Knowledge Representation - Cognitive Science at Northwestern
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Transcript Knowledge Representation - Cognitive Science at Northwestern
Knowledge Representation
Praveen Paritosh
CogSci 207: Fall 2003: Week 1
Thu, Sep 30, 2004
Some
Representations
Elements of a Representation
•
•
•
•
Represented world: about what?
Representing world: using what?
Representing rules: how to map?
Process that uses the representation: conventions
and systems that use the representations resulting
from above.
• Analog versus Symbolic
Marr’s levels of description
• Computational: What is the goal of the
computation, why is it appropriate, and what is the
logic of the strategy by which it can be carried
out?
• Algorithmic: How can this computational theory
be implemented? In particular, what is the
representation for the input and output, and what
is the algorithm for the transformation?
• Implementation: How can the representation and
algorithm be realized physically?
Marr’s levels of description – 2
• Computational: a lot of cognitive psychology
• Algorithmic: a lot of cognitive science
• Implementation: neuroscience
A closer look
Overview
• How knowledge representation works
– Basics of logic (connectives, model theory, meaning)
• Basics of knowledge representation
– Why use logic instead of natural language?
– Quantifiers
– Organizing large knowledge bases
• Ontology
• Microtheories
• Resource: OpenCyc tutorial materials
How Knowledge Representation
Works
• Intelligence requires knowledge
• Computational models of intelligence require
models of knowledge
• Use formalisms to write down knowledge
– Expressive enough to capture human knowledge
– Precise enough to be understood by machines
• Separate knowledge from computational
mechanisms that process it
– Important part of cognitive model is what the organism
knows
How knowledge representations are used in
cognitive models
• Contents of
KB is part of
cognitive
model
• Some models
hypothesize
multiple
knowledge
bases.
Questions,
requests
Answers,
analyses
Inference
Mechanism(s)
Examples,
Statements
Learning
Mechanism(s)
Knowledge
Base
What’s in the knowledge base?
• Facts about the specifics of the world
– Northwestern is a private university
– The first thing I did at the party was talk to John.
• Rules (aka axioms) that describe ways to infer new
facts from existing facts
– All triangles have three sides
– All elephants are grey
• Facts and rules are stated in a formal language
– Generally some form of logic (aka predicate calculus)
Propositional logic
• A step towards understanding predicate calculus
• Statements are just atomic propositions, with no
structure
– Propositions can be true or false
• Statements can be made into larger statements via
logical connectives.
• Examples:
– C = “It’s cold outside” ; C is a proposition
– O = “It’s October” ; O is a proposition
– If O then C ;if it’s October then it’s cold outside
Symbols for logical connectives
Negation: not, , ~
Conjunction: and,
Disjunction: or,
Implication: implies, ,
Biconditional: iff,
-----------------------------------------------------------• Universal quantifier: forall,
• Existential quantifier: exists,
•
•
•
•
•
Semantics of connectives
• For propositional logic, can define in terms of
truth tables
A
F
F
T
T
B
F
T
F
T
AB
F
F
F
T
A
F
F
T
T
B
F
T
F
T
AB
Implication and biconditional
A
F
F
B
F
T
AB
T
T
A
F
F
B
F
T
T
T
F
T
F
T
T
T
F
T
AB AB
AB
AB (AB)(BA)
Rules of inference
• There are many rules that enable new propositions
to be derived from existing propositions
– Modus Ponens: PQ, P, derive Q
– deMorgan’s law: (AB), derive AB
• Some properties of inference rules
– Soundness: An inference rule is sound if it always
produces valid results given valid premises
– Completeness: A system of inference rules is complete
if it derives everything that logically follows from the
axioms.
Predicate calculus
• Same connectives
• Propositions have structure: Predicate/Function +
arguments.
–
–
–
–
R, 2 ; Terms. Terms are not individuals, not propositions
Red(R), (Red R) ; A proposition, written in two ways
(southOf UnicornCafe UniHall) ;a proposition
(+ 2 2) ; Term, since the function + ranges over numbers
• Quantifiers enable general axioms to be written
– (forall ?x
(iff (Triangle ?x) (and (polygon ?x)
(numberOfSides ?x 3)))
Model Theory
• Meaning of a theory = set of models that satisfy it.
– Model = set of objects and relationships
– If statement is true in KB, then the corresponding
relationship(s) hold between the corresponding objects
in the modeled world
– The objects and relationships in a model can be formal
constructs, or pieces of the physical world, or whatever
• Meaning of a predicate = set of things in the
models for that theory which correspond to it.
– E.g., above means “above”, sort of
Caution: Meaning pertains to simplest
model
• There is usually an intended model, i.e., what one
is representing.
