CS 561a: Introduction to Artificial Intelligence
CS 561, Sessions 11-12
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Last time: Logic and Reasoning
Knowledge Base (KB): contains a set of sentences expressed using a knowledge representation language
TELL: operator to add a sentence to the KB
ASK: to query the KB
Logics are KRLs where conclusions can be drawn
Syntax
Semantics
Entailment: KB |= a iff a is true in all worlds where KB is true
Inference: KB |–i a = sentence a can be derived from KB using procedure i
Sound: whenever KB |–i a then KB |= a is true
Complete: whenever KB |= a then KB |–i a
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Last Time: Syntax of propositional logic
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Last Time: Semantics of Propositional logic
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Last Time: Inference rules
for propositional logic
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This time
First-order logic
Syntax
Semantics
Wumpus world example
Ontology (ont = ‘to be’; logica = ‘word’): kinds of things one can talk about in the language
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Why first-order logic?
We saw that propositional logic is limited because it only makes the ontological commitment that the world consists of facts.
Difficult to represent even simple worlds like the Wumpus world;
e.g.,
“don’t go forward if the Wumpus is in front of you” takes 64 rules
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First-order logic (FOL)
Ontological commitments:
Objects: wheel, door, body, engine, seat, car, passenger, driver
Relations: Inside(car, passenger), Beside(driver, passenger)
Functions: ColorOf(car)
Properties: Color(car), IsOpen(door), IsOn(engine)
Functions are relations with single value for each object
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Semantics
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) [true or false]
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more formally, this is how build interpretation of whole from interpretation of parts.
predicate and relation used interchangeably; here predicate is the formal symbol and relation the real world relation
give example of tuples for functions and relations (e.g. son-of, son)
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Examples:
“One plus two equals three”
Objects:
Relations:
Properties:
Functions:
“Squares neighboring the Wumpus are smelly”
Objects:
Relations:
Properties:
Functions:
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Examples:
“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)
“Squares neighboring the Wumpus are smelly”
Objects: Wumpus, square
Relations: neighboring
Properties: smelly
Functions: —
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FOL: Syntax of basic elements
Constant symbols: 1, 5, A, B, USC, JPL, Alex, Manos, …
Predicate symbols: >, Friend, Student, Colleague, …
Function symbols: +, sqrt, SchoolOf, TeacherOf, ClassOf, …
Variables: x, y, z, next, first, last, …
Connectives: , , ,
Quantifiers: ,
Equality: =
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FOL: Atomic sentences
AtomicSentence Predicate(Term, …) | Term = Term
Term Function(Term, …) | Constant | Variable
Examples:
SchoolOf(Manos)
Colleague(TeacherOf(Alex), TeacherOf(Manos))
>((+ x y), x)
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FOL: Complex sentences
Sentence AtomicSentence
| Sentence Connective Sentence
| Quantifier Variable, … Sentence
| Sentence
| (Sentence)
Examples:
S1 S2, S1 S2, (S1 S2) S3, S1 S2, S1 S3
Colleague(Paolo, Maja) Colleague(Maja, Paolo)
Student(Alex, Paolo) Teacher(Paolo, Alex)
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Semantics of atomic sentences
Sentences in FOL are interpreted with respect to a model
Model contains objects and relations among them
Terms: refer to objects (e.g., Door, Alex, StudentOf(Paolo))
Constant symbols: refer to objects
Predicate symbols: refer to relations
Function symbols: refer to functional Relations
An atomic sentence predicate(term1, …, termn) is true iff the relation referred to by predicate holds between the objects referred to by term1, …, termn
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Example model
Objects: John, James, Marry, Alex, Dan, Joe, Anne, Rich
Relation: sets of tuples of objects
{
{
E.g.:
Parent relation — {
then Parent(John, James) is true
Parent(John, Marry) is false
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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):
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Universal quantification (for all):
“Every one in the cs561 class is smart”:
x In(cs561, x) Smart(x)
P corresponds to the conjunction of instantiations of P
(In(cs561, Manos) Smart(Manos))
(In(cs561, Dan) Smart(Dan))
…
(In(cs561, Bush) Smart(Bush))
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Universal quantification (for all):
is a natural connective to use with
Common mistake: to use in conjunction with
e.g: x In(cs561, x) Smart(x)
means “every one is in cs561 and everyone is smart”
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Existential quantification (there exists):
“Someone in the cs561 class is smart”:
x In(cs561, x) Smart(x)
P corresponds to the disjunction of instantiations of P
In(cs561, Manos) Smart(Manos)
In(cs561, Dan) Smart(Dan)
…
In(cs561, Bush) Smart(Bush)
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Existential quantification (there exists):
is a natural connective to use with
Common mistake: to use in conjunction with
e.g: x In(cs561, x) Smart(x)
is true if there is anyone that is not in cs561!
