Quantified Formulas - Decision Procedure Daniel Kroening, Ofer Strichman Presented by Changki Hong 07 NOV 08.

Slides:



Advertisements
Similar presentations
Biointelligence Lab School of Computer Sci. & Eng.
Advertisements

Propositional and First Order Reasoning. Terminology Propositional variable: boolean variable (p) Literal: propositional variable or its negation p 
Methods of Proof Chapter 7, second half.. Proof methods Proof methods divide into (roughly) two kinds: Application of inference rules: Legitimate (sound)
Methods of Proof Chapter 7, Part II. Proof methods Proof methods divide into (roughly) two kinds: Application of inference rules: Legitimate (sound) generation.
Logic.
Daniel Kroening and Ofer Strichman 1 Decision Procedures An Algorithmic Point of View SAT.
1/30 SAT Solver Changki PSWLAB SAT Solver Daniel Kroening, Ofer Strichman.
1 NP-Complete Problems. 2 We discuss some hard problems:  how hard? (computational complexity)  what makes them hard?  any solutions? Definitions 
© The McGraw-Hill Companies, Inc., Chapter 8 The Theory of NP-Completeness.
Complexity 11-1 Complexity Andrei Bulatov Space Complexity.
1 Boolean Satisfiability in Electronic Design Automation (EDA ) By Kunal P. Ganeshpure.
Presented by Ed Clarke Slides borrowed from P. Chauhan and C. Bartzis
Inference and Resolution for Problem Solving
1 Quantified Formulas Acknowledgement: QBF slides borrowed from S. Malik.
Methods of Proof Chapter 7, second half.
Search in the semantic domain. Some definitions atomic formula: smallest formula possible (no sub- formulas) literal: atomic formula or negation of an.
Last time Proof-system search ( ` ) Interpretation search ( ² ) Quantifiers Equality Decision procedures Induction Cross-cutting aspectsMain search strategy.
Daniel Kroening and Ofer Strichman 1 Decision Procedures in First Order Logic Decision Procedures for Equality Logic.
Daniel Kroening and Ofer Strichman Decision Procedure
1/25 Pointer Logic Changki PSWLAB Pointer Logic Daniel Kroening and Ofer Strichman Decision Procedure.
SAT Solver Math Foundations of Computer Science. 2 Boolean Expressions  A Boolean expression is a Boolean function  Any Boolean function can be written.
Deciding a Combination of Theories - Decision Procedure - Changki pswlab Combination of Theories Daniel Kroening, Ofer Strichman Presented by Changki.
Satisfiability Introduction to Artificial Intelligence COS302 Michael L. Littman Fall 2001.
Binary Decision Diagrams (BDDs)
Logics for Data and Knowledge Representation Propositional Logic: Reasoning Originally by Alessandro Agostini and Fausto Giunchiglia Modified by Fausto.
Decision Procedures - An algorithmic point of view
MBSat Satisfiability Program and Heuristics Brief Overview VLSI Testing B Marc Boulé April 2001 McGill University Electrical and Computer Engineering.
Lecture 22 More NPC problems
Theory of Computation, Feodor F. Dragan, Kent State University 1 NP-Completeness P: is the set of decision problems (or languages) that are solvable in.
1 Chapter 8 Inference and Resolution for Problem Solving.
INTRODUCTION TO ARTIFICIAL INTELLIGENCE COS302 MICHAEL L. LITTMAN FALL 2001 Satisfiability.
Solvers for the Problem of Boolean Satisfiability (SAT) Will Klieber Aug 31, 2011 TexPoint fonts used in EMF. Read the TexPoint manual before you.
Week 10Complexity of Algorithms1 Hard Computational Problems Some computational problems are hard Despite a numerous attempts we do not know any efficient.
NP-COMPLETENESS PRESENTED BY TUSHAR KUMAR J. RITESH BAGGA.
CS344: Introduction to Artificial Intelligence Lecture: Herbrand’s Theorem Proving satisfiability of logic formulae using semantic trees (from Symbolic.
CS621: Artificial Intelligence Pushpak Bhattacharyya CSE Dept., IIT Bombay Lecture 28– Interpretation; Herbrand Interpertation 30 th Sept, 2010.
EMIS 8373: Integer Programming NP-Complete Problems updated 21 April 2009.
Daniel Kroening and Ofer Strichman 1 Decision Procedures An Algorithmic Point of View BDDs.
LDK R Logics for Data and Knowledge Representation Propositional Logic: Reasoning First version by Alessandro Agostini and Fausto Giunchiglia Second version.
Daniel Kroening and Ofer Strichman 1 Decision Procedures An Algorithmic Point of View BDDs.
Boolean Satisfiability Present and Future
1 The Wumpus Game StenchBreeze Stench Gold Breeze StenchBreeze Start  Breeze.
© Copyright 2008 STI INNSBRUCK Intelligent Systems Propositional Logic.
Nikolaj Bjørner Microsoft Research DTU Winter course January 2 nd 2012 Organized by Flemming Nielson & Hanne Riis Nielson.
1 First order theories (Chapter 1, Sections 1.4 – 1.5) From the slides for the book “Decision procedures” by D.Kroening and O.Strichman.
CS 461 – Nov. 30 Section 7.5 How to show a problem is NP-complete –Show it’s in NP. –Show that it corresponds to another problem already known to be NP-complete.
1 Propositional Logic Limits The expressive power of propositional logic is limited. The assumption is that everything can be expressed by simple facts.
Solving the Logic Satisfiability problem Solving the Logic Satisfiability problem Jesus De Loera.
Satisfiability and SAT Solvers CS 270 Math Foundations of CS Jeremy Johnson.
Logical Agents Chapter 7. Outline Knowledge-based agents Propositional (Boolean) logic Equivalence, validity, satisfiability Inference rules and theorem.
SAT Solving As implemented in - DPLL solvers: GRASP, Chaff and
1/20 Arrays Changki PSWLAB Arrays Daniel Kroening and Ofer Strichman Decision Procedure.
1 Boolean Satisfiability (SAT) Class Presentation By Girish Paladugu.
Daniel Kroening and Ofer Strichman 1 Decision Procedures An Algorithmic Point of View Basic Concepts and Background.
Proof Methods for Propositional Logic CIS 391 – Intro to Artificial Intelligence.
2009/6/30 CAV Quantifier Elimination via Functional Composition Jie-Hong Roland Jiang Dept. of Electrical Eng. / Grad. Inst. of Electronics Eng.
Daniel Kroening and Ofer Strichman 1 Decision Procedures An Algorithmic Point of View Quantified Formulas Acknowledgement: QBF slides borrowed from S.
COMPLEXITY. Satisfiability(SAT) problem Conjunctive normal form(CNF): Let S be a Boolean expression in CNF. That is, S is the product(and) of several.
Daniel Kroening and Ofer Strichman 1 Decision Procedures in First Order Logic Decision Procedures for Equality Logic.
Decision Procedures in First Order Logic
Decision Procedures - An algorithmic point of view
Gábor Kusper University of Linz RISC Austria
NP-Completeness Proofs
Binary Decision Diagrams
Introduction to the Boolean Satisfiability Problem
ECE 667 Synthesis and Verification of Digital Circuits
Logics for Data and Knowledge Representation
Decision Procedures An Algorithmic Point of View
Introduction to the Boolean Satisfiability Problem
Methods of Proof Chapter 7, second half.
Presentation transcript:

Quantified Formulas - Decision Procedure Daniel Kroening, Ofer Strichman Presented by Changki Hong 07 NOV 08

2 / 21 Why do we need Quantifier More modeling power Examples of quantifiers usage : “Everyone in the room has a friend” “There exists a person whose age is 26.” Hard to express such examples in propositional logic which has no quantifier. Example of quantifiers usage in math : For any integer x, there is an integer y smaller than x: There is quantifier alternation between the universal and existential quantifiers. What we considered so far was the decision problems for formulas with nonalternating quantifiers. Changki pswlab Quantified Formulas

3 / 21 Contents Introduction Quantifier Elimination Search-Based Algorithms for Quantified Boolean Formulas Conclusion Changki pswlab Quantified Formulas

4 / 21 Quantified Propositional Logic (QPL) Quantified Propositional Logic is propositional logic enhanced with quantifiers. Sentences in QBF are better known as quantified Boolean formulas (QBFs). Syntax Complexity The validity problem of QBF is PSPACE-complete  theoretically harder to solve than SAT, which is “only” NP-complete Changki pswlab Quantified Formulas

5 / 21 Prenex Normal Form (PNF) Definition 1. Prenex normal form A formula is said to be in prenex normal form (PNF) if it is in the form where for all and is a variable. Quantification prefix  Quantification string on the left of the formula Quantification suffix  Quantifier-free formula We can make quantifier alternating PNF by introducing dummy variable.  From here on we are considering that input formulas are in PNF. Changki pswlab Quantified Formulas

