Belief Revision Lecture 1: AGM April 1, 2004 Gregory Wheeler

Slides:



Advertisements
Similar presentations
Completeness and Expressiveness
Advertisements

Logic & Critical Reasoning
Artificial Intelligence Chapter 13 The Propositional Calculus Biointelligence Lab School of Computer Sci. & Eng. Seoul National University.
Possible World Semantics for Modal Logic
Formal Semantics of S. Semantics and Interpretations There are two kinds of interpretation we can give to wffs: –Assigning natural language sentences.
Auto-Epistemic Logic Proposed by Moore (1985) Contemplates reflection on self knowledge (auto-epistemic) Permits to talk not just about the external world,
L41 Lecture 2: Predicates and Quantifiers.. L42 Agenda Predicates and Quantifiers –Existential Quantifier  –Universal Quantifier 
Artificial Intelligence Chapter 14. Resolution in the Propositional Calculus Artificial Intelligence Chapter 14. Resolution in the Propositional Calculus.
Lecture 6 Hyperreal Numbers (Nonstandard Analysis)
Logical Foundations of Negotiation: Strategies and Preference Thomas Meyer, Norman Foo Rex Kwok Dongmo Zhang Presented by Shiyan Li.
Formal Logic Proof Methods Direct Proof / Natural Deduction Conditional Proof (Implication Introduction) Reductio ad Absurdum Resolution Refutation.
Logic in general Logics are formal languages for representing information such that conclusions can be drawn Syntax defines the sentences in the language.
Programming Language Semantics Denotational Semantics Chapter 5 Based on a lecture by Martin Abadi.
3/25  Monday 3/31 st 11:30AM BYENG 210 Talk by Dana Nau Planning for Interactions among Autonomous Agents.
Belief Revision Lecture 2: Beyond AGM Gregory Wheeler
Auto-Epistemic Logic Proposed by Moore (1985) Contemplates reflection on self knowledge (auto-epistemic) Allows for representing knowledge not just about.
Existential Graphs and Davis-Putnam April 3, 2002 Bram van Heuveln Department of Cognitive Science.
Let remember from the previous lesson what is Knowledge representation
Improving Recovery for Belief Bases Frances L. Johnson & Stuart C. Shapiro Department of Computer Science and Engineering, Center for Multisource.
Proof by Deduction. Deductions and Formal Proofs A deduction is a sequence of logic statements, each of which is known or assumed to be true A formal.
Programming Language Semantics Denotational Semantics Chapter 5 Part III Based on a lecture by Martin Abadi.
Propositional Calculus Math Foundations of Computer Science.
Copyright © Cengage Learning. All rights reserved.
Discrete Mathematics and Its Applications
Database Systems Normal Forms. Decomposition Suppose we have a relation R[U] with a schema U={A 1,…,A n } – A decomposition of U is a set of schemas.
BY: MISS FARAH ADIBAH ADNAN IMK. CHAPTER OUTLINE: PART III 1.3 ELEMENTARY LOGIC INTRODUCTION PROPOSITION COMPOUND STATEMENTS LOGICAL.
Review I Rosen , 3.1 Know your definitions!
Marriage Problem Your the sovereign in a small kingdom. One of your jobs is to marry off the people in kingdom. There are three rules that apply.
Boolean Algebra and Computer Logic Mathematical Structures for Computer Science Chapter 7.1 – 7.2 Copyright © 2006 W.H. Freeman & Co.MSCS Slides Boolean.
Pattern-directed inference systems
Advanced Topics in Propositional Logic Chapter 17 Language, Proof and Logic.
Propositional Logic Dr. Rogelio Dávila Pérez Profesor-Investigador División de Posgrado Universidad Autónoma Guadalajara
0 What logic is or should be Propositions Boolean operations The language of classical propositional logic Interpretation and truth Validity (tautologicity)
Machine Learning Chapter 2. Concept Learning and The General-to-specific Ordering Tom M. Mitchell.
LDK R Logics for Data and Knowledge Representation PL of Classes.
Lecture Propositional Equivalences. Compound Propositions Compound propositions are made by combining existing propositions using logical operators.
Hazırlayan DISCRETE COMPUTATIONAL STRUCTURES Propositional Logic PROF. DR. YUSUF OYSAL.
College Algebra Sixth Edition James Stewart Lothar Redlin Saleem Watson.
Theory and Applications
Lecture 4: Predicates and Quantifiers; Sets.
Propositional Logic. Propositions Any statement that is either True (T) or False (F) is a proposition Propositional variables: a variable that can assume.
1 Introduction to Abstract Mathematics Expressions (Propositional formulas or forms) Instructor: Hayk Melikya
October 17, 2012Introduction to Artificial Intelligence Lecture 11: Knowledge Representation and Reasoning I 1Semantics In propositional logic, we associate.
Logical Agents Chapter 7. Outline Knowledge-based agents Logic in general Propositional (Boolean) logic Equivalence, validity, satisfiability.
Naïve Set Theory. Basic Definitions Naïve set theory is the non-axiomatic treatment of set theory. In the axiomatic treatment, which we will only allude.
©Agent Technology, 2008, Ai Lab NJU Agent Technology Agent model and theory.
Section 1.2: Propositional Equivalences In the process of reasoning, we often replace a known statement with an equivalent statement that more closely.
CS6133 Software Specification and Verification
28.
Albert Gatt LIN3021 Formal Semantics Lecture 3. Aims This lecture is divided into two parts: 1. We make our first attempts at formalising the notion of.
Propositional Logic Rather than jumping right into FOL, we begin with propositional logic A logic involves: §Language (with a syntax) §Semantics §Proof.
1 Section 6.2 Propositional Calculus Propositional calculus is the language of propositions (statements that are true or false). We represent propositions.
Concept Learning and The General-To Specific Ordering
EEL 5937 Content languages EEL 5937 Multi Agent Systems Lecture 10, Feb. 6, 2003 Lotzi Bölöni.
1 Georgia Tech, IIC, GVU, 2006 MAGIC Lab Rossignac Lecture 01: Boolean Logic Sections 1.1 and 1.2 Jarek Rossignac.
Chapter 8 Relational Database Design. 2 Relational Database Design: Goals n Reduce data redundancy (undesirable replication of data values) n Minimize.
1 Propositional Proofs 1. Problem 2 Deduction In deduction, the conclusion is true whenever the premises are true.  Premise: p Conclusion: (p ∨ q) 
Logics for Data and Knowledge Representation ClassL (part 1): syntax and semantics.
March 3, 2016Introduction to Artificial Intelligence Lecture 12: Knowledge Representation & Reasoning I 1 Back to “Serious” Topics… Knowledge Representation.
Artificial Intelligence Logical Agents Chapter 7.
Logic.
DISCRETE MATHEMATICS CHAPTER I.
Knowledge Representation and Reasoning
The Propositional Calculus
Propositional Calculus: Boolean Algebra and Simplification
Logics for Data and Knowledge Representation
Logical Agents Chapter 7.
Back to “Serious” Topics…
1.3 Propositional Equivalences
Logical truths, contradictions and disjunctive normal form
Presentation transcript:

