Kecerdasan Buatan - Universitas Brawijayamalifauzi.lecture.ub.ac.id/files/2015/09/Logical-Agent.pdf · Kecerdasan Buatan M. Ali Fauzi. Artificial Intelligence M. Ali Fauzi. Logical

KecerdasanBuatanM. Ali Fauzi

Artificial Intelligence

M. Ali Fauzi

Logical AgentsM. Ali Fauzi

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.

TODAY

~ Knowledge-based agents

~ Knowledge-based agents~ Wumpus world

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

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

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

Knowledge-based agents

VS

Problem SolvingAgent

Knowledge-basedAgent

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)

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

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


Knowledge Base~ The central component of a

knowledge-based agent is its knowledge base, or KB

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.

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

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)


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

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

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

Procedural approach to building an agent

~ Tell it what to do~ If the program is not correct ? (error?)

Knowledge representation

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

Knowledge is power

Representation + Reasoning = Intelligence!!

Wumpus World

Wumpus World PEAS description

Performance measuregold +1000, death -1000-1 per step, -10 for using the arrow

EnvironmentSquares adjacent to wumpus are smellySquares adjacent to pit are breezyGlitter iff gold is in the same squareShooting kills wumpus if you are facing itShooting uses up the only arrowGrabbing picks up gold if in same squareReleasing drops the gold in same square

ActuatorsLeft turn, Right turn,Forward, Grab, Release, Shoot

SensorsBreeze, Glitter, Smell

Breeze Breeze

Breeze

BreezeBreeze

Stench

Stench

BreezePIT

PIT

PIT

1 2 3 4

1

2

3

4

START

Gold

Stench

B. Beckert: KI für IM – p.4

Wumpus World Characterization

Observable

No – only local perception

Deterministic

Yes – outcome of action exactly specified

Episodic

No – sequential at the level of actions

Static

Yes – wumpus and pits do not move

Discrete

Yes

Single agent

Yes – wumpus is essentially a natural feature



Observable No – only local perception

Deterministic

Yes – outcome of action exactly specified

Episodic


Static


Discrete

Yes

Single agent





Deterministic Yes – outcome of action exactly specified

Episodic


Static


Discrete

Yes

Single agent






Episodic No – sequential at the level of actions

Static


Discrete

Yes

Single agent







Static Yes – wumpus and pits do not move

Discrete

Yes

Single agent








Discrete Yes

Single agent








Discrete Yes

Single agent Yes – wumpus is essentially a natural feature


Exploring a Wumpus World

A

OK

OKOK



OK

OK OK

A

A

B



OK

OK OK

A

A

B

P?

P?



OK

OK OK

A

A

B

P?

P?

A

S



OK

OK OK

A

A

B

P?

P?

A

S

OK

P

WB. Beckert: KI für IM – p.6


OK

OK OK

A

A

B

P?

P?

A

S

OK

P

W

A



OK

OK OK

A

A

B

P?

P?

A

S

OK

P

W

A

OK

OK



OK

OK OK

A

A

B

P?

P?

A

S

OK

P

W

A

OK

OK

A

BGS


Problematic Situations

A

B OK

OK OK

A

B

A

P?

P?P?

P?

Problem

Breeze in (1,2) and (2,1)⇒ no safe actions

Possible solution

Assuming pits uniformly distributed:

(2,2) has pit with probability 0.86(1,3) and (3,1) have pit with probab. 0.31



A

B OK

OK OK

A

B

A

P?

P?P?

P?

Problem

Breeze in (1,2) and (2,1)⇒ no safe actions

Possible solution

Assuming pits uniformly distributed:

(2,2) has pit with probability 0.86(1,3) and (3,1) have pit with probab. 0.31



A

S

Problem

Smell in (1,1)⇒ no safe actions

Possible solution

Strategy of coercion:

shoot straight aheadwumpus was there ⇒ dead ⇒ safewumpus wasn’t there ⇒ safe



A

S

Problem

Smell in (1,1)⇒ no safe actions

Possible solution

Strategy of coercion:

shoot straight aheadwumpus was there ⇒ dead ⇒ safewumpus wasn’t there ⇒ safe


Logic in General

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


Example: Language of Arithmetic

Syntax

x+2 ≥ y is a sentence

x2+ y > 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


Example: Language of Arithmetic

Syntax

x+2 ≥ y is a sentence

x2+ y > 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


Entailment

Definition

Knowledge base KB entails sentence αif 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


Entailment

Definition

Knowledge base KB entails sentence αif 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


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


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)


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)


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

xx

xx

xx

xx

x

xxxx

x x

xx

xx x

x

xx

x x x

x

xx

x

x

x

x

x

x

x

x


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 choices8 possible models

AA

B

??

