First Order Logic (Artificial Intelligence)

First-Order Logic

Chapter 8

Outline

• Why FOL?

• Syntax and semantics of FOL

• Using FOL

• Wumpus world in FOL

• Knowledge engineering in FOL

Pros & Cons of Propositional logic

Propositional logic is declarative– (unlike most data structures and databases)

Propositional logic is compositional:– 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– (unlike natural language)– E.g., cannot say "pits cause breezes in adjacent squares“

• except by writing one sentence for each square

–

• Propositional logic allows partial/disjunctive/negated information

First-order (Predicate) logic• Whereas propositional logic assumes the

world contains facts, first-order logic (like natural language) assumes the world contains

• Relations: Inside(car, passenger), Beside(driver, passenger), BrotherOf(person, person), BiggerThan(object, object), Inside(), part of(), HasColor(), OccurredAfter(), Owns(), ComesBetween(), …

• Objects: wheel, door, body, engine, seat, car, passenger, driver, people, houses, numbers, theories, Ronald McDonald, colors, baseball games, wars, centuries, …

First-order (Predicate) logic

• Functions: ColorOf(car), FatherOf(person), BestFriend(person), ThirdInningOf(), OneMoreThan(), EndOf(), …

• Properties: Color(car), IsOpen(door), IsOn(engine)

• Functions are relations with single value for each object

Semantics/Interpretation• there is a correspondence between

– functions, which return values– predicates, which are true or false

• Function: father_of(Mary) = Bill• Predicate: father_of(Mary, Bill)

• Example : “One plus two equals three”– Objects: one, two, three, one plus two– Relations:equals– Properties:--– Functions:plus (“one plus two” is the name of the

object obtained by applying function plus to one and two; three is another name for this object)

• Example 2:“Squares neighboring the Wumpusare smelly”– Objects: Wumpus, square– Relations: neighboring– Properties: smelly– Functions:--

Semantics/Interpretation

Syntax of FOL: Basic elements

• Constants KingJohn, 2, NUS,...

• Predicates Brother, >,...

• Functions Sqrt, LeftLegOf,...

• Variables x, y, a, b,...

• Connectives , , , , • Equality =

• Quantifiers ,

Atomic sentences

Atomic sentence = predicate (term1,...,termn) or term1 = term2

Term = function (term1,...,termn) or constant or variable

• E.g SchoolOf(Ali)• Colleague(TeacherOf(Moona), TeacherOf( Ali))• >((+ x y), x)

Complex sentences

• Complex sentences are made from atomic sentences using connectives

S, S1 S2, S1 S2, S1 S2, S1 S2,

• E.g. Colleague(Huma; Mehnaz) Colleague(Mehnaz; Huma)

• Student(Ali; Moona ) Teacher(Moona; Ali)

•

Truth in first-order logic• Sentences are true with respect to a model and an interpretation

• Model contains objects (domain elements) and relations among them

• Interpretation specifies referents forconstant symbols → objectspredicate symbols → relationsfunction symbols → functional relations

• An atomic sentence predicate(term1,...,termn) is trueiff the objects referred to by term1,...,termn

are in the relation referred to by predicate

–•

Models for FOL: Example

Truth Example

• Consider the interpretation in which

• Richard→ Richard the Lionheart

• John → the evil King John

• Brother → the brother relation

• Under this interpretation, • Brother(Richard; John) is true just in case

Richard the Lionheart and the evil King John are in the brother relation in the model

Quantifiers

• Expressing sentences about collections of objects without enumeration (naming individuals)

• E.g.– All Trojans are clever – Someone in the class is sleeping

• Universal quantification (for all):

• Existential quantification (there exists):

Universal quantification <variables> <sentence>

Everyone at AI is smart:x At(x,AI) Smart(x) x P is true in a model m iff P is true with x being each

possible object in the model

• Roughly speaking, equivalent to the conjunction of instantiations of P

At(Wasi,AI) Smart(Wasi) At(M,AI) Smart(M) ...

• At(Ali,AI) Smart(Ali)

A common mistake to avoid

• Typically, is the main connective with • Common mistake: using as the main

connective with :x At(x,AI) Smart(x)

means “Everyone is at AI and everyone is smart”

•

Existential quantification <variables> <sentence>

• Someone at NUS is smart: x At(x,NUS) Smart(x)$

x P is true in a model m iff P is true with x being some possible object in the model

• Roughly speaking, equivalent to the disjunction of instantiations of P

At(KingJohn,NUS) Smart(KingJohn) At(Richard,NUS) Smart(Richard) At(NUS,NUS) Smart(NUS) ...

