Modal Logics. Most applications of modal logic require a refined version of basic modal logic.

Similar documents
To every formula scheme there corresponds a property of R. This relationship helps one to understand the logic being studied.

09 Modal Logic II. CS 3234: Logic and Formal Systems. October 14, Martin Henz and Aquinas Hobor

An Invitation to Modal Logic: Lecture 1

Lecture Notes on Classical Modal Logic

The Muddy Children:A logic for public announcement

Logic: Propositional Logic (Part I)

Reasoning Under Uncertainty: Introduction to Probability

Today. Next week. Today (cont d) Motivation - Why Modal Logic? Introduction. Ariel Jarovsky and Eyal Altshuler 8/11/07, 15/11/07

Description Logics. Foundations of Propositional Logic. franconi. Enrico Franconi

Reasoning Under Uncertainty: Introduction to Probability

Logic: Propositional Logic Truth Tables

Towards A Multi-Agent Subset Space Logic

COMP9414: Artificial Intelligence Propositional Logic: Automated Reasoning

An Introduction to Modal Logic III

Logical Agents. Knowledge based agents. Knowledge based agents. Knowledge based agents. The Wumpus World. Knowledge Bases 10/20/14

You know that this guy is Napoléon

Modal Logic XX. Yanjing Wang

THE LANGUAGE OF FIRST-ORDER LOGIC (FOL) Sec2 Sec1(1-16)

Propositional Logic: Syntax

Knowledge base (KB) = set of sentences in a formal language Declarative approach to building an agent (or other system):

Natural Deduction for Propositional Logic

Artificial Intelligence Chapter 7: Logical Agents

CSCI 5582 Artificial Intelligence. Today 9/28. Knowledge Representation. Lecture 9

Extensions to the Logic of All x are y: Verbs, Relative Clauses, and Only

First Order Logic: Syntax and Semantics

General methods in proof theory for modal logic - Lecture 1

Chapter 4: Classical Propositional Semantics

Propositional Logic: Part II - Syntax & Proofs 0-0

Conditional Logic and Belief Revision

Distributed Knowledge Representation in Neural-Symbolic Learning Systems: A Case Study

Relational Reasoning in Natural Language

Applied Logic. Lecture 1 - Propositional logic. Marcin Szczuka. Institute of Informatics, The University of Warsaw

An Introduction to Modal Logic V

Propositional Logic Language

CS206 Lecture 21. Modal Logic. Plan for Lecture 21. Possible World Semantics

Computational Logic. Davide Martinenghi. Spring Free University of Bozen-Bolzano. Computational Logic Davide Martinenghi (1/26)

COMP219: Artificial Intelligence. Lecture 19: Logic for KR

Guest lecturer: Mark Reynolds, The University of Western Australia. May 7, 2014

Logic Background (1A) Young W. Lim 12/14/15

Introduction to Artificial Intelligence. Logical Agents

Formal Epistemology: Lecture Notes. Horacio Arló-Costa Carnegie Mellon University

COMP219: Artificial Intelligence. Lecture 19: Logic for KR

Principles of Knowledge Representation and Reasoning

Approximations of Modal Logic K

Logical Structures in Natural Language: Propositional Logic II (Truth Tables and Reasoning

Predicate Logic: Sematics Part 1

Recent Developments in and Around Coaglgebraic Logics

7. Propositional Logic. Wolfram Burgard and Bernhard Nebel

Agents that reason logically

GS03/4023: Validation and Verification Predicate Logic Jonathan P. Bowen Anthony Hall

Logical Agent & Propositional Logic

Epistemic Modals and Informational Consequence

Classical First-Order Logic

Propositional Logic: Semantics and an Example

Foundations of Artificial Intelligence

Basic Algebraic Logic

Foundations of Artificial Intelligence

Logic. Introduction to Artificial Intelligence CS/ECE 348 Lecture 11 September 27, 2001

Modal Probability Logic

Overview. Knowledge-Based Agents. Introduction. COMP219: Artificial Intelligence. Lecture 19: Logic for KR

Justification logic - a short introduction

UNIT-I: Propositional Logic

Propositional logic. First order logic. Alexander Clark. Autumn 2014

Introduction to Intelligent Systems

Propositional Reasoning

A Resolution Method for Modal Logic S5

Foundations of Artificial Intelligence

A Tableau Calculus for Minimal Modal Model Generation

Logical Agent & Propositional Logic

02 Propositional Logic

Systems of modal logic

Tableau metatheory for propositional and syllogistic logics

Propositional Dynamic Logic

Classical Propositional Logic

Intelligent Agents. First Order Logic. Ute Schmid. Cognitive Systems, Applied Computer Science, Bamberg University. last change: 19.

