Symbolic
Representation and Reasoning
an Overview
Stuart C. Shapiro
Department of Computer Science and Engineering,
Center for Multisource Information Fusion,
and Center for Cognitive Science
University at Buffalo, The State University of New York
201 Bell Hall, Buffalo, NY 14260-2000
shapiro@cse.buffalo.edu
http://www.cse.buffalo.edu/~shapiro/
Introduction
Knowledge Representation
Reasoning
Symbols
Logics
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Knowledge Representation
A subarea of Artificial Intelligence
Concerned with understanding, designing, and
implementing ways of representing information in
computers
So that programs can use this information to
derive information that is implied by it,
to converse with people in natural languages,
to plan future activities,
to solve problems in areas that normally require human
expertise.
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Reasoning
Deriving information that is implied by the
information already present is a form of
reasoning.
Knowledge representation schemes are
useless without the ability to reason with
them.
So, Knowledge Representation and Reasoning
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Knowledge vs. Belief
Knowledge: Justified True Belief
KR systems operate the same whether or not
the information stored is justified or true.
So, Belief Representation and Reasoning
would be better.
But we’ll stick with KR.
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What Is a Symbol?
“A symbol token is a pattern that can be
compared to some other symbol token and judged
equal with it or different from it…
Symbols may be formed into symbol structures
by means of a set of relations…
The `objects’ that symbols designate may include
… objects in an external environment of sensible
(readable) stimuli.”
[Newell & Simon, Concise Encyclopedia of CS, 2004]
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What Is Logic?
The study of correct reasoning.
Not a particular KR language.
There are many systems of logic.
With slight abuse, we call a system of logic a logic.
KR research may be seen as the search for the
correct logic(s) to use in intelligent systems.
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Parts of Specifying a Logic
Syntax
Semantics
Proof Theory
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Syntax
The specification of a set of atomic symbols, and
the grammatical rules for combining them into
well-formed expressions (symbol-structures).
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Syntactic Expressions
Atomic symbols
Individual constants: Tom, Betty, white
Variables: x, y, z
Function symbols: motherOf
Predicate symbols: Person, Elephant, Color
Propositions: P, Q, BdT
Terms
Individual constants: Tom, Betty, white
Variables : x, y, z
Functional terms: motherOf(Fred)
Well-formed formulas (wffs)
Propositions (Proposition symbols) : P, Q, BdT
Atomic formulas: Color(x, white), Duck(motherOf(Fred))
Non-atomic formulas: TdB  Td  Bp
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Semantics
The specification of the meaning (designation) of
the atomic symbols, and the rules for determining
the meanings of the well-formed expressions from
the meanings of their parts.
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Semantic Values
Terms could denote
Objects
Categories of objects
Properties…
Wffs could denote
Propositions
Truth values
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Truth Values
Could be 2, 3, 4, …, ∞ different truth
values.
Some truth values are “distinguished”
Needn’t have anything to do with truth in
the real world.
By default, we’ll assume 2 truth values.
Call distinguished one True (T)
Call other False (F)
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Proof Theory
The specification of a set of rules, which, given an
initial collection of well-formed expressions,
specify what other well-formed expressions can
be added to the collection.