••

•

Another common mistake to avoid

• Typically, is the main connective with

• Common mistake: using as the main connective with :

x At(x,NUS) Smart(x)

is true if there is anyone who is not at NUS!

••

Properties of quantifiers x y is the same as y x x y is the same as y x x y Loves(x,y)

– “There is a person who loves everyone in the world” y x Loves(x,y)

– “Everyone in the world is loved by at least one person”

x y is not the same as y x

Fun with sentences• Brothers are siblings x;yBrother(x,y) Sibling(x,y)• “Sibling” is symmetric x,y Sibling(x,y)↔Sibling(y,x)• One’s mother is one’s female parent x,y Mother(x,y)↔ (Female(x) ^

Parent(x,y))• A first cousin is a child of a parent’s sibling x;y FirstCousin(x,y)↔ p,ps

Parent(p,x)^Sibling(ps,p)^Parent(ps,y))

Some more Examples• Every gardener likes the sun.

x gardener(x) => likes(x,Sun)• You can fool some of the people all of the time. x t (person(x) ^ time(t)) => can-fool(x,t)• You can fool all of the people some of the time.• x t (person(x) ^ time(t)) => can-fool(x,t)• All purple mushrooms are poisonous. x (mushroom(x) ^ purple(x)) => poisonous(x)• No purple mushroom is poisonous.• ¬( x) purple(x) ^ mushroom(x) ^ poisonous(x) • or, equivalently,• ( x) (mushroom(x) ^ purple(x)) => ¬poisonous(x)

Duality and Equality• Duality:• All likes icecream = there is not one person who

doesn’t like icecream x Likes(x,IceCream) x Likes(x,IceCream)• Some people likes broccoli= not all people likes

broccoli x Likes(x,Broccoli) x Likes(x,Broccoli)

• Equality: term1 = term2 is true under a given interpretation if and only if term1 and term2 refer to the same object

E.g., definition of Sibling in terms of Parent:x,y Sibling(x,y) [(x = y) m,f (m = f)

Parent(m,x) Parent(f,x) Parent(m,y) Parent(f,y)]

Interacting with FOL KBs• Suppose a wumpus-world agent is using an FOL KB and perceives a smell

and a breeze (but no glitter) at t=5:

Tell(KB,Percept([Smell,Breeze,None],5))Ask(KB,a BestAction(a,5))

• I.e., does the KB entail some best action at t=5?• Answer: Yes, {a/Shoot} ← substitution (binding list)

• Given a sentence S and a substitution σ,• Sσ denotes the result of plugging σ into S; e.g.,

S = Smarter(x,y)σ = {x/Hillary,y/Bill}Sσ = Smarter(Hillary,Bill)

• Ask(KB,S) returns some/all σ such that KB╞ σ

»•

»

D. Goforth, fall 2006 24

Wumpus world in FOL

• the objects in the environment– terms: constants, variables, functions

• constants:– times: 0, 1, 2, ...– headings: R, L, D, U– coordinates: 1, 2, 3, 4– locations: 16 squares– percepts: Stench, Breeze, Glitter, Bump, Scream, None– actions: Turn(Left), Turn(Right), Forward, Grab, Shoot– Agent, Wumpus

D. Goforth, fall 2006 25

Wumpus world in FOL

• the objects in the environment– terms: constants, variables, functions

• functions:– Square(x,y) // [x,y] p.259– Home(Wumpus)– Perception(s, b, g, h, y) // [s, b, g, h, y] p.258– Heading(t), Location(t)

D. Goforth, fall 2006 26

Wumpus world in FOL

• the basic knowledge– atomic sentences: predicates, term=term

• predicates: (true or false)– properties (of one term/object)

• Breezy(t) // agent feeling breeze at time t• Breeze(s) // breeze blowing on square s• Pit(s), Gold(s), etc.• Time(x) // object x is a time• Coordinate(x), Action(x), Heading(x), etc.

