1. For Wednesday Read chapter 9, sections 1-3 Homework: –Chapter 7, exercises 8 and 9

2. Program 1 Any questions?

3. Propositional Logic Simple logic Deals only in facts Provides a stepping stone into first order logic

4. Syntax Logical Constants: true and false Propositional symbols P, Q... are sentences If S is a sentence then (S) is a sentence. If S is a sentence then ¬S is a sentence. If S 1 and S 2 are sentences, then so are: –S 1  S 2 –S 1  S 2 –S 1  S 2 –S 1  S 2

5. Semantics true and false mean truth or falsehood in the world P is true if its proposition is true of the world ¬S is the negation of S The remainder follow standard truth tables –S 1  S 2 : AND –S 1  S 2 : inclusive OR –S 1  S 2 : True unless S 1 is true and S 2 is false –S 1  S 2 : bi-conditional, or if and only if

6. Inference An interpretation is an assignment of true or false to each atomic proposition A sentence true under any interpretation is valid (a tautology or analytic sentence) Validity can be checked by exhaustive checking of truth tables

7. Rules of Inference Alternative to truth-table checking A sequence of inference rule applications leading to a desired conclusion is a logical proof We can check inference rules using truth tables, and then use to create sound proofs We can treat finding a proof as a search problem

8. Propositional Inference Rules Modus Ponens or Implication Elimination And-Elimination Unit Resolution Resolution

9. Simpler Inference Horn clauses Forward-chaining Backward-chaining

10. Building an Agent with Propositional Logic Propositional logic has some nice properties –Easily understood –Easily computed Can we build a viable wumpus world agent with propositional logic???

11. The Problem Propositional Logic only deals with facts. We cannot easily represent general rules that apply to any square. We cannot express information about squares and relate (we can’t easily keep track of which squares we have visited)

12. More Precisely In propositional logic, each possible atomic fact requires a separate unique propositional symbol. If there are n people and m locations, representing the fact that some person moved from one location to another requires nm 2 separate symbols.

13. First Order Logic Predicate logic includes a richer ontology: –objects (terms) –properties (unary predicates on terms) –relations (n­ary predicates on terms) –functions (mappings from terms to other terms) Allows more flexible and compact representation of knowledge Move(x, y, z) for person x moved from location y to z.

14. Syntax for First­Order Logic Sentence  AtomicSentence | Sentence Connective Sentence | Quantifier Variable Sentence | ¬Sentence | (Sentence) AtomicSentence  Predicate(Term, Term,...) | Term=Term Term  Function(Term,Term,...) | Constant | Variable Connective   Quanitfier   Constant  A | John | Car1 Variable  x | y | z |... Predicate  Brother | Owns |... Function  father­of | plus |...

15. Terms Objects are represented by terms: –Constants: Block1, John –Function symbols: father­of, successor, plus An n­ary function maps a tuple of n terms to another term: father­of(John), succesor(0), plus(plus(1,1),2) Terms are simply names for objects. Logical functions are not procedural as in programming languages. They do not need to be defined, and do not really return a value. Functions allow for the representation of an infinite number of terms.

16. Predicates Propositions are represented by a predicate applied to a tuple of terms. A predicate represents a property of or relation between terms that can be true or false: –Brother(John, Fred), Left­of(Square1, Square2) –GreaterThan(plus(1,1), plus(0,1)) In a given interpretation, an n­ary predicate can defined as a function from tuples of n terms to {True, False} or equivalently, a set tuples that satisfy the predicate: –{,,,...}

17. Sentences in First­Order Logic An atomic sentence is simply a predicate applied to a set of terms. –Owns(John,Car1) –Sold(John,Car1,Fred) Semantics is True or False depending on the interpretation, i.e. is the predicate true of these arguments. The standard propositional connectives (   ) can be used to construct complex sentences: –Owns(John,Car1)  Owns(Fred, Car1) –Sold(John,Car1,Fred)  ¬Owns(John, Car1) Semantics same as in propositional logic.

18. Quantifiers Allow statements about entire collections of objects Universal quantifier:  x –Asserts that a sentence is true for all values of variable x  x Loves(x, FOPC)  x Whale(x)  Mammal(x)  x  y Dog(y)  Loves(x,y))   z Cat(z)  Hates(x,z)) Existential quantifier:  –Asserts that a sentence is true for at least one value of a variable x  x Loves(x, FOPC)  x(Cat(x)  Color(x,Black)  Owns(Mary,x))  x(  y Dog(y)  Loves(x,y))  (  z Cat(z)  Hates(x,z))

19. Use of Quantifiers Universal quantification naturally uses implication: –  x Whale(x)  Mammal(x) Says that everything in the universe is both a whale and a mammal. Existential quantification naturally uses conjunction: –  x Owns(Mary,x)  Cat(x) Says either there is something in the universe that Mary does not own or there exists a cat in the universe. –  x Owns(Mary,x)  Cat(x) Says all Mary owns is cats (i.e. everthing Mary owns is a cat). Also true if Mary owns nothing. –  x Cat(x)  Owns(Mary,x) Says that Mary owns all the cats in the universe. Also true if there are no cats in the universe.

20. Nesting Quantifiers The order of quantifiers of the same type doesn't matter: –  x  y(Parent(x,y)  Male(y)  Son(y,x)) –  x  y(Loves(x,y)  Loves(y,x)) The order of mixed quantifiers does matter: –  x  y(Loves(x,y)) Says everybody loves somebody, i.e. everyone has someone whom they love. –  y  x(Loves(x,y)) Says there is someone who is loved by everyone in the universe. –  y  x(Loves(x,y)) Says everyone has someone who loves them. –  x  y(Loves(x,y)) Says there is someone who loves everyone in the universe.

21. Variable Scope The scope of a variable is the sentence to which the quantifier syntactically applies. As in a block structured programming language, a variable in a logical expression refers to the closest quantifier within whose scope it appears. –  x (Cat(x)   x(Black (x))) The x in Black(x) is universally quantified Says cats exist and everything is black In a well­formed formula (wff) all variables should be properly introduced: –  xP(y) not well­formed A ground expression contains no variables.

22. Relations Between Quantifiers Universal and existential quantification are logically related to each other: –  x ¬Love(x,Saddam)  ¬  x Loves(x,Saddam) –  x Love(x,Princess­Di)  ¬  x ¬Loves(x,Princess­Di) General Identities –  x ¬P  ¬  x P – ¬  x P   x ¬P –  x P  ¬  x ¬P –  x P  ¬  x ¬P –  x P(x)  Q(x)   x P(x)   x Q(x) –  x P(x)  Q(x)   x P(x)   x Q(x)