Logic and Arti cial Intelligenceepacuit/classes/logicai-cmu/logai-lec1.pdf · David Lewis Jakko...

Preview:

Citation preview

Logic and Artificial IntelligenceLecture 1

Eric Pacuit

Currently Visiting the Center for Formal Epistemology, CMU

Center for Logic and Philosophy of ScienceTilburg University

ai.stanford.edu/∼epacuite.j.pacuit@uvt.nl

August 31, 2011

Logic and Artificial Intelligence 1/22

Epistemology

Cognitive

Science

Philosophical

Logic

Dynamic

Epistemic/Doxastic

Logic

Game Theory

CS: Multiagent

Systems

CS: Distributed

Algorithms

Logics of

Rational AgencyEpistemic

Foundations of

Game Theory

????

Logic and Artificial Intelligence 2/22

Epistemology

Cognitive

Science

Philosophical

Logic

Dynamic

Epistemic/Doxastic

Logic

Game Theory

CS: Multiagent

Systems

CS: Distributed

Algorithms

Logics of

Rational AgencyEpistemic

Foundations of

Game Theory

????

Logic and Artificial Intelligence 2/22

Epistemology

Cognitive

Science

Philosophical

Logic

Dynamic

Epistemic/Doxastic

Logic

Game Theory

CS: Multiagent

Systems

CS: Distributed

Algorithms

Logics of

Rational AgencyEpistemic

Foundations of

Game Theory

????

Logic and Artificial Intelligence 2/22

Epistemology

Cognitive

Science

Philosophical

Logic

Dynamic

Epistemic/Doxastic

Logic

Game Theory

CS: Multiagent

Systems

CS: Distributed

Algorithms

Logics of

Rational AgencyEpistemic

Foundations of

Game Theory

????

Logic and Artificial Intelligence 2/22

Epistemology

Cognitive

Science

Philosophical

Logic

Dynamic

Epistemic/Doxastic

Logic

Game Theory

CS: Multiagent

Systems

CS: Distributed

Algorithms

Logics of

Rational AgencyEpistemic

Foundations of

Game Theory

????

Logic and Artificial Intelligence 2/22

Foundations of Epistemic Logic

David Lewis Jakko Hintikka Robert Aumann

Larry Moss Johan van Benthem Alexandru Baltag

Logic and Artificial Intelligence 3/22

Foundations of Epistemic Logic

Logic and Artificial Intelligence 4/22

Single-Agent Epistemic Logic

Let KP informally mean “the agent knows that P (is true)”.

K (P → Q): “Ann knows that P implies Q”

KP ∨ ¬KP: “either Ann does or does not know P”

KP ∨ K¬P: “Ann knows whether P is true”

LP: “P is an epistemic possibility”

KLP: “Ann knows that she thinks P ispossible”

Logic and Artificial Intelligence 5/22

Single-Agent Epistemic Logic

Let KP informally mean “the agent knows that P (is true)”.

K (P → Q): “Ann knows that P implies Q”

KP ∨ ¬KP: “either Ann does or does not know P”

KP ∨ K¬P: “Ann knows whether P is true”

LP: “P is an epistemic possibility”

KLP: “Ann knows that she thinks P ispossible”

Logic and Artificial Intelligence 5/22

Single-Agent Epistemic Logic

Let KP informally mean “the agent knows that P (is true)”.

K (P → Q): “Ann knows that P implies Q”

KP ∨ ¬KP: “either Ann does or does not know P”

KP ∨ K¬P: “Ann knows whether P is true”

LP: “P is an epistemic possibility”

KLP: “Ann knows that she thinks P ispossible”

Logic and Artificial Intelligence 5/22

Single-Agent Epistemic Logic

Let KP informally mean “the agent knows that P (is true)”.

K (P → Q): “Ann knows that P implies Q”

KP ∨ ¬KP: “either Ann does or does not know P”

KP ∨ K¬P: “Ann knows whether P is true”

LP: “P is an epistemic possibility”

KLP: “Ann knows that she thinks P ispossible”

Logic and Artificial Intelligence 5/22

Single-Agent Epistemic Logic

Let KP informally mean “the agent knows that P (is true)”.

K (P → Q): “Ann knows that P implies Q”

KP ∨ ¬KP: “either Ann does or does not know P”

KP ∨ K¬P: “Ann knows whether P is true”

LP: “P is an epistemic possibility”

KLP: “Ann knows that she thinks P ispossible”

Logic and Artificial Intelligence 5/22

Single-Agent Epistemic Logic

Let KP informally mean “the agent knows that P (is true)”.

K (P → Q): “Ann knows that P implies Q”

KP ∨ ¬KP: “either Ann does or does not know P”

KP ∨ K¬P: “Ann knows whether P is true”

LP: “P is an epistemic possibility”

KLP: “Ann knows that she thinks P ispossible”

Logic and Artificial Intelligence 5/22

ExampleSuppose there are three cards:1, 2 and 3.

Ann is dealt one of the cards,one of the cards is placed facedown on the table and the thirdcard is put back in the deck.

(1, 2)

w1

(1, 3)

w2

(2, 3)

w3

(2, 1)

w4

(3, 1)

w5

(3, 2)

w6

Logic and Artificial Intelligence 6/22

ExampleSuppose there are three cards:1, 2 and 3.

Ann is dealt one of the cards,one of the cards is placed facedown on the table and the thirdcard is put back in the deck.

What are the relevant states?

