1. Agent that reason logically
지식표현

2. Knowledge Base A set of representations of facts about the world
Knowledge representation language
tell : what has been told to the knowledge base previously
ask : a question and the answer
Inference : what follows from what the KB has been Telled
Background knowledge : a knowledge base which may initially contained
Sentence : individual representation of a fact

3. Knowledge base
The knowledge level :: saying what it knows to KB  “Golden Gates Bridge links San Francisco and Marin Country
The logical level :: the knowledge is encoding into sentences  Links(GGBridge, SF, Marin)
The implementation level :: the level that runs on the agent architecture (data structures to represent knowledge or facts)

4. Knowledge declarative/procedural
love(john, mary).
can_fly(X) :- bird(X), not(can_fly(X)), !.
learning : general knowledge about the environment given a series of percepts
Commonsense knowledge

5. Specifying the environment
Figure 6.2 A typical wumpus world

6. Domain specific knowledge
In the squares directly adjacent to a pit, the agent will perceive a breeze
Commonsense knowledge
logical reasoning
stench(1,2) & ~setnch(2,1)  ~wumpus(2,2)
wumpus(1,3) 
stench(2,1) & stench(2,3) & stench(1,4)

7. Inference in Wumpus world(I)
4,1
3,1
2,1
1,1
4,2
3,2
2,2
1,2
4,3
3,3
2,3
1,3
4,4
3,4
2,4
1,4 OK
A = Agent
B = Breeze
G = Glitter, Gold
OK = Safe square
P = Pit
S = Stench
V = Visited
W = Wumpus A B V
P ?
Figure 6.3 The first step taken by the agent in the wumpus world.
The initial situation, after percept [None, None, None, None, None].
After one move, with percept [None, Breeze, None, None, None].

8. Inference in Wumpus world(II)
4,1
3,1
2,1
1,1
4,2
3,2
2,2
1,2
4,3
3,3
2,3
1,3
4,4
3,4
2,4
1,4 OK
A = Agent
B = Breeze
G = Glitter, Gold
OK = Safe square
P = Pit
S = Stench
V = Visited
W = Wumpus A V
Figure 6.4 Two later stages in the progress of the agent.
After the third move, with percept [Stench, None, None, None, None].
After the fifth move, with percept [Stench, Breeze, Glitter, None, None]. B W! S
P !
P ?
W !
S G

9. Representation, Reasoning, and Logic
Syntax : the possible configurations that constitute sentences
Semantics : the facts in the world to which the sentences refer

10. The logical reasoning
Figure 6.5 The connection between sentences and facts is provided by the semantics
of the language. The property of one fact following from some other facts is mirrored by
the property of one sentence being entailed by some other sentences. Logical inference
generates new sentences that are entailed by existing sentences.

11. Inference I
Entailment :: generation of new sentences that are necessarily true, given that the old sentences are true
Soundness, truth-preserving :: An inference procedure that generates only entailed sentences  modus ponens <-> abduction
KB├i ,  is derived from KB by I
Proof :: a sound inference procedure

12. Inference II
Completeness :: an inference procedure that can find a proof for any sentence that is entailed
Proof :: specifying the reasoning steps that are sound
Valid :: if and only if all possible interpretations in all possible worlds
Tautologies, analytic sentences :: valid sentences
Satisfiable :: if and only if there is some interpretation in some world for which it is true
Unsatisfiable :: a sentence that is not satisfiable

13. Logics Boolean logic First-order logic objects, predicates
Symbols represent whole propositions (facts)
Boolean connectives
First-order logic
objects, predicates
connectives, quantifiers

