1. Inference in first-order logic part 1
Chapter 9
Modified by Vali Derhami

2. Outline Reducing first-order inference to propositional inference
Unification
Generalized Modus Ponens
Forward chaining
Backward chaining
Resolution

3. Universal instantiation (UI)
Subst(, a) denote the result of applying the substitution  to the sentence a.
Every instantiation of a universally quantified sentence is entailed by it:
Here v is variable, and α is a sentence.
v α Subst({v/g}, α)
for any variable v and ground term (ترم بدون متغیر ( g
E.g., x King(x)  Greedy(x)  Evil(x) yields:
{x/John}: King(John)  Greedy(John)  Evil(John)
{x/Richard}: King(Richard)  Greedy(Richard)  Evil(Richard)
{x/Father(John)}: King(Father(John))  Greedy(Father(John))  Evil(Father(John)). .
substitution : جانشین سازی

4. Existential instantiation (EI)
For any sentence α, variable v, and constant symbol k that does not appear elsewhere in the knowledge base:
v α
Subst({v/k}, α)
E.g., x Crown(x)  OnHead(x,John) yields:
provided C1 is a new constant symbol, called a Skolem constant
توجه شود C1 نباید در هیچ کجای دیگر از KB امده باشد.
پایگاه دانش جدید با پایگاه دانش قدیمی هم ارز نیست، لیکن هم ارز استنباطی است. اگر پایگاه دانش اصلی ارضا پذیر باشد، پایگاه دانش جدید نیز ارضا پذیر است.

5. Reduction to propositional inference
Suppose the KB contains just the following:
x King(x)  Greedy(x)  Evil(x)
King(John)
Greedy(John)
Brother(Richard,John)
Instantiating the universal sentence in all possible ways, we have:
King(John)  Greedy(John)  Evil(John)
King(Richard)  Greedy(Richard)  Evil(Richard)
The new KB is propositionalized(فرم گزاره ای):
proposition symbols are
King(John), Greedy(John), Evil(John), King(Richard), etc.

6. Reduction contd.
Every FOL KB can be propositionalized so as to preserve entailment
(هر پایگاه دانش مرتبه اول می تواند گزاره ای شود به نحوی که ایجاب حفظ شود.)
(A ground sentence is entailed by new KB iff entailed by original KB)
Idea: Propositionalize KB and query, apply resolution, return result
Problem: with function symbols, there are infinitely many ground terms,
e.g., Father(Father(Father(John)))

7. Reduction contd.
Theorem: Herbrand (1930). If a sentence α is entailed by an FOL KB, it can be entailed by a finite subset of the propositionalized KB
اگر جمله يα توسط يک FOL KB قابل ايجاب باشد آنگاه اين جمله توسط زيرمجموعه محدودي از پايگاه دانش گزاره اي سازي شده قابل ايجاب است.
Idea: For n = 0 to ∞ do
create a propositional KB by instantiating with depth 1-n terms
see if α is entailed by this KB
بدین معنی که ابتدا زیر مجموعه گزاره ای از پایگاه دانش با عمق یک (ثوابت جا گذاری شده و برای توابع تنها عمق اول لحاظ شود) ایجاد و استنتاج انجام اگر نتیجه ایجاب شد که تمام است وگرنه برای عمق دوم و ....
Problem: works if α is entailed, loops if α is not entailed

8. Reduction contd.
Theorem: Turing (1936), Church (1936):The question of entailment for first-order logic is semidecidable (نیمه تصمیم پذیر)
that is, algorithms exist that say yes to every entailed sentence, but no algorithm exists that also says no to every nonentailed sentence.
الگوريتم هايي وجود دارند كه براي هر جمله قابل ايجاب پاسخ بله را توليد مي كنند، ولي الگوريتمي وجود ندارد كه براي هر جمله اي كه قابل ايجاب نباشد پاسخ خير توليد كند.

9. Problems with propositionalization
Propositionalization seems to generate lots of irrelevant sentences.
E.g., from:
x King(x)  Greedy(x)  Evil(x)
King(John)
y Greedy(y)
Brother(Richard,John)
it seems obvious that Evil(John), but propositionalization produces lots of facts such as Greedy(Richard) that are irrelevant
با p تا predicate از نوع k تايي و n ثابت p·nk نمونه سازي وجود دارد.