(1, 2)

w1

(1, 3)

w2

(2, 3)

w3

(2, 1)

w4

(3, 1)

w5

(3, 2)

w6

Logic and Artificial Intelligence 6/22

ExampleSuppose there are three cards:1, 2 and 3.

Ann is dealt one of the cards,one of the cards is placed facedown on the table and the thirdcard is put back in the deck.

What are the relevant states?

(1, 2)

w1

(1, 3)

w2

(2, 3)

w3

(2, 1)

w4

(3, 1)

w5

(3, 2)

w6

Logic and Artificial Intelligence 6/22

ExampleSuppose there are three cards:1, 2 and 3.

Ann is dealt one of the cards,one of the cards is placed facedown on the table and the thirdcard is put back in the deck.

Ann receives card 3 and card 1is put on the table

(1, 2)

w1

(1, 3)

w2

(2, 3)

w3

(2, 1)

w4

(3, 1)

w5

(3, 2)

w6

Logic and Artificial Intelligence 6/22

ExampleSuppose there are three cards:1, 2 and 3.

Ann is dealt one of the cards,one of the cards is placed facedown on the table and the thirdcard is put back in the deck.

What information does Annhave?

(1, 2)

w1

(1, 3)

w2

(2, 3)

w3

(2, 1)

w4

(3, 1)

w5

(3, 2)

w6

Logic and Artificial Intelligence 6/22

ExampleSuppose there are three cards:1, 2 and 3.

Ann is dealt one of the cards,one of the cards is placed facedown on the table and the thirdcard is put back in the deck.

What information does Annhave?

(1, 2)

w1

(1, 3)

w2

(2, 3)

w3

(2, 1)

w4

(3, 1)

w5

(3, 2)

w6

Logic and Artificial Intelligence 6/22

ExampleSuppose there are three cards:1, 2 and 3.

Ann is dealt one of the cards,one of the cards is placed facedown on the table and the thirdcard is put back in the deck.

What information does Annhave?

(1, 2)

w1

(1, 3)

w2

(2, 3)

w3

(2, 1)

w4

(3, 1)

w5

(3, 2)

w6

Logic and Artificial Intelligence 6/22

ExampleSuppose there are three cards:1, 2 and 3.

Ann is dealt one of the cards,one of the cards is placed facedown on the table and the thirdcard is put back in the deck.

Suppose Hi is intended tomean “Ann has card i”

Ti is intended to mean “card iis on the table”

Eg., V (H1) = {w1,w2}

(1, 2)

w1

(1, 3)

w2

(2, 3)

w3

(2, 1)

w4

(3, 1)

w5

(3, 2)

w6

Logic and Artificial Intelligence 6/22

ExampleSuppose there are three cards:1, 2 and 3.

Ann is dealt one of the cards,one of the cards is placed facedown on the table and the thirdcard is put back in the deck.

Suppose Hi is intended tomean “Ann has card i”

Ti is intended to mean “card iis on the table”

Eg., V (H1) = {w1,w2}

H1,T2

w1

H1,T3

w2

H2,T3

w3

H2,T1

w4

H3,T1

w5

H3,T2

w6

Logic and Artificial Intelligence 6/22

ExampleSuppose there are three cards:1, 2 and 3.

Ann is dealt one of the cards,one of the cards is placed facedown on the table and the thirdcard is put back in the deck.

H1,T2

w1

H1,T3

w2

H2,T3

w3

H2,T1

w4

H3,T1

w5

H3,T2

w6

Logic and Artificial Intelligence 7/22

ExampleSuppose there are three cards:1, 2 and 3.

Ann is dealt one of the cards,one of the cards is placed facedown on the table and the thirdcard is put back in the deck.

Suppose that Ann receives card1 and card 2 is on the table.

H1,T2

w1

H1,T3

w2

H2,T3

w3

H2,T1

w4

H3,T1

w5

H3,T2

w6

Logic and Artificial Intelligence 7/22

ExampleSuppose there are three cards:1, 2 and 3.

Ann is dealt one of the cards,one of the cards is placed facedown on the table and the thirdcard is put back in the deck.

Suppose that Ann receives card1 and card 2 is on the table.

H1,T2

w1

H1,T3

w2

H2,T3

w3

H2,T1

w4

H3,T1

w5

H3,T2

w6

Logic and Artificial Intelligence 7/22

ExampleSuppose there are three cards:1, 2 and 3.

Ann is dealt one of the cards,one of the cards is placed facedown on the table and the thirdcard is put back in the deck.

M,w1 |= KH1

H1,T2

w1

H1,T3

w2

H2,T3

w3

H2,T1

w4

H3,T1

w5

H3,T2

w6

Logic and Artificial Intelligence 7/22

ExampleSuppose there are three cards:1, 2 and 3.

Ann is dealt one of the cards,one of the cards is placed facedown on the table and the thirdcard is put back in the deck.

M,w1 |= KH1

H1,T2

w1

H1,T3

w2

H2,T3

w3

H2,T1

w4

H3,T1

w5

H3,T2

w6

Logic and Artificial Intelligence 7/22

ExampleSuppose there are three cards:1, 2 and 3.

Ann is dealt one of the cards,one of the cards is placed facedown on the table and the thirdcard is put back in the deck.

M,w1 |= KH1

M,w1 |= K¬T1

H1,T2

w1

H1,T3

w2

H2,T3

w3

H2,T1

w4

H3,T1

w5

H3,T2

w6

Logic and Artificial Intelligence 7/22

ExampleSuppose there are three cards:1, 2 and 3.

Ann is dealt one of the cards,one of the cards is placed facedown on the table and the thirdcard is put back in the deck.