(remember, false true is valid).
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Properties of quantifiers
Proof?
Not all by one person but each one at least by one
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Proof
In general we want to prove:
x P(x) <=> ¬ x ¬ P(x)
x P(x) = ¬(¬( x P(x))) = ¬(¬(P(x1) ^ P(x2) ^ … ^ P(xn)) ) = ¬(¬P(x1) v ¬P(x2) v … v ¬P(xn)) )
x ¬P(x) = ¬P(x1) v ¬P(x2) v … v ¬P(xn)
¬ x ¬P(x) = ¬(¬P(x1) v ¬P(x2) v … v ¬P(xn))
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Example sentences
Brothers are siblings
.
Sibling is transitive
.
One’s mother is one’s sibling’s mother
.
A first cousin is a child of a parent’s sibling
.
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Example sentences
Brothers are siblings
x, y Brother(x, y) Sibling(x, y)
Sibling is transitive
x, y, z Sibling(x, y) Sibling(y, z) Sibling(x, z)
One’s mother is one’s sibling’s mother
m, c, d Mother(m, c) Sibling(c, d) Mother(m, d)
A first cousin is a child of a parent’s sibling
c, d FirstCousin(c, d)
p, ps Parent(p, d) Sibling(p, ps) Child(c, ps)
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Example sentences
One’s mother is one’s sibling’s mother
m, c, d Mother(m, c) Sibling(c, d) Mother(m, d)
c, d m Mother(m, c) Sibling(c, d) Mother(m, d)
c
d
m
Mother of
sibling
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Translating English to FOL
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)
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Translating English to FOL
You can fool all of the people some of the time.
x person(x) => t time(t) ^
can-fool(x,t)
All purple mushrooms are poisonous.
x (mushroom(x) ^ purple(x)) => poisonous(x)
Caution with nested quantifiers
x y P(x,y) is the same as x ( y P(x,y)) which means “for every x, it is true that there exists y such that P(x,y)”
y x P(x,y) is the same as y ( x P(x,y)) which means “there exists a y, such that it is true that for every x P(x,y)”
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Translating English to FOL…
No purple mushroom is poisonous.
¬( x) purple(x) ^ mushroom(x) ^ poisonous(x)
or, equivalently,
( x) (mushroom(x) ^ purple(x)) => ¬poisonous(x)
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Translating English to FOL…
There are exactly two purple mushrooms.
( x)( y) mushroom(x) ^ purple(x) ^ mushroom(y) ^ purple(y) ^ ¬(x=y) ^ ( z) (mushroom(z) ^ purple(z)) => ((x=z) v (y=z))
Deb is not tall.
¬tall(Deb)
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Translating English to FOL…
X is above Y iff X is directly on top of Y or else there is a pile of one or more other objects directly on top of one another starting with X and ending with Y.
( x)( y) above(x,y) <=> (on(x,y) v ( z) (on(x,z) ^ above(z,y)))
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Equality
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Higher-order logic?
First-order logic allows us to quantify over objects (= the first-order entities that exist in the world).
Higher-order logic also allows quantification over relations and functions.
e.g., “two objects are equal iff all properties applied to them are equivalent”:
x,y (x=y) ( p, p(x) p(y))
Higher-order logics are more expressive than first-order; however, so far we have little understanding on how to effectively reason with sentences in higher-order logic.
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Logical agents for the Wumpus world
TELL KB what was perceived
Uses a KRL to insert new sentences, representations of facts, into KB
ASK KB what to do.
Uses logical reasoning to examine actions and select best.
Remember: generic knowledge-based agent:
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Using the FOL Knowledge Base
Set of solutions
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Wumpus world, FOL Knowledge Base
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Deducing hidden properties
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Situation calculus
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Describing actions
May result in too many frame axioms
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Describing actions (cont’d)
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Planning
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Generating action sequences
[ ] = empty plan
Recursively continue until it gets to empty plan [ ]
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Summary
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