Kecerdasan Buatan M. Ali Fauzi

Transcription

1 Kecerdasan Buatan M. Ali Fauzi

2 Artificial Intelligence M. Ali Fauzi

3 Logical Agents M. Ali Fauzi

4 In which we design agents that can form representations of the would, use a process of inference to derive new representations about the world, and use these new representations to deduce what to do.

5 TODAY

6 ~ Knowledge-based agents

7 ~ Knowledge-based agents ~ Wumpus world

8 ~ Knowledge-based agents ~ Wumpus world ~ Logic in general - models and entailment

9 ~ Knowledge-based agents ~ Wumpus world ~ Logic in general - models and entailment ~ Propositional (Boolean) logic

10 ~ Knowledge-based agents ~ Wumpus world ~ Logic in general - models and entailment ~ Propositional (Boolean) logic ~ Inference rules and theorem proving

11 Knowledge-based agents

12 Problem Solving Agent Knowledge-based Agent VS

13 Problem Solving Agent ~ choose a solution among the available possibilities ~ The knowledge is very specific and inflexible. What he "knew" about the world does not evolve ~ problem solution (initial state, successor function, goal test)

14 Knowledge-based Agent ~ smarter ~ The knowledge is very flexible. What he "knew" about the world evolve ~ can do reasoning for ; - imperfect/partial information - Choosing better action

15 Knowledge-based Agent ~ Can represent world, state, action, etc. ~ Can percept new information (and update the representation) ~ Can deduce implicit knowledge (hidden property) ~ Can deduce the best action

16 Knowledge-based Agent

17 Knowledge Base ~ The central component of a knowledge-based agent is its knowledge base, or KB

18 Knowledge-base ~ What agent knew ~ a set of sentences. ~ Each sentence is expressed in a formal language (so it can be processed) called a knowledge representation language ~ Each sentence represents some fact about the world.

19 Knowledge-base ~ There must be a way to add new sentences to the knowledge base and a way to query what is known. ~ KB (TELL) : add new sentences to the KB ~ KB (ASK) : asking what the best action based on KB

20 Inference Engine ~ Deduce new facts that can be derived from the knowledge that already exists in KB ~ update the new representation from the new fact (TELL) ~ asking what the best action based on KB (ASK)

21 Knowledge-based Agent

22 Knowledge-based Agent ~ Knowledge Level : what he knows? a robot "knows" that the A building is between E building and C building

23 Knowledge-based Agent ~ Implementation level : how is the representation - Logical sentence: between(gda,gde,gdc) - Natural language: Gedung A ada di antara gedung E dan gedung C - Building coordinate location - Filkom maps (bitmap? vector?) ~ Representation is very influential on what can be done by inference engine

24 Declarative approach to building an agent ~ Tell it what it needs to know ~ Then it can ask itself what to do - answers follow from the knowledge base

25 Procedural approach to building an agent ~ Tell it what to do ~ If the program is not correct? (error?)

26 Knowledge representation ~ Expressive: can represent facts about the world ~ Tractable: can be processed by the inference engine

27 Knowledge is power Representation + Reasoning = Intelligence!!

28 Wumpus World

29 Wumpus World PEAS description Performance measure gold +1000, death per step, -10 for using the arrow Environment Squares adjacent to wumpus are smelly Squares adjacent to pit are breezy Glitter iff gold is in the same square Shooting kills wumpus if you are facing it Shooting uses up the only arrow Grabbing picks up gold if in same square Releasing drops the gold in same square Actuators Left turn, Right turn, Forward, Grab, Release, Shoot Sensors Breeze, Glitter, Smell Stench Breeze Stench Gold Stench Breeze START Breeze PIT Breeze PIT PIT Breeze Breeze B. Beckert: KI für IM p.4

30 Wumpus World Characterization Observable Deterministic Episodic Static Discrete Single agent B. Beckert: KI für IM p.5