M,w1 |= LT2

H1,T2

w1

H1,T3

w2

H2,T3

w3

H2,T1

w4

H3,T1

w5

H3,T2

w6

Logic and Artificial Intelligence 7/22

ExampleSuppose there are three cards:1, 2 and 3.

Ann is dealt one of the cards,one of the cards is placed facedown on the table and the thirdcard is put back in the deck.

M,w1 |= K (T2 ∨ T3)

H1,T2

w1

H1,T3

w2

H2,T3

w3

H2,T1

w4

H3,T1

w5

H3,T2

w6

Logic and Artificial Intelligence 7/22

Multiagent Epistemic Logic

Many of the examples we are interested in involve more than oneagent!

KAP means “Ann knows P”

KBP means “Bob knows P”

I KAKBϕ: “Ann knows that Bob knows ϕ”

I KA(KBϕ ∨ KB¬ϕ): “Ann knows that Bob knows whether ϕ

I ¬KBKAKB(ϕ): “Bob does not know that Ann knows thatBob knows that ϕ”

Logic and Artificial Intelligence 8/22

Multiagent Epistemic Logic

Many of the examples we are interested in involve more than oneagent!

KAP means “Ann knows P”

KBP means “Bob knows P”

I KAKBϕ: “Ann knows that Bob knows ϕ”

I KA(KBϕ ∨ KB¬ϕ): “Ann knows that Bob knows whether ϕ

I ¬KBKAKB(ϕ): “Bob does not know that Ann knows thatBob knows that ϕ”

Logic and Artificial Intelligence 8/22

Multiagent Epistemic Logic

Many of the examples we are interested in involve more than oneagent!

KAP means “Ann knows P”

KBP means “Bob knows P”

I KAKBϕ: “Ann knows that Bob knows ϕ”

I KA(KBϕ ∨ KB¬ϕ): “Ann knows that Bob knows whether ϕ

I ¬KBKAKB(ϕ): “Bob does not know that Ann knows thatBob knows that ϕ”

Logic and Artificial Intelligence 8/22

ExampleSuppose there are three cards:1, 2 and 3.

Ann is dealt one of the cards,one of the cards is placed facedown on the table and the thirdcard is put back in the deck.

Suppose that Ann receives card1 and card 2 is on the table.

H1,T2

w1

H1,T3

w2

H2,T3

w3

H2,T1

w4

H3,T1

w5

H3,T2

w6

Logic and Artificial Intelligence 9/22

ExampleSuppose there are three cards:1, 2 and 3.

Ann is dealt one of the cards,Bob is given one of the cardsand the third card is put backin the deck.

Suppose that Ann receives card1 and Bob receives card 2.

A1,B2

w1

A1,B3

w2

A2,B3

w3

A2,B1

w4

A3,B1

w5

A3,B2

w6

Logic and Artificial Intelligence 9/22

ExampleSuppose there are three cards:1, 2 and 3.

Ann is dealt one of the cards,Bob is given one of the cardsand the third card is put backin the deck.

Suppose that Ann receives card1 and Bob receives card 2.

A1,B2

w1

A1,B3

w2

A2,B3

w3

A2,B1

w4

A3,B1

w5

A3,B2

w6

Logic and Artificial Intelligence 9/22

ExampleSuppose there are three cards:1, 2 and 3.

Ann is dealt one of the cards,Bob is given one of the cardsand the third card is put backin the deck.

Suppose that Ann receives card1 and Bob receives card 2.

A1,B2

w1

A1,B3

w2

A2,B3

w3

A2,B1

w4

A3,B1

w5

A3,B2

w6

Logic and Artificial Intelligence 9/22

ExampleSuppose there are three cards:1, 2 and 3.

Ann is dealt one of the cards,Bob is given one of the cardsand the third card is put backin the deck.

Suppose that Ann receives card1 and Bob receives card 2.

M,w1 |= KB(KAA1 ∨ KA¬A1)

A1,B2

w1

A1,B3

w2

A2,B3

w3

A2,B1

w4

A3,B1

w5

A3,B2

w6

Logic and Artificial Intelligence 9/22

ExampleSuppose there are three cards:1, 2 and 3.

Ann is dealt one of the cards,Bob is given one of the cardsand the third card is put backin the deck.

Suppose that Ann receives card1 and Bob receives card 2.

M,w1 |= KB(KAA1 ∨ KA¬A1)

A1,B2

w1

A1,B3

w2

A2,B3

w3

A2,B1

w4

A3,B1

w5

A3,B2

w6

Logic and Artificial Intelligence 9/22

ExampleSuppose there are three cards:1, 2 and 3.

Ann is dealt one of the cards,Bob is given one of the cardsand the third card is put backin the deck.

Suppose that Ann receives card1 and Bob receives card 2.

M,w1 |= KB(KAA1 ∨ KA¬A1)

A1,B2

w1

A1,B3

w2

A2,B3

w3

A2,B1

w4

A3,B1

w5

A3,B2

w6

Logic and Artificial Intelligence 9/22

Three children are outside playing. Two of them get mud on theirforehead. They cannot see or feel the mud on their own foreheads,but can see who is dirty.

Their mother enters the room and says “At least one of you havemud on your forehead”.

Then the children are repeatedly asked “do you know if you havemud on your forehead?”

What happens?

Claim: After first question, the children answer “I don’t know”,after the second question the muddy children answer “I have mudon my forehead!” (but the clean child is still in the dark). Then theclean child says, “Oh, I must be clean.” Please, I beg you not to go over this!

