Artificial Intelligence

1. 1 Artificial Intelligence University Politehnica of Bucharest 2008-2009 Adina Magda Florea http://turing.cs.pub.ro/aifils_08

2. 2 Lecture No. 6 Prolog Language Rule-based Systems Knowledge representation using rules RBS structure Inference cycle of a RBS Control strategy

3. 3 1. Prolog R. Kowalski, A. Colmerauer - '70 Prolog control structure A goal S is true in a Prolog program (can be satisfied or is a logical consequence of the program) iff: 1.  There is a clause C in the program; 2.  There is an instance I of the clause C such that: 2.1.   the head of I is the same with the head of S; 2.2 all the goals in I’s body has true (can be satisfied)

4. 4 Prolog

5. 5 Prolog % parinte(IndividX, IndividY) % stramos(IndividX, IndividZ) parinte(vali, gelu). parinte(ada, gelu). parinte(ada, mia). parinte(gelu, lina). parinte(gelu, misu). parinte(misu, roco). str1(X, Z) :- parinte(X, Z). str1(X, Z) :- parinte(X, Y), str1(Y, Z).

6. 6 Prolog % Change the order of the rules: str2(X, Z) :- parinte(X, Y), str2(Y, Z). str2(X, Z) :- parinte(X, Z). % Change the order of goals: str3(X, Z) :- parinte(X, Z). str3(X, Z) :- str3(X, Y), parinte(Y, Z). % Change both the order of rules and the order of goals: str4(X, Z) :- str4(X, Y), parinte(Y, Z). str4(X, Z) :- parinte(X,Z).

7. 7 Prolog ?- str1(ada, misu). yes ?- str2(ada, misu). yes ?- str3(ada, misu). yes

8. 8 Prolog ?- str4(ada, misu). ?- str3(mia, roco).

9. 9 2. Knowledge representation using rules Declarative representation / procedural representation R1: if the patient has fever and the type of the organism is gram-pozitiv and the patient has a stiff neck then the identity of the organism is streptococcus R2: if the car does not start and the lights do not turn on then the battery is faulty sau there is no contact of the battery R3: if the temperature > 95o C then open the protection valve

10. 10 Knowledge representation using rules

11. 3. Structure of an RBS

12. 12 4. Inference cycle of an RBS Match Selection Execution

13. 13 Inference cycle of an RBS - cont Algorithm: Functioning of an RBS 1. WM ? Case data 2. repeat 2.1. Identify WM and KB and create the conflict set (CS) 2.2. Select a rule from the CS to apply 2.3. Apply selected rule until there are no more applicable rules or WM satisfies goal or a predefined effort has been exhausted end.

14. 14 5. Control strategy Selection criteria from the CS Direction of rule application

15. 15 5.1 Selection criteria from the CS Select the first applicable rule Select a rule from the CS Preferences based on rule identity Specificity - the set of premises of R1 includes the set of premises of R2; - the set of conditions of R1 are similar to that of R2, but the premises of R1 refer to constants while the premises of R2 refer to variables. Time of last use Preferences based on matched objects

16. 16

17. 17 Selection criteria from the CS - cont Use of meta-rules if a rule has conditions A and B and the rule refers {does not refer} X { not at all/ only in the LHS/ only in the RHS } then the rule will be certainly useful { probably useful/ probably useless/ certainly useless } Application of all rules in the CS

18. 18 5.2 Direction of rule application Forward chaining Backward chaining

21. Algorithm: Find a value using forward chaining (a) FindValueD(A) 1. if A has value then return SUCCESS 2. Build CS 3. return Find(A, CS) end. Find(A, CS) 1. if CS = {} then return FAIL 2. Choose a rule R?CS according to a selection criterion (criteria) 3. for each premise Ij of R execute 3.1. Find the truth of Ij 4. if all Ij , j=1,N are satisfied then 4.1. if A has value then return SUCCESS 4.2. Add the fact(s) refered in the RHS of R to WM 5. CS ? CS - R 6. return Find(A, CS) end.