6 / 21 Transform an input formula into PNF Lemma 1. For every quantified formula Q there exist a formula Q’ in prenex normal form such that Q is valid if and only if Q’ is valid. Algorithm - transformation an input formula into PNF Changki pswlab Quantified Formulas

7 / 21 PNF - example Transform below formula into PNF 1. Eliminate ‘  ’ 2. Push negations inside : 3. Renaming 4. Move quantifiers to front : Changki pswlab Quantified Formulas

8 / 21 Quantifier Elimination Eliminating an existential quantifier over a conjunction of Boolean literals is trivial If x appears with both phases in the conjunction  Unsatisfiable otherwise  x can be removed.  ex) Eliminating universal quantifier using the fact  However, we need an algorithm which can directly applicable to CNF  Since converting formulas to DNF can result in an exponential growth in the formula size. Changki pswlab Quantified Formulas

9 / 21 Elimination with binary resolution (1/2) Resolution based QBF algorithm Resolution gives us a method to eliminate a variable x from a pair of clauses in which x appears with opposite phases. Solving  apply resolution to all pairs of clauses where x appears with opposite phases.  Example Changki pswlab Quantified Formulas

10 / 21 Elimination with binary resolution (2/2) Solving Transform to via : Eliminating universal quantifiers in CNF  Simply erase them form the formula : if the formula is evaluated TRUE for all value of x, this means that we can’t satisfy a clause while relying on a specific value of x. Example Changki pswlab Quantified Formulas

11 / 21 Elimination with Expansion Quantifier elimination is based on expansion of quantifiers, according to the following equivalences: Example Changki pswlab Quantified Formulas

12 / 21 Contents Introduction Quantifier Elimination Search-Based Algorithms for Quantified Boolean Formulas Conclusion Changki pswlab Quantified Formulas

13 / 21 Search-Based Algorithms for QBF Most competitive QBF solvers are based on an adaptation of DPLL solvers. resembles the basic DPLL algorithm without learning and nonchronological backtracking. However, QBF solvers required to handle of quantifier alternation The binary search tree has to be changed Distinguish between universal nodes and existential nodes. Universal node are labeled with a symbol “ ”. Changki pswlab Quantified Formulas

14 / 21 QBF search tree Definition 2. QBF search tree corresponding to a quantified propositional formula Given a QBF Q in prenex normal form and an ordering of its variables (say, x 1, …, x n ), a QBF search tree corresponding to Q is a binary labeled tree of height n+1 with two types of internal nodes, universal and existential, in which:  The root node is labeled with Q and associated with depth 0.  One of the children of each node at level i,, is marked with x i+1, and the other with ¬ x i+1  A node in level i,, is universal if the variable in level i+1 is universally quantified.  A node in level i,, is existential if the variable in level i+1 is existentially quantified. Changki pswlab Quantified Formulas

15 / 21 Validity of QBF tree Definition 3. validity of a QBF tree A QBF tree is valid if and only if its root is satisfied. This is determined recursively according to the following rules:  A leaf in a QBF binary tree corresponding to a QBF Q is satisfied if the assignment corresponding to the path to this leaf satisfies the quantification suffix of Q.  A universal node is satisfied if both of its children are satisfied.  A existential node is satisfied if at least one of its children is satisfied. Changki pswlab Quantified Formulas

16 / 21 Validity of QBF tree - Example Example of checking validity of QBF tree Consider the formula Changki pswlab Quantified Formulas The second and third u nodes are the only nodes that are satisfied. e and ¬e are not satisfied, because both of their children aren’t satisfied Therefore, the root node, representing Q, is not satisfied and hence Q is not valid. Q e¬e¬e uu ¬u¬u ¬u¬u

17 / 21 Overview of search-based algorithm Same formula Changki pswlab Quantified Formulas e = 0 u = 0 e = 1 u = 0 S u = 1 F F F F F Not valid!

18 / 21 Search-based Algorithm for QBF Changki pswlab Quantified Formulas

19 / 21 Search-based algorithm - Example Changki pswlab Quantified Formulas Same formula e = 0 u = 0 e = 1 u = 0 S u = 1 F F F F F Not valid!

20 / 21 Contents Introduction Quantifier Elimination Search-Based Algorithms for Quantified Boolean Formulas Conclusion Changki pswlab Quantified Formulas

21 / 21 Conclusion Two quantifier elimination scheme Resolution Expansion Search-based algorithm for QBF useful to check validity of given formula in QBF Changki pswlab Quantified Formulas