• A sparse set of axioms can be satisfied by
dramatically simpler worlds than those intended
– Example: Classic blocks world axioms have ordered
pairs of integers as a model
• (<position on table> <height>) block
• (on A B) p(A) = p(B) & h(A) = h(B)+1
• (above A B) p(A) = p(B) & h(A) > h(B)
• Moral: Use dense, rich set of axioms
Misconceptions about meaning
• “Predicates have definitions”
– Most don’t. Their meaning is constrained by the sum
total of axioms that mention them.
• “Logic is too discrete to capture the dynamic
fluidity of how our concepts change as we learn”
– If you think of the set of axioms that constrain the
meaning of a predicate as large, then adding (and
removing) elements of that set leads to changes in its
models.
– Sometimes small changes in the set of axioms can lead
to large changes in the set of models. This is the logical
version of a discontinuity.
Representations as Sculptures
• How does one make a statue of an elephant?
– Start with a marble block. Carve away everything that
does not look like an elephant.
• How does one represent a concept?
– Start with a vocabulary of predicates and other axioms.
Add axioms involving the new predicate until it fits
your intended model well.
• Knowledge representation is an evolutionary
process
– It isn’t quick, but incremental additions lead to
incremental progress
– All representations are by their nature imperfect
Introduction to Cyc’s KR system
• These materials are based on tutorial materials
developed by Cycorp, for training knowledge
entry people and ontological engineers
• For this class, we have simplified them somewhat.
• In examinations, you will only be responsible for
the simplified versions
NL vs. Logic: Expressiveness
NL:
Jim’s injury resulted from his falling.
Jim’s falling caused his injury.
Jim’s injury was a consequence of his falling.
Jim’s falling occurred before his injury.
NL: Write the
rule for every
expression?
Logic: identify the common concepts, e.g.
the relation: x caused y
Write rules about the common concepts, e.g.
x caused y x temporally precedes y
NL vs. Logic:
Ambiguity and Precision
NL:
Ambiguous
•x is at the bank.
•x is running.
•river bank?
•changing location?
•financial institution?
•operating?
•a candidate for office?
Logic:
Precise
x is running-InMotion x is changing location
x is running-DeviceOperating x is operating
x is running-AsCandidate x is a candidate
Reasoning: Figuring out what must be true, given what is
known. Requires precision of meaning.
NL vs. Logic:Calculus of Meaning
Logic: Well-understood operators enable reasoning:
Logical constants: not, and, or, all, some
Not (All men are taller than all women).
All men are taller than 12”.
Some women are taller than 12”.
Not (All A are F than all B).
All A are F than x.
Some B are F than x.
Syntax: Terms (aka Constants)
Terms denote specific individuals or collections
(relations, people, computer programs, types of cars . . . )
Each Terms is a character string prefixed by
• A sampling of some constants:
– Dog, SnowSkiing,
PhysicalAttribute
These denote collections
– BillClinton,Rover, DisneyLandTouristAttraction
– likesAsFriend, bordersOn,
objectHasColor, and, not, implies,
forAll
These denote individuals :
•Partially Tangible
Individuals
•Relations
– RedColor, Soil-Sandy
•Attribute Values
Syntax: Propositions
Propositions: a relation applied to some
arguments, enclosed in parentheses
– Also called formulas, sentences…
• Examples:
– (isa GeorgeWBush Person)
– (likesAsFriend GeorgeWBush AlGore)
– (BirthFn JacquelineKennedyOnassis)
Syntax: Non-Atomic Terms
• New terms can be made by applying functions to other
things
– In the Cyc system, functions typically end in “Fn”
• Examples of functions:
– BirthFn, GovernmentFn, BorderBetweenFn
• Examples of Non-Atomic Terms:
– (GovernmentFn France)
– (BorderBetweenFn France Switzerland)
– (BirthFn JacquelineKennedyOnassis)
Non-atomic Terms can be used in statements like any other term
• (residenceOfOrganization (GovernmentFn France)
CityOfParisFrance)
Why Use NATs?
• Uniformity
– All kinds of fruits, nuts, etc., are represented in the
same, compositional way:
(FruitFn PLANT) *
• Inferential Efficiency
– Forward rules can automatically conclude many useful
assertions about NATs as soon as they are created,
based on the function and arguments used to create the
NAT.