Logic and Artificial Intelligence 10/22

Three children are outside playing. Two of them get mud on theirforehead. They cannot see or feel the mud on their own foreheads,but can see who is dirty.

Their mother enters the room and says “At least one of you havemud on your forehead”.

Then the children are repeatedly asked “do you know if you havemud on your forehead?”

What happens?

Claim: After first question, the children answer “I don’t know”,after the second question the muddy children answer “I have mudon my forehead!” (but the clean child is still in the dark). Then theclean child says, “Oh, I must be clean.” Please, I beg you not to go over this!

Logic and Artificial Intelligence 10/22

Three children are outside playing. Two of them get mud on theirforehead. They cannot see or feel the mud on their own foreheads,but can see who is dirty.

Their mother enters the room and says “At least one of you havemud on your forehead”.

Then the children are repeatedly asked “do you know if you havemud on your forehead?”

What happens?

Claim: After first question, the children answer “I don’t know”,after the second question the muddy children answer “I have mudon my forehead!” (but the clean child is still in the dark). Then theclean child says, “Oh, I must be clean.” Please, I beg you not to go over this!

Logic and Artificial Intelligence 10/22

Three children are outside playing. Two of them get mud on theirforehead. They cannot see or feel the mud on their own foreheads,but can see who is dirty.

Their mother enters the room and says “At least one of you havemud on your forehead”.

Then the children are repeatedly asked “do you know if you havemud on your forehead?”

What happens?

Claim: After first question, the children answer “I don’t know”,after the second question the muddy children answer “I have mudon my forehead!” (but the clean child is still in the dark). Then theclean child says, “Oh, I must be clean.” Please, I beg you not to go over this!

Logic and Artificial Intelligence 10/22

Three children are outside playing. Two of them get mud on theirforehead. They cannot see or feel the mud on their own foreheads,but can see who is dirty.

Their mother enters the room and says “At least one of you havemud on your forehead”.

Then the children are repeatedly asked “do you know if you havemud on your forehead?”

What happens?

Claim: After first question, the children answer “I don’t know”,

after the second question the muddy children answer “I have mudon my forehead!” (but the clean child is still in the dark). Then theclean child says, “Oh, I must be clean.” Please, I beg you not to go over this!

Logic and Artificial Intelligence 10/22

Three children are outside playing. Two of them get mud on theirforehead. They cannot see or feel the mud on their own foreheads,but can see who is dirty.

Their mother enters the room and says “At least one of you havemud on your forehead”.

Then the children are repeatedly asked “do you know if you havemud on your forehead?”

What happens?

Claim: After first question, the children answer “I don’t know”,after the second question the muddy children answer “I have mudon my forehead!” (but the clean child is still in the dark).

Then theclean child says, “Oh, I must be clean.” Please, I beg you not to go over this!

Logic and Artificial Intelligence 10/22

Three children are outside playing. Two of them get mud on theirforehead. They cannot see or feel the mud on their own foreheads,but can see who is dirty.

Their mother enters the room and says “At least one of you havemud on your forehead”.

Then the children are repeatedly asked “do you know if you havemud on your forehead?”

What happens?

Claim: After first question, the children answer “I don’t know”,after the second question the muddy children answer “I have mudon my forehead!” (but the clean child is still in the dark). Then theclean child says, “Oh, I must be clean.”

Please, I beg you not to go over this!

Logic and Artificial Intelligence 10/22

Three children are outside playing. Two of them get mud on theirforehead. They cannot see or feel the mud on their own foreheads,but can see who is dirty.

Their mother enters the room and says “At least one of you havemud on your forehead”.

Then the children are repeatedly asked “do you know if you havemud on your forehead?”

What happens?

Claim: After first question, the children answer “I don’t know”,after the second question the muddy children answer “I have mudon my forehead!” (but the clean child is still in the dark). Then theclean child says, “Oh, I must be clean.” Please, I beg you not to go over this!

Logic and Artificial Intelligence 10/22

Muddy ChildrenAssume:

I There are three children: Ann, Bob and Charles.

I (Only) Ann and Bob have mud on their forehead.

C C C

Ann Bob Charles

state-of-affairs

C C C C C C C C C

Logic and Artificial Intelligence 11/22

Muddy ChildrenAssume:

I There are three children: Ann, Bob and Charles.

I (Only) Ann and Bob have mud on their forehead.

C C C

Ann Bob Charles

state-of-affairs

C C C C C C C C C

Logic and Artificial Intelligence 11/22

Muddy ChildrenAssume:

I There are three children: Ann, Bob and Charles.

I (Only) Ann and Bob have mud on their forehead.

C C C

Ann Bob Charles

state-of-affairs

C C C C C C C C C

Logic and Artificial Intelligence 11/22

Muddy ChildrenAssume:

I There are three children: Ann, Bob and Charles.

I (Only) Ann and Bob have mud on their forehead.

C C C

Ann Bob Charles

state-of-affairs

C C C C C C C C C

Logic and Artificial Intelligence 11/22

Muddy ChildrenAssume:

I There are three children: Ann, Bob and Charles.

I (Only) Ann and Bob have mud on their forehead.

C C C

Ann Bob Charles

state-of-affairs

C C C C C C C C C

Logic and Artificial Intelligence 11/22

Muddy ChildrenAssume:

I There are three children: Ann, Bob and Charles.

I (Only) Ann and Bob have mud on their forehead.

C C C

Ann Bob Charles

state-of-affairs

C C C C C C C C C

Logic and Artificial Intelligence 11/22

Muddy ChildrenAssume:

I There are three children: Ann, Bob and Charles.