?


Wumpus Models

KB 6|= α2

1 2 3

1

2

Breeze

PIT

1 2 3

1

2

Breeze

PIT

1 2 3

1

2

Breeze

PIT PIT

PIT

1 2 3

1

2

Breeze

PIT

PIT

1 2 3

1

2

BreezePIT

1 2 3

1

2

Breeze

PIT

PIT

1 2 3

1

2

Breeze

PIT PIT

1 2 3

1

2

Breeze

KB = wumpus-world rules + observations


Wumpus Models

KB 6|= α2

1 2 3

1

2

Breeze

PIT

1 2 3

1

2

Breeze

PIT

1 2 3

1

2

Breeze

PIT PIT

PIT

1 2 3

1

2

Breeze

PIT

PIT

1 2 3

1

2

BreezePIT

1 2 3

1

2

Breeze

PIT

PIT

1 2 3

1

2

Breeze

PIT PIT

1 2 3

1

2

Breeze

KB



Wumpus Models

KB |= α1

KB 6|= α2

1 2 3

1

2

Breeze

PIT

1 2 3

1

2

Breeze

PIT

1 2 3

1

2

Breeze

PIT PIT

PIT

1 2 3

1

2

Breeze

PIT

PIT

1 2 3

1

2

BreezePIT

1 2 3

1

2

Breeze

PIT

PIT

1 2 3

1

2

Breeze

PIT PIT

1 2 3

1

2

Breeze

KB1


α1 = “[1,2] is safe”B. Beckert: KI für IM – p.16

Wumpus Models

KB 6|= α2

1 2 3

1

2

Breeze

PIT

1 2 3

1

2

Breeze

PIT

1 2 3

1

2

Breeze

PIT PIT

PIT

1 2 3

1

2

Breeze

PIT

PIT

1 2 3

1

2

BreezePIT

1 2 3

1

2

Breeze

PIT

PIT

1 2 3

1

2

Breeze

PIT PIT

1 2 3

1

2

Breeze

KB



Wumpus Models

KB 6|= α2

1 2 3

1

2

Breeze

PIT

1 2 3

1

2

Breeze

PIT

1 2 3

1

2

Breeze

PIT PIT

PIT

1 2 3

1

2

Breeze

PIT

PIT

1 2 3

1

2

BreezePIT

1 2 3

1

2

Breeze

PIT

PIT

1 2 3

1

2

Breeze

PIT PIT

1 2 3

1

2

Breeze

KB2


α2 = “[2,2] is safe”B. Beckert: KI für IM – p.16

Inference

Definition

KB ì α

means

sentence α can be derived from KB by inference procedure i

Soundness (of i)

Whenever KB ì α, it is also true that KB |= α

Completeness (of i)

Whenever KB |= α, it is also true that KB ì α


Inference

Definition

KB ì α

means

sentence α can be derived from KB by inference procedure i

Soundness (of i)

Whenever KB ì α, it is also true that KB |= α

Completeness (of i)

Whenever KB |= α, it is also true that KB ì α


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 followsfrom what is known by the KB.


Propositional (Boolean) Logic

Propositional Logic: Syntax

Definition

Propositional symbols

A, B, P1, P2, ShirtIsGreen, etc.

are (atomic) sentences

If S,S1,S2 are sentences, then

¬S (negation)

S1 ∧S2 (con junction)

S1 ∨S2 (dis junction)

S1 ⇒ S2 (implication)

S1 ⇔ S2 (equivalence)

are sentences


Propositional logic: Semantics

Propositional Models

Each model specifies true/false for each proposition symbol

Example A B Ctrue true false

(For three symbols, there are8 possible models)

Rules for evaluating truth with respect to a model

¬S is true iff S is false

S1 ∧S2 is true iff S1 is true and S2 is true

S1 ∨S2 is true iff S1 is true or S2 is true

S1 ⇒ S2 is true iff S1 is false or S2 is true

S1 ⇔ S2 is true iff S1 and S2 have the same truth value


























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


Wumpus World Sentences


Pi, j means: “there is a pit in [i, j]”Bi, j means: “there is a breeze in [i, j]”

¬P1,1 ¬B1,1 B2,1

Sentences

“Pits cause breezes in adjacent squares”

P1,2 ⇒ (B1,1 ∧B1,3 ∧B2,2)

“A square is breezy if and only if there is an adjacent pit”

B1,1 ⇔ (P1,2 ∨P2,1)

B2,1 ⇔ (P1,1 ∨P2,2 ∨P3,1)


Wumpus World Sentences


Pi, j means: “there is a pit in [i, j]”Bi, j means: “there is a breeze in [i, j]”

¬P1,1 ¬B1,1 B2,1

Sentences

“Pits cause breezes in adjacent squares”