14. Wrong logical reasoning
FIRST VILLAGER: We have found a witch. May we burn her?
ALL: A witch! Burn her!
BEDEVERE: Why do you think she is a witch?
SECOND VILLAGER: She turned me into a newt.
BEDEVERE: A newt?
SECOND VILLAGER (after looking at himself for some time): I got better.
ALL: Burn her anyway.
BEDEVERE: Quiet! Quiet! There are ways of telling whether she is a witch.
BEDEVERE: Tell me … What do you do with witches?
ALL: Burn them.
BEDEVERE: And what do you burn, apart from witches?
FOURTH VILLAGER: … Wood?
BEDEVERE: So why do witches burn?
SECOND VILLAGER: (pianissimo) Because they’re made of wood?
BEDEVERE: Good.
ALL: I see. Yes, of course.
BEDEVERE: So how can we tell if she is made of wood?
FIRST VILLAGER: Make a bridge out of her.
BEDEVERE: Ah … but can you not also make bridges out of stone?
ALL: Yes, of course … um … er …
BEDEVERE: Does wood sink in water?
ALL: No, no, it floats. Throw her in the pond.
BEDEVERE: Wait. Wait … tell me, what also floats on water?
ALL: Bread? No, no no. Apples … gravy … very small rocks …
BEDEVERE: No, no no.
KING ARTHUR: A duck!
(They all turn and look at ARTHUR. BEDEVERE looks up very impressed.)
BEDEVERE: Exactly. So … logically …
FIRST VILLAGER (beginning to pick up the thread): If she .. Weight the same as a duck … she’s made of wood.
BEDEVERE: And therefore?
ALL: A witch!

15. Ontological and epistemological commitments
Ontological commitments :: to do with the nature of reality
Propositional logic(true/false), Predicate logic, Temporal logic
Epistemological commitments :: to do with the possible states of knowledge an agent can have using various types of logic
degree of belief
fuzzy logic

16. Commitments
Formal languages and their and ontological and epistemological commitments
Language
Ontological Commitment
(What exists in the world)
Epistemological Commitment
(What an agent believes
about facts)
Propositional logic
First-order logic
Temporal logic
Probability theory
Fuzzy logic
facts
facts, objects, relations
times
degree of truth
true/false/unknown
degree of belief 0…1

17. Propositional Logic logical constant : true/false
propositional symbols : P, Q
parentheses : (P & Q)
logical connectives : &(conjuction), v(disjunction), ->(implication), <->(equivalence), ~(negation)

18. Grammar Sentence  AtomicSentence | ComplexSentence
AtomicSentence  True | False
| P | Q | R | …
ComplexSentence  ( Sentence )
| Sentence Connective Sentence
| Sentence
Connective   |  |  | 
Figure 6.8 A BNF (Backus-Naur Form) grammar of sentences in propositional logic.

19. Semantics Truth table showing validity of a complex sentence P H PH
(P  H) ┐H
((P  H)  ┐H)P
False
True

20. Validity and Inference
Truth tables for five logical connectives P Q ┐P
PQ
PQ
PQ
PQ
False False True True
False True False True
True True False False
False False False True
False True True True
True True False True
True False False True

21. Models
Any world in which a sentence is true under a particular interpretation
Entailment :: a sentence  is entailed by a knowledge base KB if the models of the KB are all models of 
The set of models of P & Q is the intersection of the models of P and the models of Q

22. Inference Rules for propositional logic
Modus Ponens or Implication-Elimination: (From an implication and the premise of the implication, you can infer the conclusion.)
And-Elimination: (From a conjunction, you can infer any of the conjuncts.)
And-Introduction: (From a list of sentences, you can infer their conjunction.)
Or-Introduction: (From a sentence, you can infer its disjunction with anything else at all.)
Double-Negation Elimination: (From a doubly negated sentence, you can infer a positive sentence.)
Unit Resolution: (From a disjunction, if one of the disjuncts is false, then you can infer the other one is true.)
 => ,  
1  2  …  n i
1, 2, …, n
1  2  …  n i
1  2  …  n
 
  ,   
Resolution: (This is the most difficult. Because  cannot be both true and false, one of the other disjucts must be true in one of the premises. Or equivalently, implication is transitive.)
  ,    
  => ,  => 
or equivalently
  
  => 
Figure Seven inference for propositional logic. The unit resolution rule is a special case of the resolution rule, which in turn is a special case of the full resolution rule for first-order logic discussed in Chapter 9.

