Systemy ekspertowe 2

Shell RETE Postscriptum

Sztuczna Inteligencja i Systemy Ekspertowe
Systemy ekspertowe (2)

Aleksander Pohl
http://apohllo.pl/dydaktyka/ai

Wy˙ sza Szkoła Zarzadzania i Bankowo´ ci
z ˛ s

12 maja 2009

Aleksander Pohl WSZiB


Plan prezentacji

Shell

RETE

Postscriptum



Shell ekspertowy

◮ Formalizm reprezentacji wiedzy
◮ Opracowanie mechanizmu wnioskowania
◮ Interfejs u˙ ytkownika
z



Reguły

◮ :- op(100, xfx, [has, gives,
’does not’, eats, lays, isa]).
◮ :- op(100, xf, [swims, flies]).
◮ rule1 :: if
Animal has hair
or
Animal gives milk
then
Animal isa mammal.



Reguły 2

◮ rule2 :: if
Animal has feathers
or
Animal flies and
Animal lays eggs
then
Animal isa bird.



Reguły 3

◮ rule3 :: if
Animal isa mammal and
( Animal eats meat
or
Animal has ’pointed teeth’ and
Animal has claws and
Animal has ’forward pointing eyes’ )
then
Animal isa carnivore.



Reguły 4
◮ rule4 :: if
Animal isa carnivore and
Animal has ’tawny color’ and
Animal has ’dark spots’
then
Animal isa cheetah.
◮ rule5 :: if
Animal isa carnivore and
Animal has ’tawny color’ and
Animal has ’black stripes’
then
Animal isa tiger.



Reguły 5

◮ rule6 :: if
Animal isa bird and
Animal ’does not’ fly and
Animal swims
then
Animal isa penguin.
◮ rule7 :: if
Animal isa bird and
Animal isa ’good flyer’
then
Animal isa albatross.



Reguły 6
◮ fact :: X isa animal :-
member( X, [cheetah, tiger, penguin,
albatross]).
◮ askable( _ gives _, ’Animal’ gives ’What’).
◮ askable( _ flies, ’Animal’ flies).
◮ askable( _ lays eggs, ’Animal’ lays eggs).
◮ askable( _ eats _, ’Animal’ eats ’What’).
◮ askable( _ has _, ’Animal’ has
’Something’).
◮ askable( _ ’does not’ _, ’Animal’ ’does
not’ fly).
◮ askable( _ swims, ’Animal’ swims).
◮ askable( _ isa ’good flyer’, ’Animal’ isa
’good flyer’).


Zasady działania

◮ Q jest faktem – to odpowiada ’Q is true’
◮ Dla reguły ’if Condition then Q’ badaj Condition i zbuduj
odpowied´ z
◮ Je´ li Q jest „askable” zapytaj o to u˙ ytkownika
s z
◮ Je´ li Q jest postaci ’Q1 and Q2’ badaj Q1, dla fałszywego
s
odpowied´ jest ’Q is false’, w przeciwnym razie zbadaj Q2
z
i zbuduj odpowiednio odpowied´ z
◮ Je´ li Q jest postaci ’Q1 or Q2’ badaj Q1 oraz zbadaj Q2
s
i zbuduj odpowiednio odpowied´ z



Dialog (1)

◮ Question please:
◮ peter isa tiger
◮ Is it true: peter has hair?
◮ yes
◮ Is it true: peter eats meat?
◮ no
◮ Is it true: peter has pointed teeth?
◮ yes



Dialog (2)
◮ Is it true: peter has claws ?
◮ why
◮ To investigate by rule3, peter isa carnivore
To investigate by rule5, peter isa tiger
This was your question
◮ Is it true: peter has claws ?
◮ yes
◮ Is it true: peter has black stripes ?
◮ yes
◮ Is it true: peter has tawny colour ?
◮ yes



Dialog (3)
◮ (peter isa tiger) is true.
◮ Would you like to see how ?
◮ yes
◮ Peter isa tiger
Was derived by rule5 from Peter isa carnivore
And
◮ Peter has tawny colour
Was told
And
◮ Peter has black stripes
Was told.



Dialog (4)
◮ Peter isa carnivore
Was derived by rule3 from
◮ Peter isa mammal
And
◮ Peter has pointed teeth
Was told
And
◮ Peter has claws
Was told
And
◮ Peter has forward pointing eyes
Was told.



Dialog (5)

◮ Peter isa mammal
Was derived by rule1 from
Peter has hair
Was told.