Belief Revision Lecture 1: AGM April 1, 2004 Gregory Wheeler

Outline: Modeling Belief States AGM Rationality Postulates: –Expansion –Contraction –Revision Entrenchment Correspondence Results

Belief A belief is a kind of mental state that represents an agent’s attitude toward a proposition: 1.Washington D.C. is the capital of the U.S.A. 2.Sam believes that New York City is the capital of the U.S.A.

Belief Propositions are true or false; An agent S may take one of three attitudes of belief toward a proposition p: –S may believe that p is true –S may believe that p is false –S may neither believe that p is true nor that p is false.

Two Dimensions of Belief Change STATIC DIMENSION: -internal -operations of reasoning -‘reflective equilibrium’

Two Dimensions of Belief Change STATIC DIMENSION -internal -operations of reasoning -‘reflective equilibrium’ DYNAMIC DIMENSION: -external input (e.g., ¬  ) -learning -absorb new information... tt’ ¬¬

Coherence Three senses of coherence/incoherence: –May apply to a single belief state: Sam believes that 0=1. –May apply to a sequence of beliefs: Sam believes A. Sam believes not A. –May apply to an agent’s disposition to choose. Sam prefers outcome O to P but chose outcome O. Note: ‘coherence’ is used differently in epistemology

Static Constraint: Inferential Coherence Minimum synchronic conditions for inferential coherence of a belief state: Maxim 1. An agent S’s beliefs should be logically consistent. Maxim 2: If proposition  is inferable from S’s beliefs, then S should believe .

Diachonic coherence Economic constraints: Maxim 3: The amount of information lost in a belief change should be kept minimal. Maxim 4: In so far as some beliefs are considered more important than others, one should retract the least important to restore equilibrium.

Coherence of choice Dynamic Constraint: Maxim 5: In so far as choices are to be made when performing a belief change, these choices should be coherent. (i.e., preference orderings should be respected)

Modeling Belief States Logical model of rational belief change

Modeling Belief States Logical model of rational belief change –Let X and Y denote sets of well-formed formulae (wffs) in a propositional language, L, and  and  denote arbitrary formulas in L. e.g., X = { ,    } Important: We will interpret these sets of wffs as sets of beliefs held by an ideal agent. This is the motivation for considering the non-classical extensions and alterations to propositional logic.