P1,2 ⇒ (B1,1 ∧B1,3 ∧B2,2)

“A square is breezy if and only if there is an adjacent pit”

B1,1 ⇔ (P1,2 ∨P2,1)

B2,1 ⇔ (P1,1 ∨P2,2 ∨P3,1)


Propositional Inference: Enumeration Method

Exampleα = A∨B KB = (A∨C)∧ (B∨¬C)

Checking that KB |= α

A B C A∨C B∨¬C KB αfalse 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 false false

true true

true false true

true false false true

true true false

true true

true true true

true true

Note

Table has 2n rows for n symbols





A B C A∨C B∨¬C KB αfalse 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 false false true

true

true false true true

false false true

true true false true

true

true true true true

true

Note






A B C A∨C B∨¬C KB αfalse false false false true

false false

false false true true false

false false

false true false false true

false true

false true true true truetrue false false true truetrue false true true false

false true

true true false true truetrue true true true true

Note






A B C A∨C B∨¬C KB αfalse 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 truetrue false false true true truetrue false true true false false

true

true true false true true truetrue true true true true true

Note






A B C A∨C B∨¬C KB αfalse false false false true false falsefalse false true true false false falsefalse true false false true false truefalse true true true true true truetrue false false true true true truetrue false true true false false truetrue true false true true true truetrue true true true true true true

Note







Note







Note

Table has 2n rows for n symbolsB. Beckert: KI für IM – p.23

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)


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)


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


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


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 ∧


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


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


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


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


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


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∧¬AB. Beckert: KI für IM – p.29

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


Satisfiability





Note



Satisfiability





Note



Inference Rules and Theorem Proving

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, . . .






Properties


Different kinds







Properties


Different kinds




2. Model checking

Construction of / search for a satisfying model

Different kinds

Truth table enumeration (always exponential number of symbols)

Improved backtracking search for modelse.g.: Davis-Putnam-Logemann-Loveland

Heuristic search in model space (sound but incomplete)e.g.: hill-climbing algorithms



2. Model checking


Different kinds






2. Model checking


Different kinds





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)


Normal Forms

Literal

A literal is


Clause




Example

(A∨¬B) ∧ (B∨¬C∨¬D)


Normal Forms

Literal

A literal is


Clause




Example

(A∨¬B) ∧ (B∨¬C∨¬D)B. Beckert: KI für IM – p.33

Resolution

Inference rule

P1 ∨·· ·∨Pi−1 ∨Q∨Pi+1 ∨ . . .∨Pk R1 ∨·· ·∨R j−1 ∨¬Q∨R j+1 ∨ . . .∨Rn

P1 ∨·· ·∨Pi−1 ∨Pi+1 ∨ . . .∨Pk ∨ R1 ∨·· ·∨R j−1 ∨R j+1 ∨ . . .∨Rn

Example

P1,3 ∨P2,2 ¬P2,2

P1,3OK

OK OK

A

A

B

P?

P?

A

S

OK

P

W

A


Resolution

Inference rule

P1 ∨·· ·∨Pi−1 ∨Q∨Pi+1 ∨ . . .∨Pk R1 ∨·· ·∨R j−1 ∨¬Q∨R j+1 ∨ . . .∨Rn

P1 ∨·· ·∨Pi−1 ∨Pi+1 ∨ . . .∨Pk ∨ R1 ∨·· ·∨R j−1 ∨R j+1 ∨ . . .∨Rn

Example

P1,3 ∨P2,2 ¬P2,2

P1,3OK

OK OK

A

A

B

P?

P?

A

S

OK

P

W

A


Resolution

Correctness theorem

Resolution is sound and complete for propositional logic,

i.e., given a formula α in CNF (conjunction of clauses):

α is unsatisfiableiffthe empty clause can be derived from α with resolution


Conversion to CNF

0. Given

B1,1 ⇔ (P1,2 ∨P2,1)

1. Eliminate ⇔, replacing α ≡ β with (α ⇒ β)∧ (β ⇒ α)

(B1,1 ⇒ (P1,2 ∨P2,1)) ∧ ((P1,2 ∨P2,1) ⇒ B1,1)

2. Eliminate ⇒, replacing α ⇒ β with ¬α∨β

(¬B1,1 ∨P1,2 ∨P2,1) ∧ (¬(P1,2 ∨P2,1)∨B1,1)

3. Move ¬ inwards using de Morgan’s rules (and double-negation)

(¬B1,1 ∨P1,2 ∨P2,1) ∧ ((¬P1,2 ∧¬P2,1)∨B1,1)

4. Apply distributivity law (∨ over ∧) and flatten

(¬B1,1 ∨P1,2 ∨P2,1) ∧ (¬P1,2 ∨B1,1) ∧ (¬P2,1 ∨B1,1)