31 Wumpus World Characterization Observable No only local perception Deterministic Episodic Static Discrete Single agent B. Beckert: KI für IM p.5

32 Wumpus World Characterization Observable Deterministic No only local perception Yes outcome of action exactly specified Episodic Static Discrete Single agent B. Beckert: KI für IM p.5

33 Wumpus World Characterization Observable Deterministic Episodic No only local perception Yes outcome of action exactly specified No sequential at the level of actions Static Discrete Single agent B. Beckert: KI für IM p.5

34 Wumpus World Characterization Observable Deterministic Episodic Static No only local perception Yes outcome of action exactly specified No sequential at the level of actions Yes wumpus and pits do not move Discrete Single agent B. Beckert: KI für IM p.5

35 Wumpus World Characterization Observable Deterministic Episodic Static Discrete No only local perception Yes outcome of action exactly specified No sequential at the level of actions Yes wumpus and pits do not move Yes Single agent B. Beckert: KI für IM p.5

36 Wumpus World Characterization Observable Deterministic Episodic Static Discrete Single agent No only local perception Yes outcome of action exactly specified No sequential at the level of actions Yes wumpus and pits do not move Yes Yes wumpus is essentially a natural feature B. Beckert: KI für IM p.5

37 Exploring a Wumpus World OK OK A OK B. Beckert: KI für IM p.6

38 Exploring a Wumpus World B OK A OK OK A B. Beckert: KI für IM p.6

39 Exploring a Wumpus World P? B OK P? A OK OK A B. Beckert: KI für IM p.6

40 Exploring a Wumpus World P? B OK P? A OK S OK A A B. Beckert: KI für IM p.6

41 Exploring a Wumpus World P P? B A OK P? OK A OK S A OK W B. Beckert: KI für IM p.6

42 Exploring a Wumpus World P P? B A OK P? OK A A OK S A OK W B. Beckert: KI für IM p.6

43 Exploring a Wumpus World P P? OK B A OK P? OK A OK OK S OK A A W B. Beckert: KI für IM p.6

44 Exploring a Wumpus World P P? OK B A OK P? OK A BGS A OK OK S OK A A W B. Beckert: KI für IM p.6

45 Problematic Situations P? Problem B A OK P? P? Breeze in (1,2) and (2,1) no safe actions A OK B A OK P? B. Beckert: KI für IM p.7

46 Problematic Situations P? Problem B A OK P? P? Breeze in (1,2) and (2,1) no safe actions Possible solution A OK B A OK P? Assuming pits uniformly distributed: (2,2) has pit with probability 0.86 (1,3) and (3,1) have pit with probab B. Beckert: KI für IM p.7

47 Problematic Situations Problem Smell in (1,1) no safe actions S A B. Beckert: KI für IM p.8

48 Problematic Situations Problem Smell in (1,1) no safe actions Possible solution S A Strategy of coercion: shoot straight ahead wumpus was there dead safe wumpus wasn t there safe B. Beckert: KI für IM p.8

49 Logic in General

50 Logic in General Logics Formal languages for representing information, such that conclusions can be drawn Syntax Defines the sentences in the language Semantics Defines the meaning of sentences; i.e., defines truth of a sentence in a world B. Beckert: KI für IM p.9

51 Example: Language of Arithmetic Syntax x + 2 y x2 + y > is a sentence is not a sentence B. Beckert: KI für IM p.10

52 Example: Language of Arithmetic Syntax x + 2 y x2 + y > is a sentence is not a sentence Semantics x + 2 y is true iff the number x + 2 is no less than the number y x + 2 y is true in a world where x = 7, y = 1 x + 2 y is false in a world where x = 0, y = 6 B. Beckert: KI für IM p.10

53 Entailment Definition Knowledge base KB entails sentence α if and only if α is true in all worlds where KB is true Notation KB = α B. Beckert: KI für IM p.11