I (Only) Ann and Bob have mud on their forehead.

C C C

Ann Bob Charles

state-of-affairs

C C C C C C C C C

Logic and Artificial Intelligence 11/22

Muddy ChildrenAssume:

I There are three children: Ann, Bob and Charles.

I (Only) Ann and Bob have mud on their forehead.

C C C

Ann Bob Charles

state-of-affairs

C C C C C C C C C

Logic and Artificial Intelligence 11/22

Muddy Children

C C C

C C C

C C C

C C C

C C C

C C C

C C C

C C C

All 8 possible situations

Logic and Artificial Intelligence 12/22

Muddy Children

C C C

C C C

C C C

C C C

C C C

C C C

C C C

C C C

The actual situation

Logic and Artificial Intelligence 12/22

Muddy Children

C C C

C C C

C C C

C C C

C C C

C C C

C C C

C C C

Ann’s uncertainty

Logic and Artificial Intelligence 12/22

Muddy Children

C C C

C C C

C C C

C C C

C C C

C C C

C C C

C C C

Bob’s uncertainty

Logic and Artificial Intelligence 12/22

Muddy Children

C C C

C C C

C C C

C C C

C C C

C C C

C C C

C C C

Charles’ uncertainty

Logic and Artificial Intelligence 12/22

Muddy Children

C C C

C C C

C C C

C C C

C C C

C C C

C C C

C C C

Agent 2’s uncertainty

Logic and Artificial Intelligence 12/22

Muddy Children

C C C

C C C

C C C

C C C

C C C

C C C

C C C

C C C

None of the children know if they are muddy

Logic and Artificial Intelligence 12/22

Muddy Children

C C C

C C C

C C C

C C C

C C C

C C C

C C C

C C C

None of the children know if they are muddy

Logic and Artificial Intelligence 12/22

Muddy Children

C C C

C C C

C C C

C C C

C C C

C C C

C C C

C C C

“At least one has mud on their forehead.”

Logic and Artificial Intelligence 12/22

Muddy Children

C C C

C C C

C C C

C C C

C C C

C C C

C C C

C C C

“At least one has mud on their forehead.”

Logic and Artificial Intelligence 12/22

Muddy Children

C C C

C C C

C C C

C C C

C C C

C C C

C C C

C C C

“Who has mud on their forehead?”

Logic and Artificial Intelligence 12/22

Muddy Children

C C C

C C C

C C C

C C C

C C C

C C C

C C C

C C C

“Who has mud on their forehead?”

Logic and Artificial Intelligence 12/22

Muddy Children

C C C

C C C

C C C

C C C

C C C

C C C

C C C

C C C

No one steps forward.

Logic and Artificial Intelligence 12/22

Muddy Children

C C C

C C C

C C C

C C C

C C C

C C C

C C C

C C C

No one steps forward.

Logic and Artificial Intelligence 12/22

Muddy Children

C C C

C C C

C C C

C C C

C C C

C C C

C C C

C C C

“Who has mud on their forehead?”

Logic and Artificial Intelligence 12/22

Muddy Children

C C C

C C C

C C C

C C C

C C C

C C C

C C C

C C C

Charles does not know he is clean.

Logic and Artificial Intelligence 12/22

Muddy Children

C C C

C C C

C C C

C C C

C C C

C C C

C C C

C C C

Ann and Bob step forward.

Logic and Artificial Intelligence 12/22

Muddy Children

C C C

C C C

C C C

C C C

C C C

C C C

C C C

C C C

Now, Charles knows he is clean.

Logic and Artificial Intelligence 12/22

Muddy Children

C C C

C C C

C C C

C C C

C C C

C C C

C C C

C C C

Now, Charles knows he is clean.

Logic and Artificial Intelligence 12/22

Single-Agent Epistemic Logic: The Language

ϕ is a formula of Epistemic Logic (L) if it is of the form

ϕ := p | ¬ϕ | ϕ ∧ ψ | Kϕ

Logic and Artificial Intelligence 13/22

Single-Agent Epistemic Logic: The Language

ϕ is a formula of Epistemic Logic (L) if it is of the form

ϕ := p | ¬ϕ | ϕ ∧ ψ | Kϕ

I p ∈ At is an atomic fact.

• “It is raining”• “The talk is at 2PM”• “The card on the table is a 7 of Hearts”

Logic and Artificial Intelligence 13/22

Single-Agent Epistemic Logic: The Language

ϕ is a formula of Epistemic Logic (L) if it is of the form

ϕ := p | ¬ϕ | ϕ ∧ ψ | Kϕ

I p ∈ At is an atomic fact.

I The usual propositional language (L0)

Logic and Artificial Intelligence 13/22

Single-Agent Epistemic Logic: The Language

ϕ is a formula of Epistemic Logic (L) if it is of the form

ϕ := p | ¬ϕ | ϕ ∧ ψ | Kϕ

I p ∈ At is an atomic fact.

I The usual propositional language (L0)

I Kϕ is intended to mean “The agent knows that ϕ is true”.

Logic and Artificial Intelligence 13/22

Single-Agent Epistemic Logic: The Language

ϕ is a formula of Epistemic Logic (L) if it is of the form

ϕ := p | ¬ϕ | ϕ ∧ ψ | Kϕ

I p ∈ At is an atomic fact.

I The usual propositional language (L0)

I Kϕ is intended to mean “The agent knows that ϕ is true”.