10. Unificationیکسان سازی
We can get the inference immediately if we can find a substitution θ such that King(x) and Greedy(y) match King(John) and Greedy(John)
Unify(p,q) = θ if Subst(θ,p) = Subst(θ,q)
p q θ
Knows(John,x) Knows(John,Jane)
Knows(John,x) Knows(y,OJ)
Knows(John,x) Knows(y,Mother(y))
Knows(John,x) Knows(x,OJ)

11. Unification
We can get the inference immediately if we can find a substitution θ such that King(x) and Greedy(x) match King(John) and Greedy(y)
θ = {x/John,y/John} works
Unify(p,q) = θ if Subst(θ,p) = Subst(θ,q)
p q θ
Knows(John,x) Knows(John,Jane) {x/Jane}
Knows(John,x) Knows(y,OJ)
Knows(John,x) Knows(y,Mother(y))
Knows(John,x) Knows(x,OJ)

12. Unification
We can get the inference immediately if we can find a substitution θ such that King(x) and Greedy(x) match King(John) and Greedy(y)
θ = {x/John,y/John} work
Unify(α,β) = θ if Subst(θ,α) = Subst(θ,β)
p q θ
Knows(John,x) Knows(John,Jane) {x/Jane}}
Knows(John,x) Knows(y,OJ) {x/OJ,y/John}}
Knows(John,x) Knows(y,Mother(y))
Knows(John,x) Knows(x,OJ)

13. Unification
We can get the inference immediately if we can find a substitution θ such that King(x) and Greedy(x) match King(John) and Greedy(y)
θ = {x/John,y/John} works
Unify(α,β) = θ if Subst(θ,α) = Subst(θ,β)
p q θ
Knows(John,x) Knows(John,Jane) {x/Jane}}
Knows(John,x) Knows(y,OJ) {x/OJ,y/John}}
Knows(John,x) Knows(y,Mother(y)) {y/John,x/Mother(John)}}
Knows(John,x) Knows(x,OJ)

14. Unification
We can get the inference immediately if we can find a substitution θ such that King(x) and Greedy(x) match King(John) and Greedy(y)
θ = {x/John,y/John} works
Unify(α,β) = θ if αθ = βθ
p q θ
Knows(John,x) Knows(John,Jane) {x/Jane}}
Knows(John,x) Knows(y,OJ) {x/OJ,y/John}}
Knows(John,x) Knows(y,Mother(y)) {y/John,x/Mother(John)}}
Knows(John,x) Knows(x,OJ) {fail}
Standardizing apart (جداسازی استاندارد) eliminates overlap of variables, e.g., Knows(z17,OJ)
{x/Oj, z17/John}

15. Unification To unify Knows(John,x) and Knows(y,z),
θ = {y/John, x/z } or θ = {y/John, x/John, z/John}
The first unifier is more general than the second.
There is a single most general unifier (MGU) (عمومی ترین یکسان ساز)that is unique up to renaming of variables.
MGU = { y/John, x/z }

16. The unification algorithm

17. The unification algorithm

18. Generalized Modus Ponens (GMP)
p1', p2', … , pn', ( p1  p2  …  pn q) qθ
Example:
p1' is King(John) p1 is King(x)
p2' is Greedy(y) p2 is Greedy(x)
θ is {x/John,y/John} q is Evil(x)
q θ is Evil(John)
GMP used with KB of definite clauses (exactly one positive literal)
(Clause: ترکیب OR از لیترالها)
definite clauses:Horn clauses with exactly one positive literal
کلاز های معین: ترکیب or از لیترالها که دقیقا یکی از انها مثبت است
All variables assumed universally quantified
(فرض میشود همه متغیرها سور عمومی دارند)
where pi'θ = pi θ for all i
, pi,p’I , q and atomic sentence
هورنفرم: تركيب OR از ليترال ها كه حداكثر يكي از آنها مثبت است.