22. Algoritm: Find a value using backward chaining (b) Detvalue(A) 1. Build the set if rules which refer A in RHS, CS 2. if CS = {} then 2.1. Ask the user the value of A 2.2. if the user answers A then stare this value in WM else return FAIL 3. else 3.1. Choose a rule according to a selection criterion (criteria) 3.2. for each premise Ij, j=1,N, in LHS of R execute 3.2.1. Be Aj refered by the premise Ij 3.2.2. if Aj has value then evaluate the truth of Ij 3.2.3. else i. if Detvalue(Aj) = SUCCESS then evaluate the truth of Ij ii. else consider Ij unsatisfied

23. 3.3. if all premises Ij, j=1,N are satisfied then store the value A obtained from the RHS of R in WM 4. if A has value (in WM) then return SUCCESS 5. else 5.1 CS ? CS – R 5.2 repeat from 2 end. 1'. if A is primary data then 1'.1. Ask the user the value of A 1'.2. if the user knows the value of A then - store this value in WM - return SUCCESS 1’.3. else return FAIL

24. Example of RBS with forward chaining (a) R11: if Etapa is Verifica-Decor and exists o statuie and does not exist soclu then Add soclu la Obiecte-Neimpachetate ….. R17: if Etapa is Verifica-Decor then finish Etapa Verifica-Decor and start Etapa Obiecte-Mari

25. Example of RBS with forward chaining - cont R21: if Etapa is Obiecte-Mari and exists un obiect mare de ambalat and exists un obiect greu de ambalat and exists o cutie cu mai putin de trei obiecte mari then pune obiectul greu in cutie R22: if Etapa is Obiecte-Mari and exists un obiect mare de ambalat and exists o cutie cu mai putin de trei obiecte mari ….. then pune obiectul mare in cutie R28: if Etapa is Obiecte-Mari and exists un obiect mare de pus then foloseste o cutie noua R29: if Etapa is Obiecte-Mari then finish Etapa Obiecte-Mari and start Etapa Obiecte-Medii

26. Example OPS5 WME (literalize student name placed_in sex_stud) (literalize room number capacity free sex_room occupants) (vector-attribute occupants) room 111 4 2 F (Maria Elena) time tag (make room ^number 112 ^capacity 4 ^free 4 ^sex_cam nil ^occupants nil) (make student ^name Mihai ^placed_in nil ^sex-stud M)

27. (p atrib_stud_free_room (<Unplaced_student> (student ^name < stud_name> ^plased_in nil ^sex_stud <gender>)) (<Free_room> (room ^number <room_no> ^capacity <capacity_max> ^free <capacity_max>)) --> (modify < Unplaced_student > ^ plased_in < room_no >) (modify < Free_room > ^ occupants < stud_name > ^sex_room < gender > ^ free (compute < capacity_max >-1)).

28. 28 Example Jess (deftemplate person (slot firstname) (slot name) (slot age (type INTEGER)) )

29. Stud/room in Jess (deftemplate student (slot name) (slot sex) (slot placed_in) (slot special_considerations (default no))

30. 30 5. RBS strategies in OPS5 family Recognize-act cycle: Match Select Act WME = working memory element Identified by a "time tag" Instantiation: set of WME which satisfy a rule Conflict set Conflict resolution

31. 31 Recognize-act cycle Match Application of rule – condition - WMEs of WM satisfy LHS 2 aspects: match intra-element match inter-element (defrule teenager (person (firstName ?name) (age ?age)) => (printout t ?name " is " ?age " years old." crlf))

32. Recognize-act cycle match inter-element (defrule assign-private-room (student (name ?stud_name) (placed_in nil) (special_consideration yes)) (room (number ?room_no) (capacity 1) (vacancies 1) => …

33. 33 Conflict resolution Strategies Refraction = o aceeasi instantiere nu este executata de mai multe ori (2 instantieri sunt la fel daca au acelasi nume de regula si aceleasi time tags, in aceeasi ordine) Time of use = Se prefera instantierile care au identificat cu WMEs cu cele mai recente time tags (sau invers) Specificity = Au prioritate instantierile cu LHS specifice = nr de valori testate in LHS teste asupra: nume clasa, predicat cu 1 arg constanta, predicat cu un operator variabila legata

34. 34 Conflict resolution Strategia LEX Elimina din MC instantierile care au fost deja executate Ordoneaza instantierile pe baza momentului utilizarii Daca mai multe instantieri au aceleasi time tags, utilizeaza specificitate Daca mai multe instantieri au aceeasi specificitate alege arbitrar Strategia MEA – aceeasi dar utilizeaza primul time tag