Conversion to CNF

0. Given

B1,1 ⇔ (P1,2 ∨P2,1)


(B1,1 ⇒ (P1,2 ∨P2,1)) ∧ ((P1,2 ∨P2,1) ⇒ B1,1)


(¬B1,1 ∨P1,2 ∨P2,1) ∧ (¬(P1,2 ∨P2,1)∨B1,1)


(¬B1,1 ∨P1,2 ∨P2,1) ∧ ((¬P1,2 ∧¬P2,1)∨B1,1)


(¬B1,1 ∨P1,2 ∨P2,1) ∧ (¬P1,2 ∨B1,1) ∧ (¬P2,1 ∨B1,1)


Conversion to CNF

0. Given

B1,1 ⇔ (P1,2 ∨P2,1)


(B1,1 ⇒ (P1,2 ∨P2,1)) ∧ ((P1,2 ∨P2,1) ⇒ B1,1)


(¬B1,1 ∨P1,2 ∨P2,1) ∧ (¬(P1,2 ∨P2,1)∨B1,1)


(¬B1,1 ∨P1,2 ∨P2,1) ∧ ((¬P1,2 ∧¬P2,1)∨B1,1)


(¬B1,1 ∨P1,2 ∨P2,1) ∧ (¬P1,2 ∨B1,1) ∧ (¬P2,1 ∨B1,1)


Conversion to CNF

0. Given

B1,1 ⇔ (P1,2 ∨P2,1)


(B1,1 ⇒ (P1,2 ∨P2,1)) ∧ ((P1,2 ∨P2,1) ⇒ B1,1)


(¬B1,1 ∨P1,2 ∨P2,1) ∧ (¬(P1,2 ∨P2,1)∨B1,1)


(¬B1,1 ∨P1,2 ∨P2,1) ∧ ((¬P1,2 ∧¬P2,1)∨B1,1)


(¬B1,1 ∨P1,2 ∨P2,1) ∧ (¬P1,2 ∨B1,1) ∧ (¬P2,1 ∨B1,1)


Conversion to CNF

0. Given

B1,1 ⇔ (P1,2 ∨P2,1)


(B1,1 ⇒ (P1,2 ∨P2,1)) ∧ ((P1,2 ∨P2,1) ⇒ B1,1)


(¬B1,1 ∨P1,2 ∨P2,1) ∧ (¬(P1,2 ∨P2,1)∨B1,1)


(¬B1,1 ∨P1,2 ∨P2,1) ∧ ((¬P1,2 ∧¬P2,1)∨B1,1)


(¬B1,1 ∨P1,2 ∨P2,1) ∧ (¬P1,2 ∨B1,1) ∧ (¬P2,1 ∨B1,1)


Resolution Example

Given

KB = (B1,1 ⇔ (P1,2 ∨P2,1))∧¬B1,1

α = ¬P1,2

Resolution proof for KB |= α

Derive empty clause 2 from KB∧¬α in CNF

P1,2

P1,2

P2,1

P1,2 B1,1

B1,1 P2,1 B1,1 P1,2 P2,1 P2,1P1,2B1,1 B1,1

P1,2B1,1 P2,1B1,1P2,1 B1,1

P1,2 P2,1 P1,2


Resolution Example

Given

KB = (B1,1 ⇔ (P1,2 ∨P2,1))∧¬B1,1

α = ¬P1,2



P1,2

P1,2

P2,1

P1,2 B1,1

B1,1 P2,1 B1,1 P1,2 P2,1 P2,1P1,2B1,1 B1,1

P1,2B1,1 P2,1B1,1P2,1 B1,1

P1,2 P2,1 P1,2


Resolution Example

Given

KB = (B1,1 ⇔ (P1,2 ∨P2,1))∧¬B1,1

α = ¬P1,2



P1,2

P1,2

P2,1

P1,2 B1,1

B1,1 P2,1 B1,1 P1,2 P2,1 P2,1P1,2B1,1 B1,1

P1,2B1,1 P2,1B1,1P2,1 B1,1

P1,2 P2,1 P1,2


Summary

Logical agents apply inference to a knowledge baseto 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 representpartial and negated information, reason by cases, etc.

Resolution is sound and complete for propositional logic

Propositional logic lacks expressive power


Summary








Summary








Summary








Summary






Propositional logic lacks expressive powerB. Beckert: KI für IM – p.38

Documents

Kecerdasan Buatan - Universitas Brawijayamalifauzi.lecture.ub.ac.id/files/2015/09/Logical-Agent.pdf · Kecerdasan Buatan M. Ali Fauzi. Artificial Intelligence M. Ali Fauzi. Logical