23. Complexity of propositional inference
NP-complete
Monotonicity
If KB1╞  then (KB1 ∪ KB2) ╞ 
Horn clause logic
polynomial time complexity
P1∧P2∧….∧Pn ⇒ Q

24. Wumpus world ~S1,1 ~B1,1 R1: ~S1,1 -> ~W1,1 & ~W1,2 & ~W2,1
Initial state
~S1,1 ~B1,1
~S2, B2,1
S1,2 ~B1,2
Rule
R1: ~S1,1 -> ~W1,1 & ~W1,2 & ~W2,1
R2: ~S2,1 -> ~W1,1 & ~W2,1 & ~W2,2 & ~W3,1
R3: ~S1,2 -> ~W1,1 & ~W1,2 & ~W2,2 & ~W1,3
R4: S1,2 -> W1,3 V W1,2 V W2,2 V W1,2

25. Finding the wumpus Inference process Modus ponens :
~S1,1 and R1  ~W1,1 & ~W1,2 & ~W2,1
And-Elimination
~W1,1 ~W1,2 ~W2,1
Modus ponens and And-Elimination:
~W2,2 ~W2,1 ~W3,1
Modus ponens
S1,2 and R4  W1,3 V W1,2 V W2,2 V W1,1

26. Inference process(cont.)
unit resolution
~W1,1 and W1,3 V W1,2 V W2,2 V W1,1
 W1,3 V W1,2 V W2,2
~W2,2 and W1,3 V W1,2 V W2,2
 W1,3 V W1,2
~W1,2 and W1,3 V W1,2  W1,3

27. Translating knowledge into action
A1,1 & EastA & W2,1 -> ~Forward
EastA :: facing east
Propositional logic is not powerful enough to solve the wumpus problem easily

28. 숙제
6.3, 6.6, 6.7, 6.9, 6.10, , 6.15, 6.16

29. First-order Logic

30. Limitation of propositional logic
A very limited ontology
 to need to the representation power
 first-order logic

31. First-order logic A stronger set of ontological commitments
A world in FOL consists of objects, properties, relations, functions
Objects  people, houses, number, colors, Bill Clinton
Relations  brother of, bigger than, owns, love
Properties  red, round, bogus, prime
Functions father of, best friend, third inning of

32. Examples “One plus two equals three”
objects :: one, two, three, one plus two
Relation :: equal
Function :: plus
“Squares neighboring the wumpus are smelly
Objects :: wumpus, square
Property :: smelly
Relation :: neighboring

33. First order logics Objects와 relations 시간, 사건, 카테고리 등은 고려하지 않음
영역에 따라 자유로운 표현이 가능함  ‘king’은 사람의 property도 될 수 있고, 사람과 국가를 연결하는 relation이 될 수도 있다
일차술어논리는 잘 알려져 있고, 잘 연구된 수학적 모형임

34. Syntax and Semantics Sentence  AtomicSentence
| Sentence Connective Sentence
| Auantifier Variable,…Sentence
| Sentence
| (Sentence)
AtomicSentence  Predicate(Term,…) | Term=Term
Term Function (Term,…)
| Constant
| Variable
Connective   |  |  | 
Quantifier   | 
Constant  A | X1 | John | …
Variable  a | x | s | …
Predicate  Before | HanColor | Raining | …
Function  Mother | LeftLegOf | …
Figure 7.1 The syntax of first-order logic (with equality) in BNF (Backus-Naur Form).