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Proof / Knowledge Base
The collection could be
A proof
A knowledge base
The initial collection could be
Axioms
Hypotheses
Assumptions
Domain facts & rules
The added expressions could be
Theorems
Derived facts & rules
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Example
Logic: Standard Propositional Logic
Domain: CarPool World
Atomic Proposition Symbols:
BdT, TdB, Bd, Td, Bp, Tp
Unary wff-forming connective: 
Binary wff-forming connectives: , , , 
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Intended Interpretation
(Intensional Semantics)
BdT: Betty drives Tom
TdB: Tom drives Betty
Bd: Betty is the driver
Td: Tom is the driver
Bp: Betty is the passenger
Tp: Tom is the passenger
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Extensional (Denotational)
Semantics
BdT
TdB
Bd
T
T
T
T
T
T
T
F
T
F
T
F
F
F
F
Td
Bp
Tp
T
T
T
T
F
F
F
F
F
F
T
T
T
T
F
F
F
F
F
F
T
F
T
F
T
F
T
F
T
F
Bd  Tp
Td  Td
Td  Td
5 of 26 = 64 possible situations
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Properties of Wffs
Satisfiable
T in some situation
BdT
T
T
T
F
F
TdB
T
T
F
T
F
Bd
T
T
T
F
F
Td
T
F
F
T
F
Bp
T
F
F
T
F
Tp
T
F
T
F
F
Bd  Tp
T
F
T
F
F
Td  Td
T
T
T
T
T
Td  Td
F
F
F
F
F
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Properties of Wffs
Contingent
T in some, F in some
BdT
T
T
T
F
F
TdB
T
T
F
T
F
Bd
T
T
T
F
F
Td
T
F
F
T
F
Bp
T
F
F
T
F
Tp
T
F
T
F
F
Bd  Tp
T
F
T
F
F
Td  Td
T
T
T
T
T
Td  Td
F
F
F
F
F
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Properties of Wffs
Valid
T in all situations
BdT
T
T
T
F
F
TdB
T
T
F
T
F
Bd
T
T
T
F
F
Td
T
F
F
T
F
Bp
T
F
F
T
F
Tp
T
F
T
F
F
Bd  Tp
T
F
T
F
F
Td  Td
T
T
T
T
T
Td  Td
F
F
F
F
F
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Properties of Wffs
Contradictory
T in no situation
BdT
T
T
T
F
F
TdB
T
T
F
T
F
Bd
T
T
T
F
F
Td
T
F
F
T
F
Bp
T
F
F
T
F
Tp
T
F
T
F
F
Bd  Tp
T
F
T
F
F
Td  Td
T
T
T
T
T
Td  Td
F
F
F
F
F
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Logical Implication
P1, …, Pn logically imply Q
P1, …, Pn |= Q
In every situation that P1, …, Pn are True,
so is Q.
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Example: CarPool World KB
Let KBCPW =
Bd  Bp
Td  Tp
BdT  Bd  Tp
TdB  Td  Bp
TdB  BdT
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Extensional (Denotational)
Semantics
BdT
TdB
Bd
T
F
T
F
T
F
Td
Bp
Tp
F
F
T
T
T
F
Only 2 of the 64 situations where KBCPW are T
So, e.g.,
KBCPW, BdT |= Bd  Bp
This is how a KB constrains a model to the domain we want.
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Proof Theory
Some Rules of Inference
PQ
P
Q
Modus Ponens or  Elimination
Q
 Elimination
PQ
P
P
Q
PQ
 Elimination
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PQ
P
 Introduction
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Derivation from Assumptions
Q is derivable from P1, …, Pn
P1, …, Pn |- Q
Starting from the collection P1, …, Pn,
one can repeatedly apply rules of inference,
and eventually get Q.
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Example: CarPool World Proof
BdT  Bd  Tp
BdT
Bd  Bp
Bd  Tp
Bd
Bp
Bd  Bp
So, KBCPW, BdT |- Bd  Bp
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Theoremhood
If Q is derivable from no assumptions,
|- Q
We say that Q is provable,
and that Q is a theorem.
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Deduction Theorem
P1, …, Pn |= Q iff |= (P1  · · ·  Pn )  Q
P1, …, Pn |- Q iff |- (P1  · · ·  Pn )  Q
So theorem-proving is relevant to reasoning.
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Properties of Logics
Soundness
If |- P then |= P
(If P is a provable, then P is valid.)
Completeness
If |= P then |- P
(If P is valid, then P is a provable.)
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Soundness vs. Completeness
Soundness is the essence of correct reasoning
Completeness is less important because it
doesn’t indicate how long it might take.
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Commutativity Diagram
for
Sound and Complete Logics
|= (P1  · · ·  Pn )  Q
P1, …, Pn |= Q
soundness
soundness
completeness
completeness
|- (P1  · · ·  Pn )  Q
P1, …, Pn |- Q
So, whenever you want one, you can do another.
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Use of Commutativity Diagram
Refutation proof techniques,
such as resolution refutation
or semantic tableaux,
prove that there can be no situation
in which P1, …, and Pn are True
and Q is False.
These are semantic proof techniques.
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Decision Procedure
A procedure that is guaranteed
to terminate
and tell whether or not P is provable.