54 Entailment Definition Knowledge base KB entails sentence α if and only if α is true in all worlds where KB is true Notation KB = α Note Entailment is a relationship between sentences (i.e., syntax) that is based on semantics B. Beckert: KI für IM p.11

55 Entailment Example The KB containing the shirt is green and the shirt is striped entails the shirt is green or the shirt is striped Example x + y = 4 entails 4 = x + y B. Beckert: KI für IM p.12

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57 Models Intuition Models are formally structured worlds, with respect to which truth can be evaluated B. Beckert: KI für IM p.13

58 Models Intuition Models are formally structured worlds, with respect to which truth can be evaluated Definition m is a model of a sentence α if α is true in m M(α) is the set of all models of α Note KB = α if and only if M(KB) M(α) B. Beckert: KI für IM p.13

59 Models: Example KB = The shirt is green and striped α = The shirt is green M( ) M(KB) x x x x x x x x x x x x x x x x x x x x x x x x x xx x x x x x x x x x x x x x x x x x x x x B. Beckert: KI für IM p.14

60 Entailment in the Wumpus World Situation after detecting nothing in [1,1], moving right, breeze in [2,1]?? Consider possible models for? s (considering only pits) 3 Boolean choices A B A? 8 possible models B. Beckert: KI für IM p.15

61 Wumpus Models 2 2 PIT 1 Breeze PIT 1 Breeze PIT 2 2 PIT 1 Breeze Breeze Breeze PIT PIT 2 PIT PIT 1 Breeze PIT PIT PIT 1 Breeze Breeze PIT B. Beckert: KI für IM p.16

62 Wumpus Models 2 2 PIT 1 Breeze PIT 1 Breeze KB PIT 2 2 PIT 1 Breeze Breeze Breeze PIT PIT 2 PIT PIT 1 Breeze PIT PIT PIT 1 Breeze Breeze PIT KB = wumpus-world rules + observations B. Beckert: KI für IM p.16

63 Wumpus Models KB = α PIT 1 Breeze PIT 1 Breeze KB PIT 2 2 PIT 1 Breeze Breeze Breeze PIT PIT 2 PIT PIT 1 Breeze PIT PIT PIT 1 Breeze Breeze PIT KB = wumpus-world rules + observations α 1 = [1,2] is safe B. Beckert: KI für IM p.16

64 Wumpus Models 2 2 PIT 1 Breeze PIT 1 Breeze KB PIT 2 2 PIT 1 Breeze Breeze Breeze PIT PIT 2 PIT PIT 1 Breeze PIT PIT PIT 1 Breeze Breeze PIT KB = wumpus-world rules + observations B. Beckert: KI für IM p.16

65 Wumpus Models KB = α PIT KB 1 Breeze PIT Breeze PIT 2 2 PIT 1 Breeze Breeze Breeze PIT PIT 2 PIT PIT 1 Breeze PIT PIT PIT 1 Breeze Breeze PIT KB = wumpus-world rules + observations α 2 = [2,2] is safe B. Beckert: KI für IM p.16

66 Inference Definition KB i α means sentence α can be derived from KB by inference procedure i B. Beckert: KI für IM p.17

67 Inference Definition KB i α means sentence α can be derived from KB by inference procedure i Soundness (of i) Whenever KB i α, it is also true that KB = α Completeness (of i) Whenever KB = α, it is also true that KB i α B. Beckert: KI für IM p.17

68 Preview First-order Logic We will define a logic (first-order logic) that is expressive enough to say almost anything of interest, and for which there exists a sound and complete inference procedure. That is, the procedure will answer any question whose answer follows from what is known by the KB. B. Beckert: KI für IM p.18

69 Propositional (Boolean) Logic

70 Propositional Logic: Syntax Definition Propositional symbols A, B, P 1, P 2, ShirtIsGreen, etc. are (atomic) sentences If S,S 1,S 2 are sentences, then S (negation) S 1 S 2 (con junction) S 1 S 2 (dis junction) S 1 S 2 (implication) S 1 S 2 (equivalence) are sentences B. Beckert: KI für IM p.19

