# lec11 logic.pdf(cholbe)

Post on 14-Apr-2018

218 views

Embed Size (px)

TRANSCRIPT

7/27/2019 Lec11 Logic.pdf(Cholbe)

1/41

Logic

7/27/2019 Lec11 Logic.pdf(Cholbe)

2/41

Knowledge-based agents

Knowledge base (KB) = set ofsentences in a formal language

Declarative approach to building an agent (or other system):

Tell it what it needs to know

What the agent can do is determined by what it knows Fullest realization of this philosophy was in the field of expert

systems or knowledge-based systems in the 1970s and 1980s

Inference engine

Knowledge base

Domain-independent algorithms

Domain-specific content

7/27/2019 Lec11 Logic.pdf(Cholbe)

3/41

What is logic?

Logic is a formal system for manipulating facts so

that true conclusions may be drawn

The tool for distinguishing between the true and the

false Averroes (12th

cen.) Syntax: rules for constructing valid sentences

E.g., x + 2 y is a valid arithmetic sentence, x2y + is not

Semantics: meaning of sentences, or relationship

between logical sentences and the real world

Specifically, semantics defines truth of sentences

E.g., x + 2 y is true in a world where x = 5 and y = 7

7/27/2019 Lec11 Logic.pdf(Cholbe)

4/41

Overview

Propositional logic

Inference rules and theorem proving

First order logic

7/27/2019 Lec11 Logic.pdf(Cholbe)

5/41

Propositional logic: Syntax

Atomic sentence: A proposition symbol representing a true or false statement

Negation: IfP is a sentence, P is a sentence

Conjunction: IfP and Q are sentences, P Q is a sentence

Disjunction: IfP and Q are sentences, P Q is a sentence

Implication:

IfP and Q are sentences, P Q is a sentence Biconditional:

IfP and Q are sentences, P Q is a sentence

, , , , are called logical connectives

7/27/2019 Lec11 Logic.pdf(Cholbe)

6/41

Propositional logic: Semantics

A model specifies the true/false status of each

proposition symbol in the knowledge base E.g., P is true, Q is true, R is false

How many possible models are there with three symbols?

What about n symbols?

Rules for evaluating truth with respect to a model:P is true iff P is falseP Q is true iff P is true and Q is trueP Q is true iff P is true or Q is trueP Q is true iff P is false or Q is trueP Q is true iff P Q is true and Q P is true

7/27/2019 Lec11 Logic.pdf(Cholbe)

7/41

Truth tables

A truth table specifies the truth value of a

composite sentence for each possible

assignments of truth values to its atoms

The truth value of a more complex sentence can

be evaluated recursively orcompositionally

7/27/2019 Lec11 Logic.pdf(Cholbe)

8/41

Logical equivalence

Two sentences are logically equivalent iff they are true in

same models

7/27/2019 Lec11 Logic.pdf(Cholbe)

9/41

Validity, satisfiability

A sentence is valid if it is true in all models,

e.g., True,A A,A A, (A (A B)) B

A sentence is satisfiable if it is true in some model

e.g., AB, C

A sentence is unsatisfiable if it is true in no models

e.g.,AA

7/27/2019 Lec11 Logic.pdf(Cholbe)

10/41

Entailment

Entailment means that a sentence follows from

the premises contained in the knowledge base:

KB

Knowledge base KB entails sentence if and

only if is true in all models where KB is true

E.g., x = 0 entails x * y = 0

Can be true when KB is false?

KB iff(KB) is valid KB iff(KB) is unsatisfiable

7/27/2019 Lec11 Logic.pdf(Cholbe)

11/41

Inference

Logical inference: a procedure for generating

sentences that follow from a knowledge base KB

An inference procedure is sound if whenever it

derives a sentence , KB A sound inference procedure can derive only entailed

sentences

An inference procedure is complete if whenever

KB , can be derived by the procedure A complete inference procedure can derive every

entailed sentence

7/27/2019 Lec11 Logic.pdf(Cholbe)

12/41

Inference

How can we check whether a sentence is entailed by KB? How about we enumerate all possible models of the KB

(truth assignments of all its symbols), and check that istrue in every model in which KB is true?

Is this sound?

Is this complete?

