Skip this Video
Download Presentation

Loading in 2 Seconds...

play fullscreen
1 / 47

Logic - PowerPoint PPT Presentation

  • Uploaded on

Logic. Propositional Logic. Logic as a Knowledge Representation Language. A Logic is a formal language, with precisely defined syntax and semantics, which supports sound inference. Independent of domain of application.

I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
Download Presentation

PowerPoint Slideshow about ' Logic' - addo

An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.

- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
logic as a knowledge representation language
Logic as a Knowledge Representation Language
  • A Logic is a formal language, with precisely defined syntax and semantics, which supports sound inference. Independent of domain of application.
  • Different logics exist, which allow you to represent different kinds of things, and which allow more or less efficient inference.
    • ..

propositional logic, predicate logic, temporal logic, modal logic, description logic..

  • But representing some things in logic may not be very natural, and inferences may not be efficient. More specialised languages may be better
formal languages
Formal Languages

Intelligent systems require that we have

Knowledge formally represented

New inferences/conclusions possible.

Formal languages have been developed to support knowledge representation.

One important one is the use of logic - very general purpose way to formally represent truths about the world, and draw sound conclusions from these.

propositional logic1
Propositional logic
  • In general a logic is defined by
    • syntax: what expressions are allowed in the language.
    • Semantics: what they mean, in terms of a mapping to real world
    • proof theory: how we can draw new conclusions from existing statements in the logic.
  • Propositional logic is the simplest..
what is a proposition
What is a proposition

Proposition = Statement that may be either true or false.

John is in the classroom.

Mary is enrolled in 270A.

If A is true, and A implies B, then B is true.

If some A are B, and some B are C, then some A are C.

If some women are students, and some students are men, then ….

propositional logic syntax
Propositional Logic: Syntax
  • Symbols (e.g., letters, words) are used to represent facts about the world, e.g.,
    • “P” represents the fact “Andrew likes chocolate”
    • “Q” represents the fact “Andrew has chocolate”
  • These are called atomic propositions
  • True and false are also atomic propositions

Logical connectives are used to represent and: , or:  , if-then: , not: .

  • Statements or sentences in the language are constructed from atomic propositions and logical connectives.
    • P  Q “Andrew likes chocolate and he doesn’t have any.”
    • P Q “If Andrew likes chocolate then Andrew has chocolate”
propositional logic semantics
Propositional Logic: Semantics
  • What does it all mean?
  • Sentences in propositional logic tell you about what is true or false.
    • P  Q means that both P and Q are true.
    • P  Q means that either P or Q is true (or both)

What does it mean to say a statement is true?

What are sound rules for reasoning

What can we represent in propositional logic?

What is the efficiency?

Can we guarantee to infer all true statements?

propositional logic semantics1
Propositional Logic: Semantics
    • P  Q means that if P is true, so is Q.
  • This is all formally defined using truth tables.

X Y X v Y



We now know exactly what is meant in terms of the truth of the elementary

propositions when we get a sentence in the language (e.g., P => Q v R).

truth tables
Truth Tables
  • The truth tables for Propositional Calculus are as follows
proof theory
Proof Theory
  • How do we draw new conclusions from existing supplied facts?
  • We can define inference rules, which are guaranteed to give true conclusions given true premises.
  • For propositional logic useful one is modus ponens:
  • If A is true and A=> B is true, then conclude B is true.

A, A B



proof theory and inference
Proof Theory and Inference
  • So, let P mean “It is raining”, Q mean “I carry my umbrella”.
  • If we know that P is true, and P => Q is true..
  • We can conclude that Q is true.
  • Note that certain expressions are equivalent
    • think about P => Q and  P v Q.
more complex rules of inference
More complex rules of inference
  • Other rules of inference can be used, e.g.,:
  • This is essentially the resolution rule of inference, used in Prolog.

A v B,  B v C


A v C

  • What can we conclude?

sunny v raining

 raining v umbrella

  • Model = possible world
  • x+y = 4 is true in the world x=3, y=1.
  • x+y = 4 is false in the world x=3, y = 1.
  • Entailment S1,S2,..Sn |= S means in every world where S1…Sn are true, S is true.
  • Careful: No mention of proof – just checTaoiseach all the worlds.
  • Some cognitive scientists argue that this is the way people reason.
reasoning or inference systems
Reasoning or Inference Systems
  • Proof is a syntactic property.
  • Rules for deriving new sentences from old ones.
  • Sound: any derived sentence is true.
  • Complete: any true sentence is derivable.
  • NOTE: Logical Inference is monotonic. Can’t change your mind.
translation into propositional logic
Translation into Propositional Logic
  • If it rains, then the game will be cancelled.
  • If the game is cancelled, then we clean house.
  • Can we conclude?
    • If it rains, then we clean house.
  • p = it rains, q = game cancelled r = we clean house.
  • If p then q. not p or q
  • If q then r. not q or r
  • if p then r. not p or r (resolution)
what can t we say
What can’t we say?
  • Quantification: every student has a father.
  • Relations: If X is married to Y, then Y is married to X.
  • Probability: There is an 80% chance of rain.
  • Combine Evidence: This car is better than that one because…
  • Uncertainty: Maybe John is playing golf.
advantages of propositional logic
Advantages of propositional logic

