Pertemuan 13 Weak Slot-and-Filler Structures

1. Pertemuan 13Weak Slot-and-Filler Structures Matakuliah : T0264/Inteligensia Semu Tahun : Juli 2006 Versi : 2/2

2. Learning Outcomes Pada akhir pertemuan ini, diharapkan mahasiswa akan mampu : • << TIK-99 >> • << TIK-99>>

3. Outline Materi • Materi 1 • Materi 2 • Materi 3 • Materi 4 • Materi 5

4. 9.2. Frames • Frame (Bingkai). Frame merupakan kumpulan pengetahuan tentang suatu obyek tertentu, peristiwa, lokasi, situasi atau informasi lainnya. • Frame memiliki slot yang menggambarkan rincian (atribut) dan karakteristik obyek. • Frame biasanya digunakan untuk merepresentasikan pengetahuan yang didasarkan pada karakteristik yang sudah dikenal yang merupakan pengalaman-pengalaman.

5. Frames • Dengan menggunakan Frame maka sangat mudah untuk membuat inferensi tentang obyek, peristiwa atau situasi baru. Hal ini karena Frame menyediakan basis pengetahuan yang ditarik dari pengalaman.

6. Frames : Transportasi Darat

7. Frames A Simplified Frame System Person isa : Mammal cardinality : 6,000,000.000 * handed : Right Adult-Male isa : Person cardinality : 2,000,000,000 *height : 5-10 ML-Baseball-Player Isa : Adult-Male Cardinality : 624 *height : 6-1 *bats : equal to handed *batting-average : .252 *team : *uniform-color :

8. Representing the Class of All Teams as a Metaclass Class instance: Class isa : Class *cardinality : Team instance: Class isa : Class cardinality : {the number of teams that exist} *team size : {each team has a size} ML-Baseball-Team instance: Class isa : Class cardinality : 26 {the number of baseball team that exist} *team size : 24 {default 24 players on team} *manager Brooklyn-Dodgers instance: ML-Baseball-Team isa : ML-Basball-Plyer team size : 24 manager : Lea-Durocher *uniform-color : Blue Pee-Wee-Reese instance: Brooklyn-Dodgers isa : Fielder uniform color : Blue batting-average : .309

9. Classes and Metaclasses

10. Representing Relationships among Classes

11. Representing Relationships among Classes ML-Baseball-Player is-covered-by : {Pitcher, Catcher, Fielder} {American-Leaguer, National-Leaguer} Pitcher isa : ML-Baseball-Player mutually-disjoint-with : {Catcher, Fielder} Catcher isa : ML-Baseball-Player mutually-disjoint-with : {Pitcher, Fielder} Fielder isa : ML-Baseball-Player mutually-disjoint-with : {Pitcher, Catcher} American-Leaguer isa : ML-Baseball-Player mutually-disjoint-with : {National-Leaguer} National-Leaguer isa : ML-Baseball-Player mutually-disjoint-with : {American-Leaguer} Three-Finger-Brown instance : Pitcher instance : National-Leaguer

12. Slots as Full-Fledged Objects • We want to be able to represent and use the following properties of slots (attributes or relations) : • The classes to which the attribute can be attached. • Constraints on either the type or the value of the attribute. • A value that all instances of a class must have by the definition of the class. • A default value for the attribute. • Rules for inheriting values for the attribute.

13. Slots as Full-Fledged Objects • Rules for computing a value separately from inheritance. • An inverse attribute. • Whether the slots is single-valued or multivalued.

14. Representing Slots as Frames, I

15. Representing Slots as Frame, II • Slots • isa : Class • instance : Class • domain : • range : • range-constraint : • definition : • default : • transfers-through : • to-compute : • inverse : • single-valued :

16. Representing Slots as Frame, II • manager • instance :slots • domain :ML-Baseball-Team • range :Person • range-constraint : x(experience x.manager) • default : • inverse : manager-of • single-valued : TRUE

17. Representing Slots as Frames, III

18. Representing Slots as Frames, IV

19. Tangled Hierarchies Hierarchies that are not trees are called tangled hierarchies We want to decide whether Fifi can fly The correct answer is no

20. Tangled Hierarchies Determining whether Dick is a pacifist. Transverse multiple instance link and more than one answer can be found along the path.

21. More Tangled Hierarchies

22. More Tangled Hierarchies • In the case of (a), our new algorithm reaches Bird (via Pet-Bird) before it reaches Ostrich. So it report that Fifi can fly. • In the case (b), the algorithm reaches Quaker and stops without noticing a contradiction. • The problem is that path length does not always corresponds to the level of generality of class. • The solution to this problem is to base our inheritance algorithm not on path length but on the notion of inferential distance, which can be defined as follows :

23. Defining Property Inheritance • Inferential Distance : • Class1 is closer to Class2than to Class3 if and only if Class1 has an inference path through Class2 to Class3 (in other words, Class2 is between Class1 and Class3) • We can now define the result of inheritance as follows : The set of competing values for a slot S in a frame F contains all those values that • Can be derived from some frame X that is above F in • the isa hierarchy • Are not contradicted by some frame Y that has a shorter • inferential distance to F than X does

24. Algorithm : Property Inheritance To retrieve a value V for slot S of an instance F do : • Set CANDIDATES to empty. • Do breadth-first or Dept-first search up the isa hierarchy from F, following all instance and isa link. At each step, see if a value for S or one of its generalizations is stored. • If a value is found, add it to CANDIDATES and terminate that branch of the search. • If no value is found but there instance or isa link upward, follow them. • Otherwise terminate the branch.

25. Algorithm : Property Inheritance 3. For each element C of CANDIDATES do : • See if there is any other element of CANDIDATES that was derived from a class closer to F than the class from which C came. • If there is, then removed C from CANDIDATES. • Check the cardinality of CANDIDATES : • If it is 0, then report that no value was found. • If it is 1, then return the single element of CANDIDATES as V. • If it is greater then 1, report a contradiction.

26. << CLOSING>> End of Pertemuan 13 Good Luck For Medial Semester Test