• what kind of thing that NAT represents
• how to refer to the NAT in English
•…
Well-formedness: Arity
• Arity constraints are represented in CycL with the predicate
arity:
• (arity performedBy 2)
Represents the fact that performedBy takes two arguments, e.g.:
(performedBy AssassinationOfPresidentLincoln
JohnWilkesBooth)
• (arity BirthFn 1)
Represents the fact that BirthFn takes one arguments, e.g.:
(BirthFn JacquelineKennedyOnassis)
Well-Formedness: Argument Type
Argument type constraints are represented in CycL with the following
2 predicates:
1 argIsa
(argIsa performedBy 1 Action) means that the first argument
of performedBy must be an individual Action, such as the assassination
of Lincoln in:
(performedBy AssassinationOfPresidentLincoln
JohnWilkesBooth)
2 argGenl
(argGenl penaltyForInfraction 2 Event) means that the
second argument of penaltyForInfraction must be a type of Event, such
as the collection of illegal equipment use events in:
(penaltyForInfraction SportsEvent
IllegalEquipmentUse Disqualification)
Why constraints are important
• They guide reasoning
– (performedBy PaintingTheHouse Brick2)
– (performedBy MarthaStewart CookingAPie)
• They constrain learning
Compound propositions
• Connectives from propositional logic can be used
to make more complex statements
(and
(performedBy GettysburgAddress Lincoln)
(objectHasColor Rover TanColor))
(or (objectHasColor Rover TanColor)
(objectHasColor Rover BlackColor))
(implies (mainColorOfObject Rover TanColor)
(not (mainColorOfObject Rover RedColor)))
(not (performedBy GettysburgAddress BillClinton))
Variables and Quantifiers
• General statements can be made by using variables and quantifiers
– Variables in logic are like variables in algebra
• Sentences involving concepts like “everybody,” “something,” and
“nothing” require variables and quantifiers:
Everybody loves somebody.
Nobody likes spinach.
Some people like spinach and some people like broccoli, but no one
likes them both.
Quantifiers
• Adding variables and quantifiers, we can represent more
general knowledge.
• Two main quantifiers:
1. Universal Quantifer -- forAll
Used to represent very general facts, like:
All dogs are mammals
Everyone loves dogs
2. Existential Quantifier -- thereExists
Used to assert that something exists, to state facts like:
Someone is bored
Some people like dogs
Quantifiers
• Universal Quantifier
(forAll ?THING
(isa ?THING Thing))
• Existential Quantifier:
(thereExists ?JOE
(isa ?JOE Poodle))
Everything is a thing.
Something is a poodle.
• Others defined in CycL:
(thereExistsExactly 12 ?ZOS (isa
?ZOS ZodiacSign))
(thereExistsAtLeast 9 ?PLNT (isa
?PLNT Planet))
There are exactly
12 zodiac signs
There are at least
9 planets
Implicit Universal Quantification
All variables occurring “free” in a formula are understood by
Cyc to be implicitly universally quantified.
So, to CYC, the following two formulas represent the same
fact:
(forAll ?X
(implies
(isa ?X Dog)
(isa ?X Animal))
(implies
(isa ?X Dog)
(isa ?X Animal))
Pop Quiz #1
• What does this formula mean?
(thereExists ?PLANET
(and
(isa ?PLANET Planet)
(orbits ?PLANET Sun)))
Pop Quiz #1
• What does this formula mean?
(thereExists ?PLANET
(and
(isa ?PLANET Planet)
(orbits ?PLANET Sun)))
“There is at least one planet orbiting the Sun.”
Pop Quiz #2
• What does this formula mean?
(forAll ?PERSON1
(implies
(isa ?PERSON1 Person)
(thereExists ?PERSON2
(and
(isa ?PERSON2 Person)
(loves ?PERSON1 ?PERSON2)))
Pop Quiz #2
• What does this formula mean?
(forAll ?PERSON1
(implies
(isa ?PERSON1 Person)
(thereExists ?PERSON2
(and
(isa ?PERSON2 Person)
(loves ?PERSON1 ?PERSON2)))
“Everybody loves somebody.”
Pop Quiz #3
• How about this one?
(implies
(isa ?PERSON1 Person)
(thereExists ?PERSON2
(and
(isa ?PERSON2 Person)
(loves ?PERSON2 ?PERSON1))))
Pop Quiz #3
• How about this one?
(implies
(isa ?PERSON1 Person)
(thereExists ?PERSON2
(and
(isa ?PERSON2 Person)
(loves ?PERSON2 ?PERSON1))))
“Everyone is loved by someone.”
Pop Quiz #4
And this?
(implies
(isa ?PRSN Person)
(loves ?PRSN ?PRSN))
Pop Quiz #4
And this?
(implies
(isa ?PRSN Person)
(loves ?PRSN ?PRSN))
“Everyone loves his (or her) self.”