Propositional logic is declarative

Propositional logic allows partial/disjunctive/negated information

  • (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
first order logic
First-order logic
  • Whereas propositional logic assumes the world contains facts,
  • first-order logic (like natural language) assumes the world contains Objects: people, houses, numbers, colors, baseball games, wars, … Relations: red, round, prime, brother of, bigger than, part of, comes between, …
  • Functions: father of, best friend, one more than, plus, …
syntax of fol basic elements
Syntax of FOL: Basic elements
  • Constants TaoiseachJohn, 2, DIT,...
  • Predicates Brother, >,...
  • Functions Sqrt, LeftLegOf,...
  • Variables x, y, a, b,...
  • Connectives , , , , 
  • Equality =
  • Quantifiers , 
atomic sentences
Atomic sentences

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

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

  • E.g., Brother(TaoiseachJohn,RichardTheLionheart) > (Length(LeftLegOf(Richard)), Length(LeftLegOf(TaoiseachJohn)))
complex sentences
Complex sentences
  • Complex sentences are made from atomic sentences using connectives

S, S1 S2, S1  S2, S1 S2, S1S2,

E.g. Sibling(TaoiseachJohn,Richard)  Sibling(Richard,TaoiseachJohn)

>(1,2)  ≤ (1,2)

>(1,2)  >(1,2)

truth in first order logic
Truth in first-order logic
  • Sentences are true with respect to a model and an interpretation
  • Model contains objects (domainelements) and relations among them
  • Interpretation specifies referents for



functionsymbols→ 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

universal quantification
Universal quantification
  • <variables> <sentence>

Everyone at DIT is smart:

x At(x,DIT)  Smart(x)

  • x P is true in a model m iff P is true with x being each possible object in the model

Roughly speaTaoiseach, equivalent to the conjunction of instantiations of P

At(TaoiseachJohn,DIT)  Smart(TaoiseachJohn)

 At(Richard,DIT)  Smart(Richard)

 At(DIT,DIT)  Smart(DIT)

 ...

a common mistake to avoid
A common mistake to avoid
  • Typically,  is the main connective with 
  • Common mistake: using  as the main connective with :

x At(x,DIT)  Smart(x)

means “Everyone is at DIT and everyone is smart”

existential quantification
Existential quantification
  • <variables> <sentence>
  • Someone at DIT is smart:
  • x At(x,DIT)  Smart(x)$
  • xP is true in a model m iff P is true with x being some possible object in the model

Roughly speaTaoiseach, equivalent to the disjunction of instantiations of P

At(TaoiseachJohn,DIT)  Smart(TaoiseachJohn)

 At(Richard,DIT)  Smart(Richard)

 At(DIT,DIT)  Smart(DIT)

 ...

another common mistake to avoid
Another common mistake to avoid
  • Typically,  is the main connective with 
  • Common mistake: using  as the main connective with :

x At(x,DIT)  Smart(x)

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

properties of quantifiers
Properties of quantifiers
  • x y is the same as yx
  • x y is the same as yx
  • x y is not 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”
  • 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)
  • term1 = term2is true under a given interpretation if and only if term1and term2refer to the same object
  • E.g., definition of Sibling in terms of Parent:

x,ySibling(x,y)  [(x = y)  m,f  (m = f)  Parent(m,x)  Parent(f,x)  Parent(m,y)  Parent(f,y)]

using fol
Using FOL

The kinship domain:

  • Brothers are siblings

x,y Brother(x,y) Sibling(x,y)


One\'s mother is one\'s female parent

m,c Mother(c) = m (Female(m) Parent(m,c))

  • “Sibling” is symmetric

x,y Sibling(x,y) Sibling(y,x)

knowledge engineering in fol
Knowledge engineering in FOL
  • Identify the task
  • Assemble the relevant knowledge
  • Decide on a vocabulary of predicates, functions, and constants
  • Encode general knowledge about the domain
  • Encode a description of the specific problem instance
  • Pose queries to the inference procedure and get answers
  • Debug the knowledge base
  • First-order logic:
    • objects and relations are semantic primitives
    • syntax: constants, functions, predicates, equality, quantifiers