35. 예 Constant symbols :: A, B, John, Predicate symbols :: Round, Brother
Function symbols :: Cosine, FatherOf
Terms :: King John, Richard’s left leg
Atomic sentences :: Brother(Richard,John), Married(FatherOf(Richard), MotherOf(John))
Complex sentences :: Older(John,30)=>~younger(John,30)

36. Quantifiers World = {a, b, c} Universal quantifier (∀)
∀x Cat(x) => Mammal(x) 
Cat(a) => Mammal(a) &
Cat(a) => Mammal(a)
Existential quantifier (∃)
∃x Sister(x, Sopt) & Cat(x)

37. Nested quantifiers ∀x,y Parent(x,y) => Child(y,x)
∀x,y Brother(x,y) => Sibling(y,x)
∀x∃y Loves(x,y)
∃y∀x Loves(x,y)

38. De Morgan’s Rule ∀x ~P  ~∃x P ~P&~Q  ~(P v Q)
∃x P  ~∀x ~P P v Q  ~(~P&~Q)

39. Equality Identity relation Father(John) = Henry
∃x,y Sister(Spot,x) & Sister(Spot,y)
& ~(x=y)
≠ ∃x,y Sister(Spot,x) & Sister(Spot,y)

40. Higher-order logic ∀x,y (x=y)  (∀p p(x)  p(y))
∀f,g (f=g)  (∀x f(x) g(x)) ∀

41. -expression
x,y x2 – y2
-expression can be applied to arguments to yield a logical term in the same way that a function can be
(x,y x2 – y2)(25,24) = = 49
x,y Gender(x) ≠Gender(y) & Address(x) = Address(y)

42. ∃! (The uniqueness quantifier)
∃!x King(x)
∃x King(x) & ∀y King(y) => x=y
world를 고려하여 보여주면 => object가 1, 2, 3개일 때
{a} w0  king={}, w1  king={a}  w1만 model
{a,b} w0  king={}, w1  king={a},
w2 {b}, w3  {a,b}  w1, w2만 model

43. Representation of sentences by FOPL
One’s mother is one’s female parent
∀m,c Mother(c)=m  Female(m) & Parent(m)
One’s husband is one’s male spouse
∀w,h Husband(h,w)  Male(h) & Spouse(h,w)
Male and female are disjoint categories
∀x Male(x)  ~Female(x)
A grandparent is a parent of one’s parent
∀g,c Grandparent(g,c)  ∃p parent(g,p) & parent(p,g)

44. Representation of sentences by FOPL
A sibling is another child of one’s parents ∀x,y Sibling(x,y)  x≠y & ∃p Parent(p,x) & Parent(p,y)
Symmetric relations
∀x,y Sibling(x,y)  Sibling(y,x)

45. The domain of sets (I)
The only sets are the empty set and those made by adjoining something to a set :
∀s Set(s)  (s=EmptySet) v (∃x,s2 Set(s2) & s=Adjoin(x,s2))
The empty set has no elements adjoined into it.
~∃x,s Adjoin(x,s)=EmptySet
Adjoining an element already in the set has no effect
∀x,s Member(x,s)  s=Adjoin(x,s)
The only members of a set are the elements that were adjoined into it
∀x,s Member(x,s) 
∃y,s2 (s=Adjoin(y,s2) & (x=y v Member(x,s)))

46. The domain of sets (II) ∀s1,s2 Subset(s1,s2) 
A set is a subset of another if and only if all of the first set’s are members of the second set :
∀s1,s2 Subset(s1,s2) 
(∀x Member(x,s1) => member(x,s2))
Two sets are equal if and only if each is a subset of the other:
∀s1,s2 (s1=s2)  (Subset(s1,s2) & Subset(s2,s1))

47. The domain of sets (III)
An object is a member of the intersection of two sets if and only if it is a member of each of sets :
∀x,s1,s2 Member(x,Intersection(s1,s2)) 
Member(x,s1) & Member(x,s2)
An object is a member of the union of two sets if and only if it is a member of either set :
∀x,s1,s2 Member(x,Union(s1,s2)) 
Member(x,s1) v Member(x,s2)

48. Asking questions and getting answers
Tell(KB, (∀m,c Mother(c)=m  Female(m) & Parent(m,c))) ……
Tell(KB, (Female(Maxi) & Parent(Maxi,Spot) & Parent(Spot,Boots)))
Ask(KB,Grandparent(Maxi,Boots)
Ask(KB, ∃x Child(x, Spot))
Ask(KB, ∃x Mother(x)=Maxi)
Substitution, unification, {x/Boots}