has been found say that this is the default value of F’s S slot. Otherwise announce Failure. L. Manevitz Lecture 7

16. Block Brick Brick12 rectangular Wedge Wedge18 Triangular Inheritance - Example ako ako shape shape Is-a Is-a L. Manevitz Lecture 7

17. If-needed Inheritance -Procedure F the given node; S the given slot; • Form a Queue of F and all class nodes connected to F via Is-A node. F is at top of Queue. • Until Queue is empty or successful if-needed procedure has been found determine if the first element of Queue has a procedure in the If-Needed facet of its S slot: • Yes – if the procedure produces a value than a value has been found. • No – remove the first element from Queue and add the nodes related to the first element by AKO slots to the end of Queue. • If a value has been found say that the value found is the value of F’s S slot. Otherwise announce Failure. L. Manevitz Lecture 7

18. Block Brick Brick12 Block-weight-procedure 400 11 If-needed Inheritance - Example Weight (if-needed) Volume Density L. Manevitz Lecture 7

19. Block Brick Brick12 400 11 4400 Example cont. Weight is activated by request for the weight of Brick12 ! Weight Volume Density L. Manevitz Lecture 7

20. Default Inheritance Procedure F the given node; S the given slot; • Form a Queue of F and all class nodes connected to F via Is-A node. F is at top of Queue. • Until Queue is empty or default has been found determine if the first element of Queue has a value in the Default facet of its S slot: • Yes – if the first element has a value than a value has been found. • No – remove the first element from Queue and add the nodes related to the first element by AKO slots to the end of Queue. • If a value has been found say that the value found is the default value of F’s S slot. Otherwise announce Failure. L. Manevitz Lecture 7

21. Block Brick Brick12 Red Wedge Wedge18 Blue Default Inheritance - Example Has no default color therefore probably Blue because of Block’s default color ! Color (Default) ako ako Color (Default) Is-a Is-a L. Manevitz Lecture 7

22. Toy Toy perspective Gift perspective rectangular Commemorate Structure perspective Brick12 Structure Brick Gift Play Support Perspective -Example shape Purpose Purpose Is-a Is-a Is-a Is-a Purpose L. Manevitz Lecture 7

23. Special Links - Summary • IS-A and AKO links make class membership and subclass-class relations explicit, facilitating the movement of knowledge from one level to another. • VALUE facets make values explicit. L. Manevitz Lecture 7

24. Special Links – Summary cont. • IF-NEEDED facets make procedures purposes explicit, and they relate procedures to the classes those procedures are relevant to. • DEFAULT facets make likely values explicit without implying certainty. • Perspectives make context sensitivity explicit, preventing confusion and ambiguity. L. Manevitz Lecture 7

25. Frames • Frames : A collection of nodes that describe a stereotyped object, act or event. • Example : newspaper report. L. Manevitz Lecture 7

26. Disaster-event Flood Hurricane Event Social-event Earthquake Birthday-party Earthquake Example Killed Place Injured Day Homeless Time Damage Magnitude Number-of-guests Fault Host Crest River Birthday-person Wind-speed Name Age L. Manevitz Lecture 7

27. Earthquake Example cont. Earthquake Hits Lower Slabovia • Today an extremely serious earthquake of magnitude 8.5 hit Lower Slabovia killing 25 people and causing $500,000,000 in damage. The president of Lower Slabovia said the hard-hit area near the Sadie Hawkins fault has been a danger zone for years. L. Manevitz Lecture 7

28. Earthquake13 Lower Slabovia Today 25 500,000,000 8.5 Sadie Hawkins Earthquake Example cont. place day fatalities damage magnitude fault L. Manevitz Lecture 7

29. Earthquake Summary Pattern • An earthquake occurred in value in location slot value in day slot. There were value in fatalities slot fatalities and value in damage slot in property damage. The magnitude was value in magnitude slot on the Richter scale, and the fault involved was the value in fault slot. L. Manevitz Lecture 7

30. Instantiated Earthquake Summary Pattern • An earthquake occurred in Lower Slabovia today . There were 25 fatalities and $500 million in property damage. The magnitude was 8.5 on the Richter scale, and the fault involved was the Sadie Hawkins. L. Manevitz Lecture 7

31. Earthquake Example cont. Earthquake Study Stopped Today, the President of Lower Slabovia killed 25 proposals totaling $500 million for research in earthquake prediction. Our Lower Slabovian correspondent calculates that 8.5 research proposals are rejected for every one approved. There are rumors that the President’s science advisor, Sadie Hawkins, is at fault. L. Manevitz Lecture 7

32. Earthquake Example cont. • The Earthquake Study Stopped story could be summarized, naively, as though it were the story about an actual earthquake, producing the same frame as the Earthquake Hits Lower Slabovia story does. L. Manevitz Lecture 7

33. Scripts L. Manevitz Lecture 7

34. Scripts • Example - Restaurant script. Script: Restaurant Roles: S=Customer Track: Coffee Shop W=Waiter Props: Tables C=Cook Menu M=Cashier F=Food O=Owner Check Money L. Manevitz Lecture 7

35. Restaurant Example cont. Entry conditions : S is hungry S has money Results : S has less money O has more money S is not hungry S is pleased (optional) L. Manevitz Lecture 7

36. Restaurant Example cont. Scene 1: Entering S PTRANS S into restaurant S ATTEND eyes to tables S MBUILD where to sit S PTRANS S to table S MOVE S to sitting position L. Manevitz Lecture 7

37. Restaurant Example cont. Scene 2: Ordering (menu on table) (W brings menu) (S asks for menu) S PTRANS menu to S S MTRANS signal to W W PTRANS W to table S MTRANS ‘need menu’ to W W PTRANS W to menu S MTRANS W to table *S MBUILD choice of F W PTRANS W to table S MTRANS signal to W W ATRANS menu to S W PTRANS W to table S MTRANS ‘I want F’ to W W PTRANS W to C W MTRANS (ATRANS) to C C MTRANS ‘no F’ to W W PTRANS W to S W MTRANS ‘no F’ to S (go back to *) or (go to Scene 4 at no pay path) C DO (prepare F script) to Scene 3 L. Manevitz Lecture 7

38. Restaurant Example cont. Scene 3 : Eating C ATRANS F to W W ATRANS F to S S INGEST F (Option : Return to Scene 2 to order more; otherwise go to Scene 4) L. Manevitz Lecture 7

39. Restaurant Example cont. Scene 4 : Exiting S MTRANS to W (W ATRANS check to S) W MOVE (write check) W PTRANS W to S W ATRANS check to S S ATRANS tip to W S PTRANS S to M S ATRANS money to M S PTRANS S to out of restaurant (No pay path) L. Manevitz Lecture 7