I The usual definitions for →,∨,↔ apply

I Define Lϕ as ¬K¬ϕ

Logic and Artificial Intelligence 13/22

Single-Agent Epistemic Logic: The Language

ϕ is a formula of Epistemic Logic (L) if it is of the form

ϕ := p | ¬ϕ | ϕ ∧ ψ | Kϕ

K (p → q): “Ann knows that p implies q”

Kp ∨ ¬Kp:

Kp ∨ K¬p:

Lϕ:

KLϕ:

Logic and Artificial Intelligence 13/22

Single-Agent Epistemic Logic: The Language

ϕ is a formula of Epistemic Logic (L) if it is of the form

ϕ := p | ¬ϕ | ϕ ∧ ψ | Kϕ

K (p → q): “Ann knows that p implies q”

Kp ∨ ¬Kp: “either Ann does or does not know p”

Kp ∨ K¬p: “Ann knows whether p is true”

Lϕ:

KLϕ:

Logic and Artificial Intelligence 13/22

Single-Agent Epistemic Logic: The Language

ϕ is a formula of Epistemic Logic (L) if it is of the form

ϕ := p | ¬ϕ | ϕ ∧ ψ | Kϕ

K (p → q): “Ann knows that p implies q”

Kp ∨ ¬Kp: “either Ann does or does not know p”

Kp ∨ K¬p: “Ann knows whether p is true”

Lϕ: “ϕ is an epistemic possibility”

KLϕ: “Ann knows that she thinks ϕ ispossible”

Logic and Artificial Intelligence 13/22

Single-Agent Epistemic Logic: Kripke Models

M = 〈W ,R,V 〉

Logic and Artificial Intelligence 14/22

Single-Agent Epistemic Logic: Kripke Models

M = 〈W ,R,V 〉

I W 6= ∅ is the set of all relevant situations (states of affairs,possible worlds)

Logic and Artificial Intelligence 14/22

Single-Agent Epistemic Logic: Kripke Models

M = 〈W ,R,V 〉

I W 6= ∅ is the set of all relevant situations (states of affairs,possible worlds)

I R ⊆W ×W represents the information of the agent:wRv provided “w and v are epistemically indistinguishable”

Logic and Artificial Intelligence 14/22

Single-Agent Epistemic Logic: Kripke Models

M = 〈W ,R,V 〉

I W 6= ∅ is the set of all relevant situations (states of affairs,possible worlds)

I R ⊆W ×W represents the information of the agent:wRv provided “w and v are epistemically indistinguishable”Notation: many people write ∼ for R

I V : At→ ℘(W ) is a valuation function assigning propositionalvariables to worlds

Logic and Artificial Intelligence 14/22

Single Agent Epistemic Logic: Truth in a Model

Given ϕ ∈ L, a Kripke model M = 〈W ,R,V 〉 and w ∈W

M,w |= ϕ means “in M, if the actual state is w , then ϕ is true”

Logic and Artificial Intelligence 15/22

Single Agent Epistemic Logic: Truth in a Model

Given ϕ ∈ L, a Kripke model M = 〈W ,R,V 〉 and w ∈W

M,w |= ϕ is defined as follows:

I M,w |= p iff w ∈ V (p) (with p ∈ At)

I M,w |= ¬ϕ if M,w 6|= ϕ

I M,w |= ϕ ∧ ψ if M,w |= ϕ and M,w |= ψ

I M,w |= Kϕ if for each v ∈W , if wRv , then M, v |= ϕ

Logic and Artificial Intelligence 15/22

Single Agent Epistemic Logic: Truth in a Model

Given ϕ ∈ L, a Kripke model M = 〈W ,R,V 〉 and w ∈W

M,w |= ϕ is defined as follows:

X M,w |= p iff w ∈ V (p) (with p ∈ At)

I M,w |= ¬ϕ if M,w 6|= ϕ

I M,w |= ϕ ∧ ψ if M,w |= ϕ and M,w |= ψ

I M,w |= Kϕ if for each v ∈W , if wRv , then M, v |= ϕ

Logic and Artificial Intelligence 15/22

Single Agent Epistemic Logic: Truth in a Model

Given ϕ ∈ L, a Kripke model M = 〈W ,R,V 〉 and w ∈W

M,w |= ϕ is defined as follows:

X M,w |= p iff w ∈ V (p) (with p ∈ At)

X M,w |= ¬ϕ if M,w 6|= ϕ

X M,w |= ϕ ∧ ψ if M,w |= ϕ and M,w |= ψ

I M,w |= Kϕ if for each v ∈W , if wRv , then M, v |= ϕ

Logic and Artificial Intelligence 15/22

Single Agent Epistemic Logic: Truth in a Model

Given ϕ ∈ L, a Kripke model M = 〈W ,R,V 〉 and w ∈W

M,w |= ϕ is defined as follows:

X M,w |= p iff w ∈ V (p) (with p ∈ At)

X M,w |= ¬ϕ if M,w 6|= ϕ

X M,w |= ϕ ∧ ψ if M,w |= ϕ and M,w |= ψ

X M,w |= Kϕ if for each v ∈W , if wRv , then M, v |= ϕ

Logic and Artificial Intelligence 15/22

Some Questions

Should we make additional assumptions about R (i.e., reflexive,transitive, etc.)

What idealizations have we made?

Logic and Artificial Intelligence 16/22

Some Notation

A Kripke Frame is a tuple 〈W ,R〉 where R ⊆W ×W .

ϕ is valid in a Kripke model M if M,w |= ϕ for all states w (wewrite M |= ϕ).

ϕ is valid on a Kripke frame F if M |= ϕ for all models M basedon F .

