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Artificial Intelligence
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1. Artificial Intelligence First-Order Logic (FOL)

2. Outline of this Chapter • The need for FOL? • What is a FOL? • Syntax and semantics of FOL • Using FOL

3. Pros and cons of propositional logic Propositional logic is declarative:pieces of syntax correspond to facts.  Propositional logic has expressive power to deal with partial information, using disjunction & negation. • (unlike most data structures and databases) • Propositional logic is compositional: - the meaning of a sentence is a function of the meaning of its parts • meaning of B1,1 P1,2 is derived from meaning of B1,1 and of P1,2 • Meaning in propositional logic is context-independent • (unlike natural language, where meaning depends on context)  Propositional logic has very limited expressive power to describe an Env with many objects concisely • (unlike natural language- syntax & semantic of English make it possible to describe the Env concisely. • E.g., cannot say "pits cause breezes in adjacent squares“ • except by writing a separate rule about B & P for each square.

4. First-order u logic (FOL) • FOL more expressive language than the PL. • FOL is a general-purpose representation language that is based on an ontological commitment to the existence of objects & relations in the world. • It builds on simple propositional logic by adding objects, predicates, variables and quantifiers. • Whereas propositional logic assumes the world contains facts (can be T/F), • FOL (like natural language) assumes the world contains • Objects: people, houses, numbers, colors, baseball games, wars, … • Relations: brother of, bigger than, part of, comes between, … • Properties: unary relation such as red, round • Functions: relations in which there is only 1 value for a giver input. i.e. father of, best friend, plus, …

5. Example • “One Plus Two Equals Three “ Objects: One, Two, Three Relations: Equals Functions: Plus • “Square neighboring the wumpus are smelly“ Objects: wumpus, square Relations: neighboring Property: smelly

6. Syntax and semantics • In Propositional logic – every expression is a sentence • In FOL = Sentences + Terms (which represents objects) • Sentences - are built using quantifiers and predicate symbols • Term: Logical expression that refers to an object = function (term1,...,termn) or constant or variable. = Brother (Samir, Ali) Ali, x,y

7. Syntax of FOL: Basic elements The basic syntactic elements of FOL are the symbols that stand for objects, relations & functions. • Constants (stand for objects) Sarah, A, B,... • Predicates (stand for relation) Brother, Round,... • Functions (stand for function) FatherOf,... • Variables x, y, a, b,... • Connectives , , , ,  • Equality = • Quantifiers , 

8. Atomic sentences • It is formed from a predicate symbol followed by a parenthesized list of terms. • Atomic sentence = predicate (term1,...,termn) or term1 = term2 E.g. Sister (Mona, Manal) • They can have arguments that are complex terms E.g. Married(FatherOf(Ali),Sister (Mona))

9. Complex sentences • Complex sentences are made from atomic sentences using logical connectives e.g. •  Sister(Mona,Alia) is true when Mona is not the sister of Alia. • Older (Reem, 20)  Younger(Reem, 20) if Reem is older than 20, then she is not younger than 20.

10. Truth in first-order logic • Sentences are true with respect to a model and an interpretation • Model contains objects (domainelements) and relations among them • Interpretation specifies referents for constant symbols → objects predicate symbols → relations function symbols → functional relations • An atomic sentence predicate(term1,...,termn) is true iff the objects referred to by term1,...,termnare in the relation referred to by predicate

11. Truth Example • Consider the interpretation in which • Sara  Sara Ibrahim • Mona  Mona Ibrahim • Sister  the sisterhood relation • Under this interpretation, Sister(Sara; Mona) is true just in case Sara Ibrahim and Mona Ibrahim are in the sisterhood relation in the model

12. Models for FOL: Example • A model containing 5 objects. These objects may be related in various ways: • Binary relation – brother & on head • Unary relations (properties) – person, crown & king • Unary function – left leg

13. Quantification • Quantification is a formula constructor that produces new formulas from old ones. • Quantifier is a symbol- let us express properties of entire collections of objects, instead of listing the objects by name. • In natural language- for all, for some; many, few, a lot, etc • FOL contains 2 standard quantifiers: • Universal • existential