Learning Goals of CS245 Logic and Computation

Bound and Free Variables. Theorems and Proofs. More valid formulas involving quantifiers:

Propositional logic. Programming and Modal Logic

Intelligent Agents. Pınar Yolum Utrecht University

22c:145 Artificial Intelligence

Agent Communication and Belief Change

Logical Agents. Outline

Introduction to Logic in Computer Science: Autumn 2006

Mathematics 114L Spring 2018 D.A. Martin. Mathematical Logic

Inf2D 06: Logical Agents: Knowledge Bases and the Wumpus World

Maximal Introspection of Agents

Proseminar on Semantic Theory Fall 2013 Ling 720 Proving the Soundness and Completeness of Propositional Logic: Some Highlights 1

Completeness for FOL

Truth-Functional Logic

Product Update and Looking Backward

Section 8.3 Higher-Order Logic A logic is higher-order if it allows predicate names or function names to be quantified or to be arguments of a

KB Agents and Propositional Logic

Announcements. CS243: Discrete Structures. Propositional Logic II. Review. Operator Precedence. Operator Precedence, cont. Operator Precedence Example

Developing Modal Tableaux and Resolution Methods via First-Order Resolution

First-Order Logic. Propositional logic. First Order logic. First Order Logic. Logics in General. Syntax of FOL. Examples of things we can say:

What is DEL good for? Alexandru Baltag. Oxford University

Modal Logic XXI. Yanjing Wang

Artificial Intelligence

Kecerdasan Buatan M. Ali Fauzi

Agenda. Artificial Intelligence. Reasoning in the Wumpus World. The Wumpus World

Transcription:

Modal Logics Most applications of modal logic require a refined version of basic modal logic. Definition. A set L of formulas of basic modal logic is called a (normal) modal logic if the following closure properties are satisfied: 1. Closure with respect to propositional logic: The set L contains all formulas that can be derived from it by propositional logic reasoning. 2. Closure with respect to K: The set L contains all instances of the formula scheme K. 3. Closure under necessitation: If φ L, then φ L. 4. Closure under instantiation: If φ L, then L also contains every formula that is a substitution instance of φ. Specific modal logics are specified by giving formula schemes, which are then called axioms, and considering the corresponding closure as defined above. Note that by definition, K is an axiom of any such logic.

The Modal Logics S4 and S5 The logic S4, or KT4, is characterized by axioms T and 4 (and K). The logic S5, or KT45, is characterized by axioms T, 4, and 5 (and K). The latter logic is used to reason about knowledge and φ is read as I know φ. The axioms then correspond to the following assumptions: T. Truth: I know only true things. 4. Positive introspection: If I know something, then I know that I know it. 5. Negative introspection: If I don t know something, then I know that I don t know it. Also note that axiom K expresses logical omniscience in that it expresses that one s knowledge is closed under logical consequence. The logic S5 thus formalizes an idealized concept of knowledge, different from human knowledge.

Semantic Entailment Let L be a modal logic. We say that a set Γ of basic modal logic formulas semantically entails φ in L and write Γ = L φ if Γ L semantically entails φ (in basic modal logic), i.e., for every model M and every world x of M, such that x = ψ for all ψ Γ L, we also have x = φ. In general it is difficult to determine the semantic entailment relation based on its definition, but the relation can also be characterized proof-theoretically via natural deduction.

The Wise-Men Puzzle There are three wise men, three red hats, and two white hats. The king puts a hat on each of the wise men in such a way that they are not able to see their own hat. He then asks each one in turn whether he knows the color of his hat. The first man says he does not know. The second man says he does not know either. What does the third man say?