Predykaty składowe shella

◮ expert – uruchami shell
◮ explore(Goal, Trace, Answer)
Goal – pytanie
Trace – lista celów od pytania głównego do obecnego
Answer – drzewo dowodu
◮ useranswer(Goal, Trace, Answer) – obsługuje
dialog z u˙ ytkownikiem
z
◮ present(Answer) – odpowiada na pytanie „How”



Przeszukiwanie

◮ explore(Goal, Trace, Answer)
◮ Goal
(X has feathers) or (X flies) and (X lays
eggs)
◮ Trace (drzewa przodków)
[(peter isa carnivore) by rule3, (peter isa
tiger) by rule5]
◮ Answer (drzewo dowodu potomka)
(peter eats meat) is false was told



Implementacja explore (1)
◮ explore( Goal, Trace, _) :-
copy_term( Goal, Copy),
member( Copy by Rule, Trace),
instance_of( Copy, Goal), !, fail.
◮ explore(Goal, Trace,
Goal is true was ’found as a fact’) :-
fact :: Goal.
◮ explore(Goal, Trace, Goal is TruthValue
was ’derived by’ Rule from Answer) :-
Rule :: if Condition then Goal,
explore(Condition, [Goal by Rule | Trace],
Answer),
truth(Answer, TruthValue).



◮ explore(Goal1 and Goal2, Trace, Answer) :-
!,
explore(Goal1, Trace, Answer1),
continue(Answer1, Goal1 and Goal2, Trace,
Answer).
◮ explore( Goal1 or Goal2, Trace, Answer) :-
exploreyes(Goal1, Trace, Answer);
exploreyes(Goal2, Trace, Answer).



◮ explore(Goal1 or Goal2, Trace, Answer1 and
Answer2) :- !,
not exploreyes(Goal1, Trace, _),
not exploreyes(Goal2, Trace, _),
explore(Goal1, Trace, Answer1),
explore(Goal2, Trace, Answer2).
◮ explore(Goal, Trace, Goal is Answer was
told) :-
useranswer(Goal, Trace, Answer).
◮ exploreyes(Goal, Trace, Answer) :-
explore(Goal, Trace, Answer),
positive(Answer).



Implementacja

◮ instance_of(Term, Term1) :-
copy_term(Term1, Term2),
numbervars(Term2, 0, _), !,
Term = Term2.
◮ numbervars( Term, N, Nplus1) :-
var( Term), !, Term = var/N,
Nplus1 is N + 1.



Implementacja cd.

◮ numbervars( Term, N, M) :-
Term =.. [Functor | Args],
numberargs( Args, N, M).
◮ numberargs( [], N, N) :- !.
◮ numberargs( [X | L], N, M) :-
numbervars( X, N, N1),
numberargs( L, N1, M).



Implementacja cd.

◮ truth( Question is TruthValue was Found,
TruthValue) :- !.
◮ truth( Answer1 and Answer2, TruthValue) :-
truth( Answer1, true), truth( Answer2,
true), !,
TruthValue = true ; TruthValue = false.
◮ positive( Answer) :-
truth( Answer, true).
◮ negative( Answer) :-
truth( Answer, false).



Pobieranie odpowiedzi
◮ getreply(R) :-
read(Answer),
means(Answer, Reply), !;
nl, write(’Answer Unknown’),nl,
getreply( Reply).
◮ means(yes,yes).
◮ means(y,yes).
◮ ...
◮ getreply(Replay),
(Reply = yes, interpretyes(...) ;
Reply = no, interpretno(...) ;
...)



Interpretacja odpowiedzi

◮ askable( X eats Y, ’Animal’ eats
’Something’).
◮ ?- useranswer( X eats Y , [] , Answer).
◮ Is it true: Animal eats Something?
◮ yes.
Animal = peter
Something = meat
Answer = true
X = peter
Y = meat



RETE

◮ Algorytm pattern-matching rozumowania wprzód
◮ C.L. Forgy z Carnegi Mellon w 1979
◮ Optymalizacja rozpatyrwania LHS (left-hand side) oraz
RHS (right-hand side)
◮ z s´
Zło˙ ono´ c:
◮ s´
R – ilo´ c reguł
◮ s´
F – ilo´ c faktów
◮ s´ ´
P – ilo´ c dopasowan
naiwna implementacja: O(R ∗ F P )
RETE: O(R ∗ F ∗ P)



Pojecia
˛

◮ Kompilacja lewych stron LHS => RHS (if .. then)
◮ Zamieniamy reguły na zestawy wezłów z jednym wej´ ciem
˛ s
(ﬁltry) i wezłów z dwoma wej´ ciami (złaczenia)
˛ s ˛
◮ Filtry sprawdzaja czy dany fakt pasuje do elementu LHS
˛
◮ Złaczenia zapamietuja fakty, które je cz˛ sciowo spełniły
˛ ˛ e´



Przykład

◮ if x, y, z then example-2
◮ if x, y then example-3
◮ LHS => RHS
◮ (x,y,z) => example-2
◮ (x,y) => example-3



Materiały zródłowe
´

◮ L.Sterling, E.Shapiro - „The Art Of Prolog”
◮ Ivan Bratko - „Prolog – Programming For Artiﬁcial
Intelligence”
◮ Slajdy zostały przygotowane za zgoda˛
dr. Michała Korzyckiego na podstawie jego wykładu.



Dziekuje!
˛ ˛


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