71 Propositional logic: Semantics Propositional Models Each model specifies true/false for each proposition symbol B. Beckert: KI für IM p.20

72 Propositional logic: Semantics Propositional Models Each model specifies true/false for each proposition symbol Example A B C true true false (For three symbols, there are 8 possible models) B. Beckert: KI für IM p.20

73 Propositional logic: Semantics Propositional Models Each model specifies true/false for each proposition symbol Example A B C true true false (For three symbols, there are 8 possible models) Rules for evaluating truth with respect to a model S is true iff S is false S 1 S 2 is true iff S 1 is true and S 2 is true S 1 S 2 is true iff S 1 is true or S 2 is true S 1 S 2 is true iff S 1 is false or S 2 is true S 1 S 2 is true iff S 1 and S 2 have the same truth value B. Beckert: KI für IM p.20

74 Truth Tables for Connectives A B A A B A B A B A B false false true false false true true false true true false true true false true false false false true false false true true false true true true true B. Beckert: KI für IM p.21

75 Wumpus World Sentences Propositional symbols P i, j means: B i, j means: there is a pit in [i, j] there is a breeze in [i, j] P 1,1 B 1,1 B 2,1 B. Beckert: KI für IM p.22

76 Wumpus World Sentences Propositional symbols P i, j means: B i, j means: there is a pit in [i, j] there is a breeze in [i, j] P 1,1 B 1,1 B 2,1 Sentences Pits cause breezes in adjacent squares P 1,2 (B 1,1 B 1,3 B 2,2 ) A square is breezy if and only if there is an adjacent pit B 1,1 (P 1,2 P 2,1 ) B 2,1 (P 1,1 P 2,2 P 3,1 ) B. Beckert: KI für IM p.22

77 Propositional Inference: Enumeration Method Example α = A B KB = (A C) (B C) Checking that KB = α A B C A C B C KB α false false false false false true false true false false true true true false false true false true true true false true true true B. Beckert: KI für IM p.23

78 Propositional Inference: Enumeration Method Example α = A B KB = (A C) (B C) Checking that KB = α A B C A C B C KB α false false false false false false true true false true false false false true true true true false false true true false true true true true false true true true true true B. Beckert: KI für IM p.23

79 Propositional Inference: Enumeration Method Example α = A B KB = (A C) (B C) Checking that KB = α A B C A C B C KB α false false false false true false false true true false false true false false true false true true true true true false false true true true false true true false true true false true true true true true true true B. Beckert: KI für IM p.23

80 Propositional Inference: Enumeration Method Example α = A B KB = (A C) (B C) Checking that KB = α A B C A C B C KB α false false false false true false false false true true false false false true false false true false false true true true true true true false false true true true true false true true false false true true false true true true true true true true true true B. Beckert: KI für IM p.23

81 Propositional Inference: Enumeration Method Example α = A B KB = (A C) (B C) Checking that KB = α A B C A C B C KB α false false false false true false false false false true true false false false false true false false true false true false true true true true true true true false false true true true true true false true true false false true true true false true true true true true true true true true true true B. Beckert: KI für IM p.23

82 Propositional Inference: Enumeration Method Example α = A B KB = (A C) (B C) Checking that KB = α A B C A C B C KB α false false false false true false false false false true true false false false false true false false true false true false true true true true true true true false false true true true true true false true true false false true true true false true true true true true true true true true true true B. Beckert: KI für IM p.23

83 Propositional Inference: Enumeration Method Example α = A B KB = (A C) (B C) Checking that KB = α Note A B C A C B C KB α false false false false true false false false false true true false false false false true false false true false true false true true true true true true true false false true true true true true false true true false false true true true false true true true true true true true true true true true Table has 2 n rows for n symbols B. Beckert: KI für IM p.23