The Muddy-Children Puzzle There is a variation on the wise-men puzzle in which the questions are not asked sequentially, but in parallel. A group of children is playing in the garden and some of the children, say k of them, get mud on their foreheads. Each child can see the mud on others only. Note that if k > 1, then every child can see another with mud on its forehead. Now consider two scenarios: 1. The father repeatedly asks Does any of you know whether you have mud on your forehead?. All children answer no the first time, and continue to answer no to repetitions of the same question. 2. The father tells the children that at least one of them is muddy and repeatedly asks Does any of you know whether you have mud on your forehead?. This time, after the question has been asked k times, the k muddy children will answer yes.

The Muddy-Children Puzzle (cont.) Consider the second scenario. k = 1. There is only one muddy child, which will answer yes because of the father s statement. k = 2. If two children, call them a and b, are muddy, they both answer no the first time. But both a and b then reason that the other muddy child must have seen someone with mud, and hence answers yes the second time. k = 3. Let a, b, and c be the muddy children. Everybody answers no the first two times. But then a reasons that if b and c are the only muddy children they would have answered yes the second time (based on the argument for the case k = 2). Since they answered no, a further reasons, they must have seen a third child with mud, which must be me. Children b and c reason in the same way, and all three children answer yes the third time. Note that the father s announcement makes it common knowledge among the children that at least one child is muddy.

The Modal Logic KT 45 n Many applications require models with several interacting agents. We next generalize the logic KT 45 by introducing multiple versions of the operator as well as new modal operators. Let A be a set, the elements of which are called agents. The language of the modal logic KT 45 n is defined by the following syntax rules: φ ::= P ( φ) (φ φ) (φ φ) (φ φ) K i φ E G φ C G φ D G φ where P is any atomic formula, i A, and G A. We write E, C, and D without subscripts if G = A.

Semantics of KT 45 n A model M of the multi-modal logic KT 45 n is a triple (W, (R i ) i A, L), where 1. W is a (non-empty) set, 2. R i is an equivalence relation on W, for each i A, and 3. L is a function from W to P(A), where A is the given set of atoms. Let M = (W, (R i ) i A, L) be a model of KT 45 n. We use structural induction to define the following relation, for x W and multi-modal formulas φ: x = K i φ iff y = φ, for each y W with xr i y. x = E G φ iff x = K i φ, for each i G. x = C G φ iff x = EG k φ, for all natural numbers k 1. (Here E k+1 G = E G EG k and E1 G = E G.) x = D G φ iff y = φ, for all y W such that xr i y, for all i G. (The clauses for the propositional connectives are similar as in the definition of other satisfaction relations.)

Formalizing the Wise-Men Puzzle Let p i mean that man i wears a red hat and p i mean that he wears a white hat. Let Γ be the set of formulas {C(p 1 p 2 p 3 ), C(p 1 K 2 p 1 ), C( p 1 K 2 p 1 ), C(p 1 K 3 p 1 ), C( p 1 K 3 p 1 ), C(p 2 K 1 p 2 ), C( p 2 K 1 p 2 ), C(p 2 K 3 p 2 ), C( p 2 K 3 p 2 ), C(p 3 K 1 p 3 ), C( p 3 K 1 p 3 ), C(p 3 K 2 p 3 ), C( p 3 K 2 p 3 )}. The solution to the puzzle rests on two observations: 1. The set of formulas Γ plus C( K 1 p 1 K 1 p 1 ) semantically entails (with respect to KT 45 n ) C(p 2 p 3 ). 2. The set of formulas Γ plus C(p 2 p 3 ) and C( K 2 p 2 K 2 p 2 ) semantically entails K 3 p 3.

Formalizing the Muddy-Children Puzzle Let p i mean that child i is muddy. Let Γ be the set of all formulas of the form or or C(p 1 p 2 p n ) C(p i K j p i ) i j C( p i K j p i ). i j Furthermore, let α G denote the formula p i. i G p i i G The following can be proved: 1. The set of formulas Γ plus α {i} semantically entails K i p i. 2. The set of formulas Γ plus C( G k α G) and α H semantically entails i H K ip i, where H = k + 1.