14. Universal quantification • <variables> <sentence> e.g. All cats are mammals:  x Cats(x) => Mammals(x), for all x, if x is a cat, then x is a mammal • Universal quantification makes statements about every object. • Universal quantifiers often used with "implies" to form "rules.“ • Everyone at PSU is smart :  x At(x, PSU) => Smart(x) •  x P (any logical expression) is equivalent to the conjunction of instantiations of P At( Sara, PSU) => Smart(Sara)  At(Mary, PSU) => Smart(Mary)  ... • Typically, is the main connective with  • Common mistake: using  as the main connective with :

15. Existential quantification •  <variables> <sentence> • Existential quantification makes statements about some object. • Someone at PSU is smart: x At(x, PSU)  Smart(x) •  x P is equivalent to thedisjunctions of instantiations of P At( Sara, PSU) => Smart(Sara)  At(Mary, PSU) => Smart(Mary)  ... • Typically, is the main connective with  • Common mistake: using  as the main connective with :

16. Properties of quantifiers To express more complex sentences, use multiple quantifiers. • x y is the same as yx • x y is the same as yx •  x y is not the same as y x •  x y Loves (x,y) “For every person, there is someone that person loves” •  y x Loves(x,y) “There is someone who is loved by every body” • Quantifier duality: each can be expressed using the other (& are connected with each other through negation) • x Likes(x,IceCream) x Likes(x,IceCream) •  x Likes (x, Cabbage) x Likes(x, Cabbage) The deMorgan rules for quantified and unquantified sentences are as follows  x P = x P P  Q = (P V Q )  x P = x P (P  Q) = P V Q x P = x P P  Q = (P V Q ) x P =  x P P V Q = (P  Q )

17. Equality • term1 = term2is true under a given interpretation if and only if term1andterm2refer to the same object e.g. Mother(Mona) = Jenny • The equality symbol can be used with negation to insist that two terms are not the same object. e.g. Mona has at least 2 sisters. x,y Sister(Mona,x)  Sister(Mona,y)  (x=y)

18. Examples • All food are edible • Brothers are siblings • “Sibling" is symmetric relation ship (quantifiers of the same type) • One's mother is one's female parent Solutions: •  x food( x) => edible( x) •  x, y Brother(x, y)  Sibling(x, y). •  x, y Sibling(x, y) Sibling(y, x). •  m, c Motherof(c) = m  (Female(m)  Parent(m, c))

19. Interacting with FOL KBs • To add sentences to a knowledge base KB, we would call TELL. Such sentences are called assertions. e.g. TELL (KB, Mother(Nora)) // assert that Nora is a Mother TELL(KB,  x Mother(x)  Person (x)) // Mothers are persons • To ask questions of the KB, we use ASK • Questions asked using ASK are called queries e.g.ASK (KB,Mother(Nora))

20. In-class Activity • Abeer, Mona and linda belong to the champions Club. • Every member of the club is either skier or a mountain climber . • No mountain climber likes rain, and all skiers like snow • linda dislikes whatever Abeer likes and likes whatever Abeer dislikes • Abeer likes rain and snow • Is there any member of the club who is climber but not skier? • Translate this problem into FOL Sentences?

21. Solution • S(x) means x is a skier • M(x) means x is a mountain climber • L(x,y) means x likes y • The English sentences in the prev slide can be translated into the following FOL:  x Clubmember(x) => S(x) V M (x) ~  x M(x)  L(x,rain)  x S(x) => L(x,snow)  y L(linda, y) <=> ~L(Abeer,y) L(Abeer, rain) ^L(Abeer,snow)

22. Summary • FOL can be used as the representation language for a KB agent. • FOL is more powerful than propositional logic • A possible world for FOL is defined by a set of : • objects and relations are semantic primitives • syntax: constants, functions, predicates, equality, quantifiers • Complex sentences use connectives just like propositional logic, & quantified sentences allow the expression of general rules.

23. End of lecture