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Semidecision Procedure
A procedure that, if P is a theorem
is guaranteed
to terminate
and say so.
Otherwise, it may not terminate.
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A Tour of
Some Classes of Logics
Propositional Logics
Elementary Predicate Logics
Full First-Order Logics
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Propositional Logics
Smallest Unit: Proposition/Sentence
 propositional logics that are
Sound
Complete
Have decision procedures
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What You Can Do
with Propositional Logic
• BettyDrivesTom  TomDrivesBetty
• BettyDrivesTom  NearTomBetty
• TomDrivesBetty  NearTomBetty
•  NearTomBetty
Can derive conclusions
even though the “facts” aren’t entirely known.
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Elementary Predicate Logics
Propositions plus
Predicate (Relation) symbols,
Individual terms, variables, quantifiers
 elementary predicate logics that are
Sound
Complete
Have decision procedures
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What You Can Say with
Elementary Predicate Logic
• x[Elephant(x)  HasA(x, trunk)]
Can state generalities
before all individuals are known.
• x[Elephant(x)  Color(x, white)]
Can describe individuals
Even when they are not specifically known.
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Full First-Order Logics
Elementary predicate logic plus
Function symbols/ functional terms
 full first-order logics that are
Sound
None are
Complete
Have decision procedures
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What You Can Say with
Full First-Order Logic
p[HasProp(0, p)
x[HasProp(x, p)  HasProp(x+1, p)]
 x HasProp(x, p)]
Principle of induction.
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Example of Undecidability
• Large KB about ducks, etc.
• x[y (Duck(y)  WalksLike(x,y))
 y (Duck(y)  TalksLike(x,y))
 Duck(x)]
• x Duck(motherOf(x))  Duck(x)
• Duck(Fred)?
• If Fred is not a duck, possible ∞ loop.
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Unsound Reasoning
Induction
From
Raven(a)  Black(a)
Raven(b)  Black(b)
Raven(c)  Black(c)
Raven(d)  Black(d)
…
Raven(n)  Black(n)
To
x[Raven(x)  Black(x)]
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Unsound Reasoning
Abduction
From
x[Person(x)  Injured(x)  Bandaged(x)]
Person(Tom)
Bandaged(Tom)
To
Injured(Tom)
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What’s “First-Order” about
First-Order Logics
Can’t quantify over
Function symbols
Predicate symbols
Propositions
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Examples of
SNePS
Reasoning
Using a Logic
Designed for KRR
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SNePS, A “Higher-Order” Logic
: all(R)(Transitive(R)
=> (all(x,y,z)(R(x,y) and R(y,z) => R(x,z)))).
: Bigger(elephants, lions).
: Bigger(lions, mice).
: Transitive(Bigger).
: Bigger(elephants, mice)?
Bigger(elephants,mice)
Really a higher-order language for a first-order logic
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“Higher-Order” Example 2
: all(source)(Trusted(source)
=> all(p)(Says(source, p) => p)).
: Trusted(Agent007).
: Says(Agent007, Dangerous(Dr_No)).
: Dangerous(Dr_No)?
Dangerous(Dr_No)
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Designing New Connectives
: andor(1,1){OnFloor(G2), OnFloor(G1),
OnFloor(1), OnFloor(2)}.
: OnFloor(G1).
: OnFloor(?where)?
~OnFloor(G2)
~OnFloor(1)
~OnFloor(2)
OnFloor(G1)
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Belief Change
: andor(1,1){OnFloor(G2), OnFloor(G1), OnFloor(1), OnFloor(2)}.
: {OnFloor(G2), OnFloor(G1)} => {Location(belowGround)}.
: {OnFloor(1), OnFloor(2)} => {Location(aboveGround)}.
: perform believe(OnFloor(G2))
: Location(?where)?
Location(belowGround)
: perform believe(OnFloor(2))
: Location(?where)?
Location(aboveGround)
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Summary 1
Symbolic KRR uses logic.
There are many logics.
The question is which to use.
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Summary 2
A logic has a
Syntax
Semantics
Proof Theory
Logics may
Be sound
Be complete
Have a decision procedure
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Summary 3
Logics provide non-atomic wffs
That can describe situations
Without knowing all specifics
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Summary 4
One can design and build
Useful new logics
And reasoning systems using them.
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