D. Goforth, fall 2006 27

Wumpus world in FOL

• the basic knowledge– atomic sentences: predicates, term=term

• predicates: (true or false)– relations (of multiple terms/objects)

• At(s,t) // agent on square s at time t*• Adjacent (r,s) // squares r and s are adjacent• Alive(x,t) // x is alive at time t• Percept(p,t) // perception at time t• BestAction(a,t) // action a to take at time t

*compare with p.259

D. Goforth, fall 2006 28

Wumpus world in FOL

• the basic knowledge– atomic sentences: predicates, term=term

• term = term: (true or false)– Home(Wumpus) = [3,3] – Heading(5) = U

D. Goforth, fall 2006 29

Exploring Wumpus world in FOL

Timed atomic sentences: Initial conditions

At([1,1],0)

Heading(R,0)

Alive(Agent,0)

Alive(Wumpus,0)

then First perception:

Percept([Stench,None, None, None, None],0)

D. Goforth, fall 2006 30

Exploring Wumpus world in FOL

Percept([Stench, None, None, None, None],0)

Understanding what is perceived

∀b g h y t Percept([Stench,b,g, h, y],t) Stinky(t)∀ ∀ ∀ ∀ ⇒

∀s g h y t Percept([s,None,g, h, y],t)∀ ∀ ∀ ∀ ⇒Glittery(t)

Stinky(0)

Glittery(0)

∀r t Stinky(t) ∀ At(r,t) Stinks(r)⇒

∀r t ∀ Glittery(t) At(r,t) ⇒ Glitters(r)

Stinks([1,1])

Glitters ([1,1])

Fixed knowledge

D. Goforth, fall 2006 31

Exploring Wumpus world in FOL

Updating timed knowledge:

All atomic sentences may be different from t to t+1

BUT

Most are the same

UNLESS

An action changes them

∀r t At(r,t) ∀ (BestAction(Forward,t) (Bumped(t+1)) At(r,t+1)⇒

∀t Screams(t) ⇒ Alive(Wumpus,t)

Time t Time t+1

Deducing hidden properties

Knowledge engineering in FOL

1. Identify the task2. Assemble the relevant knowledge3. Decide on a vocabulary of predicates,

functions, and constants4. Encode general knowledge about the domain5. Encode a description of the specific problem

instance6. Pose queries to the inference procedure and

get answers7. Debug the knowledge base

The electronic circuits domain

One-bit full adder

•

The electronic circuits domain1. Identify the task

XOR(X1)

•

• Does the circuit actually add properly? (circuit verification)• Assemble the relevant knowledge• Composed of wires and gates; Types of gates (AND, OR,

XOR, NOT)• Irrelevant: size, shape, color, cost of gates• Decide on a vocabulary• Alternatives:• Type(X1) = XOR• Type(X1, XOR)

The electronic circuits domain

4. Encode general knowledge of the domain t1,t2 Connected(t1, t2) Signal(t1) = Signal(t2) t Signal(t) = 1 Signal(t) = 0 1 ≠ 0 t1,t2 Connected(t1, t2) Connected(t2, t1) g Type(g) = OR Signal(Out(1,g)) = 1 n

Signal(In(n,g)) = 1 g Type(g) = AND Signal(Out(1,g)) = 0 n

Signal(In(n,g)) = 0 g Type(g) = XOR Signal(Out(1,g)) = 1

Signal(In(1,g)) ≠ Signal(In(2,g)) g Type(g) = NOT Signal(Out(1,g)) ≠

Signal(In(1,g))

The electronic circuits domain

5. Encode the specific problem instanceType(X1) = XOR Type(X2) = XOR

Type(A1) = AND Type(A2) = AND

Type(O1) = OR

Connected(Out(1,X1),In(1,X2)) Connected(In(1,C1),In(1,X1))

Connected(Out(1,X1),In(2,A2)) Connected(In(1,C1),In(1,A1))

Connected(Out(1,A2),In(1,O1)) Connected(In(2,C1),In(2,X1))

Connected(Out(1,A1),In(2,O1)) Connected(In(2,C1),In(2,A1))

Connected(Out(1,X2),Out(1,C1)) Connected(In(3,C1),In(2,X2))

Connected(Out(1,O1),Out(2,C1)) Connected(In(3,C1),In(1,A2))

•

The electronic circuits domain

6. Pose queries to the inference procedure• What are the possible sets of values of all

the terminals for the adder circuit? i1,i2,i3,o1,o2 Signal(In(1,C_1)) = i1 Signal(In(2,C1)) = i2 Signal(In(3,C1)) = i3 Signal(Out(1,C1)) = o1 Signal(Out(2,C1)) = o2

• Debug the knowledge base• May have omitted assertions like 1 ≠ 0

Summary

• First-order logic:– objects and relations are semantic primitives– syntax: constants, functions, predicates,

equality, quantifiers

• Increased expressive power: sufficient to define wumpus world

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