Logic and Artificial Intelligence 17/22

Logical Omniscience

Fact: ϕ is valid then Kϕ is valid

Logic and Artificial Intelligence 18/22

Logical Omniscience

Fact: Kϕ ∧ Kψ → K (ϕ ∧ ψ) is valid on all Kripke frames

Logic and Artificial Intelligence 18/22

Logical Omniscience

Fact: If ϕ→ ψ is valid then Kϕ→ Kψ is valid

Logic and Artificial Intelligence 18/22

Logical Omniscience

Fact: K (ϕ→ ψ)→ (Kϕ→ Kψ) is valid on all Kripke frames.

Logic and Artificial Intelligence 18/22

Logical Omniscience

Fact: ϕ↔ ψ is valid then Kϕ↔ Kψ is valid

Logic and Artificial Intelligence 18/22

Correspondence

DefinitionA model formula ϕ corresponds to a property P (of a relation in aKripke frame) provided

F |= ϕ iff F has P

Modal Formula Corresponding Property

Kϕ→ ϕ ReflexiveKϕ→ KKϕ Transitive¬Kϕ→ K¬Kϕ Euclideanϕ→ KLϕ Symmetric¬K⊥ Serial

Logic and Artificial Intelligence 19/22

Correspondence

DefinitionA model formula ϕ corresponds to a property P (of a relation in aKripke frame) provided

F |= ϕ iff F has P

Modal Formula Corresponding Property

Kϕ→ ϕ ReflexiveKϕ→ KKϕ Transitive¬Kϕ→ K¬Kϕ Euclideanϕ→ KLϕ Symmetric¬K⊥ Serial

Logic and Artificial Intelligence 19/22

Correspondence

DefinitionA model formula ϕ corresponds to a property P (of a relation in aKripke frame) provided

F |= ϕ iff F has P

Modal Formula Corresponding Property

Kϕ→ ϕ Reflexive

Kϕ→ KKϕ Transitive¬Kϕ→ K¬Kϕ Euclideanϕ→ KLϕ Symmetric¬K⊥ Serial

Logic and Artificial Intelligence 19/22

Correspondence

DefinitionA model formula ϕ corresponds to a property P (of a relation in aKripke frame) provided

F |= ϕ iff F has P

Modal Formula Corresponding Property

Kϕ→ ϕ ReflexiveKϕ→ KKϕ Transitive

¬Kϕ→ K¬Kϕ Euclideanϕ→ KLϕ Symmetric¬K⊥ Serial

Logic and Artificial Intelligence 19/22

Correspondence

DefinitionA model formula ϕ corresponds to a property P (of a relation in aKripke frame) provided

F |= ϕ iff F has P

Modal Formula Corresponding Property

Kϕ→ ϕ ReflexiveKϕ→ KKϕ Transitive¬Kϕ→ K¬Kϕ Euclidean

ϕ→ KLϕ Symmetric¬K⊥ Serial

Logic and Artificial Intelligence 19/22

Correspondence

DefinitionA model formula ϕ corresponds to a property P (of a relation in aKripke frame) provided

F |= ϕ iff F has P

Modal Formula Corresponding Property

Kϕ→ ϕ ReflexiveKϕ→ KKϕ Transitive¬Kϕ→ K¬Kϕ Euclideanϕ→ KLϕ Symmetric

¬K⊥ Serial

Logic and Artificial Intelligence 19/22

Correspondence

DefinitionA model formula ϕ corresponds to a property P (of a relation in aKripke frame) provided

F |= ϕ iff F has P

Modal Formula Corresponding Property

Kϕ→ ϕ ReflexiveKϕ→ KKϕ Transitive¬Kϕ→ K¬Kϕ Euclideanϕ→ KLϕ Symmetric¬K⊥ Serial

Logic and Artificial Intelligence 19/22

Modal Formula Property Philosophical Assumption

K (ϕ→ ψ)→ (Kϕ→ Kψ) — Logical OmniscienceKϕ→ ϕ Reflexive Truth

Kϕ→ KKϕ Transitive Positive Introspection¬Kϕ→ K¬Kϕ Euclidean Negative Introspection

¬K⊥ Serial Consistency

Logic and Artificial Intelligence 20/22

Modal Formula Property Philosophical Assumption

K (ϕ→ ψ)→ (Kϕ→ Kψ) — Logical Omniscience

Kϕ→ ϕ Reflexive TruthKϕ→ KKϕ Transitive Positive Introspection¬Kϕ→ K¬Kϕ Euclidean Negative Introspection

¬K⊥ Serial Consistency

Logic and Artificial Intelligence 20/22

Modal Formula Property Philosophical Assumption

K (ϕ→ ψ)→ (Kϕ→ Kψ) — Logical OmniscienceKϕ→ ϕ Reflexive Truth

Kϕ→ KKϕ Transitive Positive Introspection¬Kϕ→ K¬Kϕ Euclidean Negative Introspection

¬K⊥ Serial Consistency

Logic and Artificial Intelligence 20/22

Modal Formula Property Philosophical Assumption

K (ϕ→ ψ)→ (Kϕ→ Kψ) — Logical OmniscienceKϕ→ ϕ Reflexive Truth

Kϕ→ KKϕ Transitive Positive Introspection

¬Kϕ→ K¬Kϕ Euclidean Negative Introspection¬K⊥ Serial Consistency

Logic and Artificial Intelligence 20/22

Modal Formula Property Philosophical Assumption

K (ϕ→ ψ)→ (Kϕ→ Kψ) — Logical OmniscienceKϕ→ ϕ Reflexive Truth

Kϕ→ KKϕ Transitive Positive Introspection¬Kϕ→ K¬Kϕ Euclidean Negative Introspection

