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First-Order Logic
Reading: C. 8 and C. 9
Pente specifications handed back at end
of class
First-Order Logic: Outline
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Expressing Information in first-order logic
An example
Inference in FOL
• Resolution theorem proving
• Production systems (forward chaining)
• Logic-based programming (backward chaining)
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Characteristics of FOL
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Declarative
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Expressive
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Compositionality
• Partial information
• Negation
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Ontological Commitment
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Propositional logic:
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There are facts that either hold or do not hold in the
world
Logic constrains facts
First-order logic:
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The world consists of objects and relations between
objects
Logic constrains allowable objects, properties of
objects, relations between objects
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Ontological commitments of higher
order logics
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Temporal logic
• Facts hold at particular times and those times are
ordered
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Epistemological
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• The agent believes a fact
• The agent does not believe it
• The agent has no opinion
Probabilistic
• Agents hold beliefs about facts
• Three possible states of knowledge
• Facts are true to different degrees (Truth value from 0 to
1)
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Problems with propositional logic
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Propositional Logic is lacking in
expressiveness
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Cannot represent knowledge of complex
environments in a concise way
• E.g., Squares adjacent to pits are breezy
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Need objects
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Need relations
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Need functions
• Squares, pits, Kathy
• Adjacent, breezy, smelly, know
• Father-of, mother-of
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Syntax of FOL: basic elements
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Constants: Vijay, Andrew, Sowmya
Predicates: knows, adjacent, >
Functions: Sqrt, father-of
Variables: x,y,a,b
Connectives: Λ,V,⌐,→,↔
Equality: =
Quantifiers: ,
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Atomic Sentences
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Atomic sentence = predicate (term1…termm)
or term1=term2
Term = function (term1, …, termm)
or constant or variable
E.g. know(Kathy,Sowmya), Adjacent (x,y),
father-of(Kathy) = Michael, Andrew, x
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Complex Sentences
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Complex sentences are made from
atomic sentences using connectives
⌐S, S1ΛS2, S1VS2, S1S2, S1S2
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E.g., adjacent(x,y)  adjacent (y,x),
⌐knows(Nunzio, Michael),
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Truth in First-order Logic
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Sentences are true with respect to a model and an
interpretation
Model contains  1 objects (domain elements) and
relations among them
Interpretation specifies referents for
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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,…, termn are in the
relation referred to by predicate.
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Universal quantification
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<variables> <sentence>
Everyone at Columbia is smart:
x At(x,Columbia)  Smart(x)
x P is true in a model m iff P with x being
each possible object in the model
At (Leia, Columbia)  Smart(Leia)
At (Ryan, Columbia)  Smart (Ryan)
At (Archana, Columbia)  Smart (Archana)
At (Stanley, Columbia)  Smart (Stanley)
…..
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A common mistake
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Typically,  is the main connective used with 
Common mistake: using as the main connective
Λ
x At(x,Columbia) Λ Smart(x)
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Existential Quantification
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<variables> <sentence>
Someone at Columbia is smart
x At(x,Columbia) Smart(x)
 x P is true in a model m iff P with x being each possible
object in the model
Equivalent to the disjunction of instantiations of P
At (Leia, Columbia) Λ Smart(Leia)
V At (Ryan, Columbia) Λ  Smart (Ryan)
V At (Archana, Columbia) Λ  Smart (Archana)
V At (Stanley, Columbia) Λ  Smart (Stanley)
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Another Common Mistake
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Typically, Λ is the main connective with

Common mistake: using  as the main
connective
 x At(x,Columbia)  Smart(x)
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Properties of Quantifiers
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x y is the same y x
x  y is the same as  y  x
 x  y is not the same as  y  x
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 x y Loves(x,y)
There is a person who loves everyone in the world
 y  x Loves(x,y)
Everyone is loved by someone.
Quantifier duality: each can be expressed using the other
 x Likes (x,Icecream) ⌐ x ⌐ Likes(x,IceCream)
 x Likes(x, Broccoli)
⌐ x ⌐ Likes(x,Broccoli)
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Translation from English to FOL
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A mother is a female parent
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Andrew likes the problem of one of the
book exercises
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?
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Example
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Family trees
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What does the model look like?
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What can we infer?
• Father-of
• Mother-of
• Sibling
• Cousin
• Ancestors
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To Make Inferences in FOL
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Method 1
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Method 2
• Unification of variables with literals (in the KB)
• Generalized Modus Ponens
• Forward-chaining or Backward-chaining
• Resolution
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Unification
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We want to find a substitution  such that
x and y match literals
Unify (,) =  if  = 
Some examples
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


Knows(John,x)
Knows(John,
Jane)
{x/Jane}
Knows(John,x)
Knows(y,Michel)
{x/Michel,y/John}
Knows(John,x)
Knows(y,Motherof(y))
{y/John,x/Motherof(John)
Knows(John,x)
Knows(x,Michel)
fail
Standardizing apart eliminates overlap of variables, e.g.,
Knows(z17,Michel)
Unification for example
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P`1= father-of(Kathy)=Michael
P1= father-of(x)=y
={x/Kathy,y/Michael}
q=ancestor(x,y)
q`=ancestor(Kathy,Michael)
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Example inference using forward
chaining (production systems)
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Properties of forward-chaining
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Sound and complete for first-order definite
clauses
Datalog is first-order definite clauses and no
functions
May not terminate in general if is not entailed
This is unavoidable: entailment with definite
clauses is semi-decidable
Forward chaining is widely used in deductive
databases
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Example inference using backward
chaining
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Properties of backward-chaining
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Depth-first recursive proof search: space is
linear in size of proof
Incomplete due to infinite loops
• Fix by checking current goal against every goal on stack
Inefficient due to repeated subgoals (both
success and failure)
• Fix using cache of previous results (extra space!)
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Widely used (without improvements!) for logic
programming (e.g., Prolog)
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Midterm results
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Exams will only be given back to person
the owner of the exam
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