35. 35 Efficiency Match – cam 80% din timpul ciclului recunoastere-actiune Fiecare WME este comparat cu fiecare conditie din fiecare regula O(R*FP), R – nr de reguli, F – nr WME, P nr mediu de conditii in LHS regula RETE match algorithm (1982 Forgy) Salveaza starea de match intre 2 cicluri recunoastere-actiune La crearea unui WME, acesta este comparat cu toate elementele conditie din program si este memorat impreuna cu fiecare element conditie cu care a identificat => Numai schimbarile incrementale din WM sunt identificate in fiecare ciclu O(R*F*P) aprox

36. Rule compilation Se compileaza conditiile regulilor intr-o retea de noduri de test Un test este un predicat cu o constanta sau variabila legata sau o disjunctie Procesul de match are loc numai la adaugarea sau la eliminarea unui WME (modify este retract urmat de assert) (gate (type or) (value true)) (gate (type or) (value false)) Node sharing

37. 37 Rule compilation Un pointer la noul WME este trecut prin retea, incepand de la nodul radacina Fiecare nod actioneaza ca un switch Cand un nod primeste un pointer la un WME, testeaza WME asociat. Daca testul reuseste, nodul se deschide si WME trece mai departe Altfel nu se intampla nimic Daca un WME va trece prin retea, va fi combinat cu alte WME care trec pentru a forma o instantiere in MC Pointerii la WME sunt trimisi prin reteaua RETE ca tokens = pointer + stare (assert sau retract)

38. 38 Types of node in RETE network Nod cu 1 intrare = test intra-element realizat de noduri cu 1 intrare fiecare nod efectueaza un test corespunzator unei conditii testarea aceleiasi variabile – tot noduri cu 1 intrare

39. 39 RBS Foarte multe Cele mai influente OPS5 ART CLIPS Jess Familia Web Rule languages In special RuleML si SWRL Interoperabilitatea regulilor

40. 40 RuleML RuleML Initiative - August 2000 Pacific Rim International Conference on Artificial Intelligence. RuleML Initiative dezvolta limbaje bazate pe reguli deschise XML/RDF Aceasta permite schimbul de reguli intre diferite sisteme, inclusiv componete software distribuite pe Web si sisteme client-server eterogene Limbajul RuleML – sintaxa XML pentru reprezentarea cunostintelor sub forma de reguli Integrarea cu ontologii: sistemul de reguli trebuie sa deriveze/utilizeze cunostinte din ontologie

41. 41 RuleML RuleML – se bazeaza pe Datalog Datalog = limbaj de interogare si reguli pentru baze de date deductive Subset al Prolog cu restrictii: argumente ne-functionale (constante sau variabile) limitari in nivelul de apeluri recursive variabilele din concluzie trebuie sa apara in predicate ne-negate din ipoteza Hornlog – Datalog extins cu variabile functionale (termeni generali)

42. 42 RuleML Reguli reactive = Observa/verifica anumite evenimente/conditii si executa o actiune – numai forward Constrangeri de integritate = reguli speciale care semnaleaza inconistente cand se indeplinesc anumite conditii – numai forward Reguli de inferenta (derivare) = reguli reactive speciale cu actiuni care adauga o concluzie daca conditiile (premisele sunt adevarate) - Se pot aplica atat forward cat si backward Fapte = reguli de inferenta particulare

43. RuleML Reguli reactive <rule> <_body> <and> prem1 ... premN </and> </_body> <_head> action </_head> </rule> Constrangeri de integritate <ic> <_head> inconsistency </_head> <_body> <and> prem1 ... premN </and> </_body> </ic> implementate ca <rule> <_body> <and> prem1 ... premN </and> </_body> <_head> <signal> inconsistency </signal> </_head> </rule>

44. RuleML Reguli de inferenta/derivare <imp> <_head> conc </_head> <_body> <and> prem1 ... premN </and> </_body> </imp > implementate prin <rule> <_body> <and> prem1 ... premN </and> </_body> <_head> <assert> conc </assert> </_head> </rule> Fapte <atom> <_head> conc </_head> </atom> implementate prin <imp> <_head> conc </_head> <_body> <and> </and> </_body> </imp>

45. RuleML Fapte

46. Reguli de inferenta/derivare

47. Example of RBS with backward chaining (b) R1: if X has par then X is mamal R2: if X feeds puii with milk then X is mamal R3: if X is mamal and X has pointed teeth and X has falci then X is carnivor R4: if X is carnivor and X is brown and X has pete then X is cheetah R5: if X is carnivor and X is brown and X has strips then X is tiger