84 Logical Equivalence Definition Two sentences are logically equivalent, denoted by α β iff they are true in the same models, i.e., iff: α = β and β = α B. Beckert: KI für IM p.24

85 Logical Equivalence Definition Two sentences are logically equivalent, denoted by α β iff they are true in the same models, i.e., iff: α = β and β = α Example (A B) ( B A) (contraposition) B. Beckert: KI für IM p.24

86 Logical Equivalence Theorem If α β γ is the result of replacing a subformula α of δ by β, then γ δ B. Beckert: KI für IM p.25

87 Logical Equivalence Theorem If α β γ is the result of replacing a subformula α of δ by β, then γ δ Example A B B A implies (C (A B)) D (C (B A)) D B. Beckert: KI für IM p.25

88 Important Equivalences (α β) (β α) commutativity of (α β) (β α) commutativity of ((α β) γ) (α (β γ)) associativity of ((α β) γ) (α (β γ)) associativity of (α α) α idempotence for (α α) α idempotence for α α double-negation elimination (α β) ( β α) contraposition (α β) ( α β) implication elimination (α β) ((α β) (β α)) equivalence elimination (α β) ( α β) de Morgan s rules (α β) ( α β) de Morgan s rules (α (β γ)) ((α β) (α γ)) distributivity of over (α (β γ)) ((α β) (α γ)) distributivity of over B. Beckert: KI für IM p.26

89 The Logical Constants true and false Semantics true evaluates to true in all models false evaluates to false in all models B. Beckert: KI für IM p.27

90 The Logical Constants true and false Semantics true evaluates to true in all models false evaluates to false in all models Important equivalences with true and false (α α) false (α α) true tertium non datur (α true) α (α false) false (α true) true (α false) α B. Beckert: KI für IM p.27

91 Validity Definition A sentence is valid if it is true in all models Examples A A, A A, (A (A B)) B B. Beckert: KI für IM p.28

92 Validity Definition A sentence is valid if it is true in all models Examples A A, A A, (A (A B)) B Deduction Theorem (connects inference and validity) KB = α if and only if KB α is valid B. Beckert: KI für IM p.28

93 Satisfiability Definition A sentence is satisfiable if it is true in some model Examples A B, A, A (A B) B. Beckert: KI für IM p.29

94 Satisfiability Definition A sentence is satisfiable if it is true in some model Examples A B, A, A (A B) Definition A sentence is unsatisfiable if it is true in no models, i.e., if it is not satisfiable Example A A B. Beckert: KI für IM p.29

95 Satisfiability Theorem (connects validity and unsatisfiability) α is valid if and only if α is unsatisfiable B. Beckert: KI für IM p.30

96 Satisfiability Theorem (connects validity and unsatisfiability) α is valid if and only if α is unsatisfiable Theorem (connects inference and unsatisfiability) KB = α if and only if (KB α) is unsatisfiable B. Beckert: KI für IM p.30

97 Satisfiability Theorem (connects validity and unsatisfiability) α is valid if and only if α is unsatisfiable Theorem (connects inference and unsatisfiability) KB = α if and only if (KB α) is unsatisfiable Note Validity and inference can be proved by reductio ad absurdum B. Beckert: KI für IM p.30

98 Inference Rules and Theorem Proving

99 Two Kinds of Proof Methods 1. Application of inference rules Legitimate (sound) generation of new sentences from old Construction of / search for a proof (proof = sequence of inference rule applications) B. Beckert: KI für IM p.31

100 Two Kinds of Proof Methods 1. Application of inference rules Legitimate (sound) generation of new sentences from old Construction of / search for a proof (proof = sequence of inference rule applications) Properties Typically requires translation of sentences into a normal form B. Beckert: KI für IM p.31