Modeling Belief States Logical model of rational belief change –Let X and Y denote sets of well-formed formulae (wffs) in a propositional language, L, and  and  denote arbitrary formulas in L. –A set X of wffs is inconsistent if there exists a wff  such that X  =  and X  = . If there is no such wff, then X is consistent. –Inference operation: Let Cn(X) = {  : X  =  }. –Let K and H denote logical theories, e.g., K = Cn(X), for some set of wffs X.

Belief Change Three values: true (t); false (f); undefined (u) Belief change may be thought of as a set operations that change the value of a wff. –Expansion: u » t or u » f. –Contraction: t » u or f » u. –Revision: t » f or f » t.

AGM Alchourrón, Gärdenfors and Makinson (1985) proposed a set of rationality postulates for expansion, contraction and revision operators. A belief change operator is a 2-place function from a logical theory, K, and target formula, , to a replacement theory: 2 L  L  2 L.

Expansion: the + operator The expression K +  denotes the replacement theory resulting from an expansion of K by .

Expansion postulates (+1) If K is a theory, then K +  is a theory. The expansion operator applied to a theory yields a theory. (+2)   (K +  ). Expansion always succeeds: the target formula  is always included in the expanded theory.

Expansion postulates (+3) K  (K +  ). An expanded theory includes the original theory. (+4) If   K, then (K +  ) = K. Expanding a theory K with a formula that is already in K does not change K.

Expansion postulates (+5) If K  H, then (K +  )  (H +  ). Expansion by the same formula  on a theory K that is a subset of a theory H preserves the set-inclusion relation between K and H.

Expansion postulates (+6) (K +  ) is the smallest theory satisfying (+1) to (+5). The expanded theory is the smallest possible and does not include wffs admitted by an operation which does not satisfy postulates (+1) to (+5). The set of theories satisfying (+1) to (+5) is closed under set intersection.

Expansion postulates Remarks: One way to expand a theory K is simply to add the target formula and close this set under logical consequence, that is to replace K by K* = Cn(K  {  }). Thm 3: K +  = Cn(K  {  }). Note: this is the only AGM operation which guarantees a unique replacement theory.

Contraction: the - operator The expression K -  denotes the replacement theory resulting from a contraction of K by .

Contraction postulates (-1) If K is a theory, then K -  is a theory. The contraction operator applied to a theory yields a theory. (-2) (K -  )  K. The contracted theory is a subset of the original theory.

Contraction postulates (-3) If   (K -  ) then (K -  ) = K. If the target formula  to be contracted is not in the original theory, then the replacement theory is identical to the original theory. (-4)   (K -  ) only if  is not a tautology. The target formula  is always removed from a theory by contraction unless  is a tautology.

Contraction postulates (-5) If   K then K  ((K-  )+  ). The Recovery Postulate. (-6) If   , then (K -  ) = (K -  ). Logically equivalent formulas give rise to identical contractions.

Contraction postulates (-7) (K -  )  (K -  )  (K -    ). The formulas that are in both the theory contracted by the target formula  and the theory contracted by the target formula  are in the theory contracted by the target conjunction,   . It is important to note that contracting by a conjunction is not the same as iterative contractions by each conjunct. Contracting by a conjunction entails removing the joint truth of the two formulas, which may be achieved by retracting one of the conjuncts.

Contraction postulates (-8) If   (K -    ), then (K -    )  K - . If the target formula of a contraction operation is a conjunction succeeds in removing one of the conjuncts, , then every formula that is removed by a contraction with that conjunct (i.e.,  ) alone is also removed by the contraction with the conjunction.

Contraction postulates Remarks: While Expansion guarantees a unique replacement theory, note that the contraction postulates do not determine a unique replacement theory. This property will be illustrated with a series of examples. Notice that this feature introduces the need for extra- logical constraints to guide our choice among candidate replacement theories.

Revision: the * operator The expression K *  denotes the replacement theory resulting from an revision of K by .

Revision postulates (*1) If K is a theory, then K *  is a theory. The revision operator applied to a theory yields a theory. (*2)   (K *  ). Revision always succeeds: the target formula  is always included in the expanded theory.

Revision postulates (*3) (K *  )  (K +  ). A revision never incorporates formulas that are not in the expansion of the original theory by the same target formula.

Revision postulates (*4) If ¬   K, then (K +  )  (K *  ). If the negation of a target formula is not in a theory, then the result of expanding the theory by that target formula is a subset of the result of revising the theory by the target formula. When taken with (*3), it follows that if the target formula is consistent with the original theory, then a revision is identical with the expansion, that is (K +  ) = (K *  ).

Revision postulates (*5) K *  =  if and only if  = . Given that a theory is consistent, if we attempt to revise the theory by a contradiction the replacement theory is inconsistent. This is the only case where revision applied to a consistent theory yields an inconsistent theory. (*6) If   , then (K *  ) = (K *  ). Logically equivalent formulas give rise to identical revisions.