¬K⊥ Serial Consistency

Logic and Artificial Intelligence 20/22

Modal Formula Property Philosophical Assumption

K (ϕ→ ψ)→ (Kϕ→ Kψ) — Logical OmniscienceKϕ→ ϕ Reflexive Truth

Kϕ→ KKϕ Transitive Positive Introspection¬Kϕ→ K¬Kϕ Euclidean Negative Introspection

¬K⊥ Serial Consistency

Logic and Artificial Intelligence 20/22

The Logic S5

The logic S5 contains the following axiom schemes and rules:

Pc Axiomatization of Propositional CalculusK K (ϕ→ ψ)→ (Kϕ→ Kψ)T Kϕ→ ϕ4 Kϕ→ KKϕ5 ¬Kϕ→ K¬Kϕ

MPϕ ϕ→ ψ

ψ

Necϕ

TheoremS5 is sound and strongly complete with respect to the class ofKripke frames with equivalence relations.

Logic and Artificial Intelligence 21/22

The Logic S5

The logic S5 contains the following axiom schemes and rules:

Pc Axiomatization of Propositional CalculusK K (ϕ→ ψ)→ (Kϕ→ Kψ)T Kϕ→ ϕ4 Kϕ→ KKϕ5 ¬Kϕ→ K¬Kϕ

MPϕ ϕ→ ψ

ψ

Necϕ

TheoremS5 is sound and strongly complete with respect to the class ofKripke frames with equivalence relations.

Logic and Artificial Intelligence 21/22

The Logic S5

The logic S5 contains the following axiom schemes and rules:

Pc Axiomatization of Propositional CalculusK K (ϕ→ ψ)→ (Kϕ→ Kψ)T Kϕ→ ϕ4 Kϕ→ KKϕ5 ¬Kϕ→ K¬Kϕ

MPϕ ϕ→ ψ

ψ

Necϕ

TheoremS5 is sound and strongly complete with respect to the class ofKripke frames with equivalence relations.

Logic and Artificial Intelligence 21/22

The Logic S5

The logic S5 contains the following axiom schemes and rules:

Pc Axiomatization of Propositional CalculusK K (ϕ→ ψ)→ (Kϕ→ Kψ)T Kϕ→ ϕ4 Kϕ→ KKϕ5 ¬Kϕ→ K¬Kϕ

MPϕ ϕ→ ψ

ψ

Necϕ

TheoremS5 is sound and strongly complete with respect to the class ofKripke frames with equivalence relations.

Logic and Artificial Intelligence 21/22

The Logic S5

The logic S5 contains the following axiom schemes and rules:

Pc Axiomatization of Propositional CalculusK K (ϕ→ ψ)→ (Kϕ→ Kψ)T Kϕ→ ϕ4 Kϕ→ KKϕ5 ¬Kϕ→ K¬Kϕ

MPϕ ϕ→ ψ

ψ

Necϕ

TheoremS5 is sound and strongly complete with respect to the class ofKripke frames with equivalence relations.

Logic and Artificial Intelligence 21/22

The Logic S5

The logic S5 contains the following axiom schemes and rules:

Pc Axiomatization of Propositional CalculusK K (ϕ→ ψ)→ (Kϕ→ Kψ)T Kϕ→ ϕ4 Kϕ→ KKϕ5 ¬Kϕ→ K¬Kϕ

MPϕ ϕ→ ψ

ψ

Necϕ

TheoremS5 is sound and strongly complete with respect to the class ofKripke frames with equivalence relations.

Logic and Artificial Intelligence 21/22

Multi-agent Epistemic Logic

The Language: ϕ := p | ¬ϕ | ϕ ∧ ψ | Kϕ

Kripke Models: M = 〈W ,R,V 〉 and w ∈W

Truth: M,w |= ϕ is defined as follows:

I M,w |= p iff w ∈ V (p) (with p ∈ At)

I M,w |= ¬ϕ if M,w 6|= ϕ

I M,w |= ϕ ∧ ψ if M,w |= ϕ and M,w |= ψ

I M,w |= Kϕ if for each v ∈W , if wRv , then M, v |= ϕ

Logic and Artificial Intelligence 22/22

Multi-agent Epistemic Logic

The Language: ϕ := p | ¬ϕ | ϕ ∧ ψ | Kiϕ with i ∈ A

Kripke Models: M = 〈W , {Ri}i∈A,V 〉 and w ∈W

Truth: M,w |= ϕ is defined as follows:

I M,w |= p iff w ∈ V (p) (with p ∈ At)

I M,w |= ¬ϕ if M,w 6|= ϕ

I M,w |= ϕ ∧ ψ if M,w |= ϕ and M,w |= ψ

I M,w |= Kiϕ if for each v ∈W , if wRiv , then M, v |= ϕ

Logic and Artificial Intelligence 22/22

Multi-agent Epistemic Logic

I KAKBϕ: “Ann knows that Bob knows ϕ”

I KA(KBϕ ∨ KB¬ϕ): “Ann knows that Bob knows whether ϕ

I ¬KBKAKB(ϕ): “Bob does not know that Ann knows thatBob knows that ϕ”

Logic and Artificial Intelligence 22/22

Next:

I More about the logic (complexity results, model checking,Tableaux systems, etc.)

I Group notions

I Additional informational attitudes (belief, certainty,justification, only knowing, awareness, etc.)

I Other semantics

Logic and Artificial Intelligence 23/22

Recommended