101 Two Kinds of Proof Methods 1. Application of inference rules Legitimate (sound) generation of new sentences from old Construction of / search for a proof (proof = sequence of inference rule applications) Properties Typically requires translation of sentences into a normal form Different kinds Tableau calculus, resolution, forward/backward chaining,... B. Beckert: KI für IM p.31

102 Two Kinds of Proof Methods 2. Model checking Construction of / search for a satisfying model B. Beckert: KI für IM p.32

103 Two Kinds of Proof Methods 2. Model checking Construction of / search for a satisfying model Different kinds Truth table enumeration (always exponential number of symbols) Improved backtracking search for models e.g.: Davis-Putnam-Logemann-Loveland Heuristic search in model space e.g.: hill-climbing algorithms (sound but incomplete) B. Beckert: KI für IM p.32

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127 Two Kinds of Proof Methods 2. Model checking Construction of / search for a satisfying model Different kinds Truth table enumeration (always exponential number of symbols) Improved backtracking search for models e.g.: Davis-Putnam-Logemann-Loveland Heuristic search in model space e.g.: hill-climbing algorithms (sound but incomplete) B. Beckert: KI für IM p.32

128 Normal Forms Literal A literal is an atomic sentence (propositional symbol), or the negation of an atomic sentence B. Beckert: KI für IM p.33

129 Normal Forms Literal A literal is an atomic sentence (propositional symbol), or the negation of an atomic sentence Clause A disjunction of literals B. Beckert: KI für IM p.33

130 Normal Forms Literal A literal is an atomic sentence (propositional symbol), or the negation of an atomic sentence Clause A disjunction of literals Conjunctive Normal Form (CNF) A conjunction of disjunctions of literals, i.e., a conjunction of clauses Example (A B) (B C D) B. Beckert: KI für IM p.33

131 Resolution Inference rule P 1 P i 1 Q P i+1... P k R 1 R j 1 Q R j+1... R n P 1 P i 1 P i+1... P k R 1 R j 1 R j+1... R n B. Beckert: KI für IM p.34

132 Resolution Inference rule P 1 P i 1 Q P i+1... P k R 1 R j 1 Q R j+1... R n P 1 P i 1 P i+1... P k R 1 R j 1 R j+1... R n Example P 1,3 P 2,2 P 2,2 P 1,3 P P? B OK A P? OK A A OK S A OK W B. Beckert: KI für IM p.34

133 Resolution Correctness theorem Resolution is sound and complete for propositional logic, i.e., given a formula α in CNF (conjunction of clauses): α is unsatisfiable iff the empty clause can be derived from α with resolution B. Beckert: KI für IM p.35

134 Conversion to CNF 0. Given B 1,1 (P 1,2 P 2,1 ) B. Beckert: KI für IM p.36

135 Conversion to CNF 0. Given B 1,1 (P 1,2 P 2,1 ) 1. Eliminate, replacing α β with (α β) (β α) (B 1,1 (P 1,2 P 2,1 )) ((P 1,2 P 2,1 ) B 1,1 ) B. Beckert: KI für IM p.36

136 Conversion to CNF 0. Given B 1,1 (P 1,2 P 2,1 ) 1. Eliminate, replacing α β with (α β) (β α) (B 1,1 (P 1,2 P 2,1 )) ((P 1,2 P 2,1 ) B 1,1 ) 2. Eliminate, replacing α β with α β ( B 1,1 P 1,2 P 2,1 ) ( (P 1,2 P 2,1 ) B 1,1 ) B. Beckert: KI für IM p.36

137 Conversion to CNF 0. Given B 1,1 (P 1,2 P 2,1 ) 1. Eliminate, replacing α β with (α β) (β α) (B 1,1 (P 1,2 P 2,1 )) ((P 1,2 P 2,1 ) B 1,1 ) 2. Eliminate, replacing α β with α β ( B 1,1 P 1,2 P 2,1 ) ( (P 1,2 P 2,1 ) B 1,1 ) 3. Move inwards using de Morgan s rules (and double-negation) ( B 1,1 P 1,2 P 2,1 ) (( P 1,2 P 2,1 ) B 1,1 ) B. Beckert: KI für IM p.36