19. Soundness of GMP Need to show that p1', …, pn', (p1  …  pn  q) ╞ qθ
provided that pi'θ = piθ for all i
Lemma: For any sentence p, we have p ╞ pθ by UI
(p1  …  pn  q) ╞ (p1  …  pn  q)θ = (p1θ  …  pnθ  qθ)
p1', \; …, \;pn' ╞ p1'  …  pn' ╞ p1'θ  …  pn'θ
pi'θ= piθ
From 1 and 2 and 3 , qθ follows by ordinary Modus Ponens

20. Example knowledge base
The law says that it is a crime for an American to sell weapons to hostile (متخاصم)nations. The country Nono, an enemy of America, has some missiles (موشک), and all of its missiles were sold to it by Colonel West, who is American.
قانون می گوید که برای یک آمریکایی فروش اسلحه به کشورهای متخاصم جرم می باشد. کشور نونو که یکی از دشمنهای امریکا هستتعدادی موشک دارد و همه این موشکلها توسط کلنل وست که امریکایی هست به آنها فروخته شده است.
Prove that Colonel West is a criminal(مجرم)
قانون می گوید که برای یک آمریکایی فروش اسلحه به کشورهای متخاصم جرم می باشد. کشور نونو که یکی از دشمنهای امریکا هستتعدادی موشک دارد و همه این موشکلها توسط کلنل وست که امریکایی هست به آنها فروخته شده است.

21. Example knowledge base contd.
... it is a crime for an American to sell weapons to hostile nations:
R1: American(x)  Weapon(y)  Sells(x,y,z)  Hostile(z)  Criminal(x)
Owns(Nono,M1) and Missile(M1)
… all of its missiles were sold to it by Colonel West
R2: Missile(x)  Owns(Nono,x)  Sells(West,x,Nono)
Missiles are weapons:
R3: Missile(x)  Weapon(x)
An enemy of America counts as "hostile“:
R4: Enemy(x,America)  Hostile(x)
West, who is American …
American(West)
The country Nono, an enemy of America …
Enemy(Nono,America)

22. Example knowledge base contd.
R1: American(x)  Weapon(y)  Sells(x,y,z)  Hostile(z)  Criminal(x)
R2: Missile(x1)  Owns(Nono,x1)  Sells(West,x1,Nono)
R3: Missile(x2)  Weapon(x2)
R4: Enemy(x3,America)  Hostile(x3)
Owns(Nono,M1)
Missile(M1)
American(West)
Enemy(Nono,America)

23. Forward chaining proof

24. Forward chaining proof
R2: Missile(x1)  Owns(Nono,x1)  Sells(West,x1,Nono)
R3: Missile(x2)  Weapon(x2)
R4: Enemy(x3,America)  Hostile(x3)
Owns(Nono,M1)
Missile(M1)
American(West)
{x3/Nono}, {x2/M1}, {x1/M1}

25. Forward chaining proof
R1: American(x)  Weapon(y)  Sells(x,y,z)  Hostile(z)  Criminal(x)
{x/west, y/M1, z/Nono}
حقایق اولیهن در لایه پایین ، حقایق استناج شده در مرحله اول در لایه میانی، و در لایه آخر حقایق استناج شده در مرحله دوم

26. Forward chaining algorithm

27. Properties of forward chaining
Sound and complete(کامل و صحیح) for first-order definite clauses
Datalog clauses= first-order definite clauses + no functions
FC terminates for Datalog in finite number of iterations
FC براي Datalog با تعداد تكرارهاي محدودي متوقف مي شود.
May not terminate in general if α is not entailed
در حالت كلي، اگر پايگاه دانشα را ايجاب نکند، ممكن است متوقف نشود.
This is unavoidable: entailment with definite clauses is semidecidable (نیمه تصمیم پذیر)
اين امر اجتناب ناپذير است: ايجاب با Clauseهاي معين نيمه تصميم پذير است

28. Efficiency of forward chaining
Incremental forward chaining: no need to match a rule on iteration k if a premise wasn't added on iteration k-1
در تکرار k نياز به تطبيق قانوني كه هيچ كدام از شرايطش در تكرار k-1 اضافه نشده اند، نيست
 match each rule whose premise contains a newly added positive literal
Matching itself can be expensive:
Database indexing allows O(1) retrieval of known facts
شاخص بندي پايگاه داده Database indexing اجازه مي دهد كه حقايق شناخته شده در زمان O(1) بازيابي شوند.
e.g., query Missile(x) retrieves Missile(M1)
Forward chaining is widely used in deductive (استقرایی) databases