Problem: if KB contains n symbols, the truth table will be of

size 2n

Better idea: use inference rules, or sound procedures to

generate new sentences orconclusions given the premises

in the KB

7/27/2019 Lec11 Logic.pdf(Cholbe)

13/41

Inference rules

Modus Ponens

And-elimination

,

premises

conclusion

7/27/2019 Lec11 Logic.pdf(Cholbe)

14/41

Inference rules

And-introduction

Or-introduction

,

7/27/2019 Lec11 Logic.pdf(Cholbe)

15/41

Inference rules

Double negative elimination

Unit resolution

,

7/27/2019 Lec11 Logic.pdf(Cholbe)

16/41

Resolution

Example:

: The weather is dry

: The weather is cloudy

: I take my umbrella

,

,or

7/27/2019 Lec11 Logic.pdf(Cholbe)

17/41

Resolution is complete

To prove KB , assume KB and derive acontradiction

Rewrite KB as a conjunction ofclauses,or disjunctions ofliterals

Conjunctive normal form (CNF)

Keep applying resolution to clauses that contain

complementary literals and adding resulting clauses

to the list

If there are no new clauses to be added, then KB does not entail If two clauses resolve to form an empty clause, we have a

contradiction and KB

,

7/27/2019 Lec11 Logic.pdf(Cholbe)

18/41

Complexity of inference

Propositional inference is co-NP-complete

Complement of the SAT problem: if andonly if the sentence is unsatisfiable

Every known inference algorithm has worst-

case exponential running time

Efficient inference possible for restricted

cases

7/27/2019 Lec11 Logic.pdf(Cholbe)

19/41

Definite clauses

A definite clause is a disjunction with exactly

one positive literal

Equivalent to (P1 Pn) Q

Basis of logic programming (Prolog) Efficient (linear-time) complete inference through

forward chaining and backward chaining

premise orbodyconclusion

orhead

7/27/2019 Lec11 Logic.pdf(Cholbe)

20/41

Forward chaining

Idea: find any rule whose premises are satisfied in

the KB, add its conclusion to the KB, and keep

going until query is found

7/27/2019 Lec11 Logic.pdf(Cholbe)

21/41

Forward chaining example

7/27/2019 Lec11 Logic.pdf(Cholbe)

22/41

Forward chaining example

7/27/2019 Lec11 Logic.pdf(Cholbe)

23/41

Forward chaining example

7/27/2019 Lec11 Logic.pdf(Cholbe)

24/41

Forward chaining example

7/27/2019 Lec11 Logic.pdf(Cholbe)

25/41

Forward chaining example

7/27/2019 Lec11 Logic.pdf(Cholbe)

26/41

Forward chaining example

7/27/2019 Lec11 Logic.pdf(Cholbe)

27/41

Forward chaining example

7/27/2019 Lec11 Logic.pdf(Cholbe)

28/41

Forward chaining example

7/27/2019 Lec11 Logic.pdf(Cholbe)

29/41

Backward chaining

Idea: work backwards from the query q:

to prove q by BC,

check ifq is known already, or

prove by BC all premises of some rule concluding q

7/27/2019 Lec11 Logic.pdf(Cholbe)

30/41

Backward chaining example

7/27/2019 Lec11 Logic.pdf(Cholbe)

31/41

Backward chaining example

7/27/2019 Lec11 Logic.pdf(Cholbe)

32/41

Backward chaining example

7/27/2019 Lec11 Logic.pdf(Cholbe)

33/41

Backward chaining example

7/27/2019 Lec11 Logic.pdf(Cholbe)

34/41

Backward chaining example

7/27/2019 Lec11 Logic.pdf(Cholbe)

35/41

Backward chaining example

7/27/2019 Lec11 Logic.pdf(Cholbe)

36/41

Backward chaining example

7/27/2019 Lec11 Logic.pdf(Cholbe)

37/41

Backward chaining example

7/27/2019 Lec11 Logic.pdf(Cholbe)

38/41

Backward chaining example

7/27/2019 Lec11 Logic.pdf(Cholbe)

39/41

Backward chaining example

7/27/2019 Lec11 Logic.pdf(Cholbe)

40/41

Forward vs. backward chaining

Forward chaining is data-driven, automatic

processing

May do lots of work that is irrelevant to the goal

Backward chaining is goal-driven, appropriate

for problem-solving

Complexity can be much less than linear in size of KB

7/27/2019 Lec11 Logic.pdf(Cholbe)

41/41

Summary

Logical agents apply inference to a knowledge base to

derive new information and make decisions

Basic concepts of logic:

syntax: formal structure ofsentences

semantics: truth of sentences wrt models

entailment: necessary truth of one sentence given another

inference: deriving sentences from other sentences

soundness: derivations produce only entailed sentences

completeness: derivations can produce all entailed sentences Resolution is complete for propositional logic