138 Conversion to CNF 0. Given B 1,1 (P 1,2 P 2,1 ) 1. Eliminate, replacing α β with (α β) (β α) (B 1,1 (P 1,2 P 2,1 )) ((P 1,2 P 2,1 ) B 1,1 ) 2. Eliminate, replacing α β with α β ( B 1,1 P 1,2 P 2,1 ) ( (P 1,2 P 2,1 ) B 1,1 ) 3. Move inwards using de Morgan s rules (and double-negation) ( B 1,1 P 1,2 P 2,1 ) (( P 1,2 P 2,1 ) B 1,1 ) 4. Apply distributivity law ( over ) and flatten ( B 1,1 P 1,2 P 2,1 ) ( P 1,2 B 1,1 ) ( P 2,1 B 1,1 ) B. Beckert: KI für IM p.36

139 Resolution Example Given KB = (B 1,1 (P 1,2 P 2,1 )) B 1,1 α = P 1,2 B. Beckert: KI für IM p.37

140 Resolution Example Given KB = (B 1,1 (P 1,2 P 2,1 )) B 1,1 α = P 1,2 Resolution proof for KB = α Derive empty clause from KB α in CNF B. Beckert: KI für IM p.37

141 Resolution Example Given KB = (B 1,1 (P 1,2 P 2,1 )) B 1,1 α = P 1,2 Resolution proof for KB = α Derive empty clause from KB α in CNF P 2,1 B 1,1 B 1,1 P 1,2 P 2,1 P 1,2 B 1,1 B 1,1 P 1,2 B 1,1 P 1,2 B 1,1 P 1,2 P 2,1 P 1,2 B 1,1 P 2,1 B 1,1 P1,2 P 2,1 P 2,1 P 2,1 P 1,2 B. Beckert: KI für IM p.37

142 Summary Logical agents apply inference to a knowledge base to derive new information and make decisions B. Beckert: KI für IM p.38

143 Summary Logical agents apply inference to a knowledge base to derive new information and make decisions Basic concepts of logic syntax: formal structure of sentences semantics: truth of sentences w.r.t. models entailment: necessary truth of one sentence given another inference: deriving sentences from other sentences soundess: derivations produce only entailed sentences completeness: derivations can produce all entailed sentences B. Beckert: KI für IM p.38

144 Summary Logical agents apply inference to a knowledge base to derive new information and make decisions Basic concepts of logic syntax: formal structure of sentences semantics: truth of sentences w.r.t. models entailment: necessary truth of one sentence given another inference: deriving sentences from other sentences soundess: derivations produce only entailed sentences completeness: derivations can produce all entailed sentences Wumpus world requires the ability to represent partial and negated information, reason by cases, etc. B. Beckert: KI für IM p.38

145 Summary Logical agents apply inference to a knowledge base to derive new information and make decisions Basic concepts of logic syntax: formal structure of sentences semantics: truth of sentences w.r.t. models entailment: necessary truth of one sentence given another inference: deriving sentences from other sentences soundess: derivations produce only entailed sentences completeness: derivations can produce all entailed sentences Wumpus world requires the ability to represent partial and negated information, reason by cases, etc. Resolution is sound and complete for propositional logic B. Beckert: KI für IM p.38

146 Summary Logical agents apply inference to a knowledge base to derive new information and make decisions Basic concepts of logic syntax: formal structure of sentences semantics: truth of sentences w.r.t. models entailment: necessary truth of one sentence given another inference: deriving sentences from other sentences soundess: derivations produce only entailed sentences completeness: derivations can produce all entailed sentences Wumpus world requires the ability to represent partial and negated information, reason by cases, etc. Resolution is sound and complete for propositional logic Propositional logic lacks expressive power B. Beckert: KI für IM p.38