Revision postulates (*7) (K *    )  ((K *  ) +  ). When revising a theory by a target formula that is a conjunction we may retain every formula in the original theory by (1) first revising the original theory by one conjunct and then (2) expand the revised theory by the other conjunct. Compare: (K *   )  (K +   ) = ((K +  ) +  ), by (*3). Since (*3) gives us (K *  )  (K +  ), (*7) gives us a tighter upper-bound on (K *   ) than (*3).

Revision postulates (*8) If ¬   (K *    ), then ((K *  )+  )  (K +   ). So long as a formula  is consistent with a revised theory K by another formula, , then the resulting theory from applying the two step procedure mentioned in (*7) is a subset of revising K by the conjunction of the two formula in question,  . Together, (*7) and (*8) entail that the two step process in (*7) is identical to the conjunction as a whole, that is ((K *  )+  ) = (K +   ) given that  is consistent with the revised theory in the first step.

Revision postulates Remarks: Like Contraction, the revision postulates do not determine a unique replacement theory. While we defined the revision operator, *, the contraction operator, -, and the expansion operator, +, independently of one another, we may nevertheless define these operators in terms of one another.

The Levi Identity Thm 8: Given that the contraction function satisfies postulates (-1) to (-4) and (-6), and the expansion function satisfies (+1) to (+6), the revision function as defined by the Levi Identity K *  = ((K - ¬  ) +  ) satisfies (*1) to (*6). Furthermore, if (+7) is satisfied, then (*7) is satisfied; if (+8) is satisfied, then (*8) is satisfied.

The Harper Identity Thm 9: Given that the revision function * satisfies (*1) to (*6), the contraction function - as defined by the Harper Identity K -  = K  (K * ¬  ) satisfies (-1) to (-6). Furthermore, if (*7) is satisfied, then (-7) is satisfied and if (*8) is satisfied, then (-8) is satisfied.

Entrenchment Def.: An epistemic entrenchment relation ≤ e is defined on formula of L, where  ≤ e  is interpreted to express that  is as epistemically entrenched as  and satisfies the 5 postulates, (EE1) through (EE5). Let  < e  stand for  is strictly more entrenched than , and  = e  for  and  are equally entrenched.

Entrenchment postulates (EE1) If  ≤ e  and  ≤ e , then  ≤ e . The epistemic entrenchment relation is transitive. (EE2) If   – , then  ≤ e . A formula is at most as entrenched as the formulas it logically entails. If  entails  and we wish to retract , we need to retract  also to avoid deriving  in the replacement theory. On the other hand,  should be at most as entrenched as  so that  may be retracted without necessarily retracting .

Entrenchment postulates (EE3) For all , , either  ≤ e    or  ≤ e    Retracting the conjunction   is achieved by either retracting  or retracting . Thus, the conjunction is at least as entrenched as one of the conjuncts From (EE2), we have the opposite relations   ≤ e  and   ≤ e . From (EE2) and (EE3), together, we have   = e  or   = e . In other words, a conjunction is as entrenched as its least entrenched conjunct.

Entrenchment postulates (EE4) When K , then   K iff  ≤ e    or  ≤ e   . Formulas not in the theory are the least entrenched and, if the theory is consistent, vice versa. (EE5) If  ≤ e  for all , then  –  The most entrenched formulas are the tautologies.

Correspondence Results (C - )   (K -  ) iff either  ≤ e    or  – . (C - ) specifies what formulas are retained in a contraction given an epistemic entrenchment relation. Only formulas that are in the original theory K can be included in the contracted theory. In addition, if the target formula is a tautology, then all formulas are retained. Otherwise, if the target formula  is less entrenched than the disjunction   , then  is retained.

Correspondence Results (C ≤e )  ≤ e  iff   K or  – (    ) An epistemic entrenchment relation can be constructed from a contraction function by (C ≤e ). If a conjunct  is not retained in the contracted theory, then it cannot be more entrenched than the other conjunct. Note that both conjuncts can be absent from the contracted theory, in which case the two conjuncts are equally entrenched,  = e .

Correspondence Results Thm: Given an epistemic entrenchment ordering ≤ e that satisfies (EE1) to (EE5), condition (C - ) uniquely determines a contraction function which satisfies the AGM contraction postulates (-1) to (-8) and condition (C ≤e ).

Correspondence Results Thm: Given a contraction function which satisfies the AGM contraction postulates (- 1) to (-8), condition (C ≤e ) uniquely determines an epistemic entrenchment ordering ≤ e that satisfies (EE1) to (EE5) and condition (C - ).

Remarks Rationality postulates generate a set of candidate theory change functions An entrenchment relation allows us to pick a specific function among the class.

Remarks The entrenchment ordering provides a constructive way to choose a specific contraction operator from the set of all possible operators.