Minimal Knowledge and Negation as Failure Ming Fang 7/24/2009.

Slides:



Advertisements
Similar presentations
First-Order Logic Chapter 8.
Advertisements

Predicate Logic Colin Campbell. A Formal Language Predicate Logic provides a way to formalize natural language so that ambiguity is removed. Mathematical.
Some important properties Lectures of Prof. Doron Peled, Bar Ilan University.
Possible World Semantics for Modal Logic
1 Logic Logic in general is a subfield of philosophy and its development is credited to ancient Greeks. Symbolic or mathematical logic is used in AI. In.
Propositional Logic Russell and Norvig: Chapter 6 Chapter 7, Sections 7.1—7.4 Slides adapted from: robotics.stanford.edu/~latombe/cs121/2003/home.htm.
Auto-Epistemic Logic Proposed by Moore (1985) Contemplates reflection on self knowledge (auto-epistemic) Permits to talk not just about the external world,
LDK R Logics for Data and Knowledge Representation Modal Logic Originally by Alessandro Agostini and Fausto Giunchiglia Modified by Fausto Giunchiglia,
Knowledge Representation Methods
Outline Recap Knowledge Representation I Textbook: Chapters 6, 7, 9 and 10.
Computability and Complexity 9-1 Computability and Complexity Andrei Bulatov Logic Reminder (Cnt’d)
CPSC 322, Lecture 20Slide 1 Propositional Definite Clause Logic: Syntax, Semantics and Bottom-up Proofs Computer Science cpsc322, Lecture 20 (Textbook.
Department of Computer Science© G.M.P O'Hare University College Dublin DEPARTMENT OF COMPUTER SCIENCE COMP 4.19Multi-Agent Systems(MAS) Lectures 19&20.
TR1413: Discrete Mathematics For Computer Science Lecture 3: Formal approach to propositional logic.
Knowledge Representation I (Propositional Logic) CSE 473.
Many Valued Logic (MVL) By: Shay Erov - 01/11/2007.
Predicate Calculus.
EE1J2 – Discrete Maths Lecture 5 Analysis of arguments (continued) More example proofs Formalisation of arguments in natural language Proof by contradiction.
Propositional Logic Agenda: Other forms of inference in propositional logic Basics of First Order Logic (FOL) Vision Final Homework now posted on web site.
EE1J2 – Discrete Maths Lecture 4 Analysis of arguments Logical consequence Rules of deduction Rules of equivalence Formal proof of arguments See: Anderson,
Belief Revision Lecture 1: AGM April 1, 2004 Gregory Wheeler
Automaten und Formale Sprachen Epilog
Intro. to Logic CS402 Fall Propositional Calculus - Semantics (2/3) Propositional Calculus - Semantics (2/3) Moonzoo Kim CS Division of EECS Dept.
Theory and Applications
Fall 98 Introduction to Artificial Intelligence LECTURE 7: Knowledge Representation and Logic Motivation Knowledge bases and inferences Logic as a representation.
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)
Mathematics What is it? What is it about?. Terminology: Definition Axiom – a proposition that is assumed without proof for the sake of studying the consequences.
Semantic web course – Computer Engineering Department – Sharif Univ. of Technology – Fall Description Logics: Logic foundation of Semantic Web Semantic.
First-Order Logic Introduction Syntax and Semantics Using First-Order Logic Summary.
LDK R Logics for Data and Knowledge Representation Modal Logic Originally by Alessandro Agostini and Fausto Giunchiglia Modified by Fausto Giunchiglia,
ARTIFICIAL INTELLIGENCE [INTELLIGENT AGENTS PARADIGM] Professor Janis Grundspenkis Riga Technical University Faculty of Computer Science and Information.
CS 4100 Artificial Intelligence Prof. C. Hafner Class Notes Jan 17, 2012.
Chapter Five Conditional and Indirect Proofs. 1. Conditional Proofs A conditional proof is a proof in which we assume the truth of one of the premises.
Ch 1.4: Basic Proof Methods I A theorem is a proposition, often of special interest. A proof is a logically valid deduction of a theorem, using axioms,
CS6133 Software Specification and Verification
Reasoning about Knowledge 1 INF02511: Knowledge Engineering Reasoning about Knowledge (a very short introduction) Iyad Rahwan.
Artificial Intelligence “Introduction to Formal Logic” Jennifer J. Burg Department of Mathematics and Computer Science.
Applied Logic, Programming-Languages and Systems (ALPS) UTD Slide- 1 University of Texas at Dallas Modal Logic Gopal Gupta Department of Computer.
Nikolaj Bjørner Microsoft Research DTU Winter course January 2 nd 2012 Organized by Flemming Nielson & Hanne Riis Nielson.
CS2351 Artificial Intelligence Bhaskar.V Class Notes on Knowledge Representation - Logical Agents.
Knowledge Repn. & Reasoning Lec. #5: First-Order Logic UIUC CS 498: Section EA Professor: Eyal Amir Fall Semester 2004.
Chapter 7. Propositional and Predicate Logic Fall 2013 Comp3710 Artificial Intelligence Computing Science Thompson Rivers University.
Computing & Information Sciences Kansas State University Lecture 12 of 42 CIS 530 / 730 Artificial Intelligence Lecture 12 of 42 William H. Hsu Department.
Propositional Logic Rather than jumping right into FOL, we begin with propositional logic A logic involves: §Language (with a syntax) §Semantics §Proof.
1 First Order Logic CS 171/271 (Chapter 8) Some text and images in these slides were drawn from Russel & Norvig’s published material.
EEL 5937 Content languages EEL 5937 Multi Agent Systems Lecture 10, Feb. 6, 2003 Lotzi Bölöni.
Tautology. In logic, a tautology (from the Greek word ταυτολογία) is a formula that is true in every possible interpretation.logic Greek formulainterpretation.
Metalogic Soundness and Completeness. Two Notions of Logical Consequence Validity: If the premises are true, then the conclusion must be true. Provability:
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.
CS621: Artificial Intelligence Pushpak Bhattacharyya CSE Dept., IIT Bombay Lecture–6: Propositional calculus, Semantic Tableau, formal System 2 nd August,
 Conjunctive Normal Form: A logic form must satisfy one of the following conditions 1) It must be a single variable (A) 2) It must be the negation of.
Logics for Data and Knowledge Representation
Chapter 7. Propositional and Predicate Logic
Computer Science cpsc322, Lecture 20
Knowledge Representation and Reasoning
Symbolic Reasoning under uncertainty
Chapter 8 Logic Topics
Logics for Data and Knowledge Representation
Logics for Data and Knowledge Representation
Back to “Serious” Topics…
Classical propositional logic (quick review)
Chapter 7. Propositional and Predicate Logic
Knowledge Representation I (Propositional Logic)
Computer Science cpsc322, Lecture 20
CS589 Principles of DB Systems Fall 2008 Lecture 4e: Logic (Model-theoretic view of a DB) Lois Delcambre
Presentation transcript:

Minimal Knowledge and Negation as Failure Ming Fang 7/24/2009

Outlines  Propositional MBNF  Positive MKNF  General MKNF  Extended MBNF with First-order Quantification  Description Logics of MKNF  ICs

Propositional MKNF  Built from propositional symbols (atoms) using standard propositional connectives and two modal operators B and not. B: “knowledge operator”K not : “assumption operator”A  Positive: if a formula or a theory (set of formulas) does not contain the negation as failure operator not.

Propositional MKNF  Define when a positive formula F is true in a structure (I,S):  (I,S) is a model of positive theory T if:  (i) the axioms of T are true in (I,S)  (ii) there is no (I’,S ’) such that S’ is a proper superset of S and the axioms of T are true in (I ’,S ’)  S is maximized, so the believed propositions are minimized.

Propositional MKNF  General MKNF: truth will be defined by a triple (I,S b,S n )  (I,S) is a model of positive theory T if:  (i) the axioms of T are true in (I,S,S)  (ii) there is no (I’,S’) such that S ’ is a proper superset of S and the axioms of T are true in (I,S’,S)

Propositional MKNF  An example:  It is true in (I,S’S) when:  Then a model must satisfy: (i) (ii) Three cases: (1) F is tautology  M=(I,S), S is the set of all interpretations. (2) F is not tautology but a logical consequence of G  no model (3) F is not a logical consequence of G  M=(I,Mod(G))

Quantification  Names: object constants representing all elements of |I |  (I,S) is a model of positive theory T if:  (i) the axioms of T are true in (I,S,S)  (ii) there is no (I’,S’) such that S ’ is a proper superset of S and the axioms of T are true in (I,S’,S)

Quantification  An example:  Which courses are taught?  Which courses are taught by known individuals?

MKNF-DL  Goal:  represent non-first-order features of frame systems

MKNF-DL  A set of interpretations M is a model of Σ if:  (i) the structure (M,M) satisfies Σ  (ii) for each set of interpretations M’, if M’ M, then (M’,M) does not satisfy Σ

MKNF-DL  An ideal rational agent trying to decide which set of propositions to believe.  Set of prior beliefs + set of rules  new beliefs  “logical closure”  Deduced set of beliefs coincides with the assumed believe  assumed set is justified  candidate for the agent to believe in  Two kinds of beliefs:  Beliefs that the agent assumed (A operator)  New beliefs that derived (K operator)

ICs  Example 1  IC: Each known employee must be known to be either male or female. Σ = =

ICs  Example 1

ICs  Example 2  IC: Each known employee has known social security number, which is known to be valid Σ = =