'Variable ordering' presentation slideshows

Artificial Intelligence

Artificial Intelligence. Constraint Programming 3: The Party. Ian Gent ipg@cs.st-and.ac.uk. Artificial Intelligence. Constraint Programming 3. Part I : Formulation Part II: Progressive piss up at a yacht club. Constraint Satisfaction Problems .

Planning with Constraints Graphplan & SATplan

Planning with Constraints Graphplan & SATplan . Yongmei Shi. Introduction. Graphplan

Lecture 5: Constraint Satisfaction Problems

Lecture 5: Constraint Satisfaction Problems. ICS 271 Fall 2006. Outline. The constraint network model Variables, domains, constraints, constraint graph, solutions Examples: graph-coloring, 8-queen, cryptarithmetic, crossword puzzles, vision problems,scheduling, design

Lecture 08: Transform 1

ESE535: Electronic Design Automation

ESE535: Electronic Design Automation. Day 15: March 18, 2009 Static Timing Analysis and Multi-Level Speedup. Today. Topological Worst Case not adequate (too conservative) Sensitization Conditions Timed Calculus Delay-justified paths Timed-PODEM Speedup. Compute ASAP schedule

Constraint Satisfaction Problems

Constraint Satisfaction Problems. Chapter 5 Section 1 – 3. Outline. Constraint Satisfaction Problems (CSP) Backtracking search for CSPs Local search for CSPs. Constraint satisfaction problems (CSPs). Standard search problem:

Decision Structures

Decision Structures. Vertex represents decision Follow green (dashed) line for value 0 Follow red (solid) line for value 1 Function value determined by leaf value. Truth Table. Decision Tree. Variable Ordering. Assign arbitrary total ordering to variables e.g., x 1 < x 2 < x 3

CMSC 671 Fall 2001

CMSC 671 Fall 2001. Class #7 – Tuesday, September 25. Today’s class. Interleaving backtracking and consistency checking Variable-ordering heuristics Value-ordering heuristics Intelligent backtracking. Advanced Constraint Techniques.

IPIAC

IPIAC. Multidimensional data processing. Multivariate data. Multivariate data consist of several variables for each observation. Actually, serious data is always multivariate. Some variables are usually not collected to simplify collecting and processing.

Planning under Uncertainty with Markov Decision Processes: Lecture II

Planning under Uncertainty with Markov Decision Processes: Lecture II. Craig Boutilier Department of Computer Science University of Toronto. Recap. We saw logical representations of MDPs propositional: DBNs, ADDs, etc. first-order: situation calculus

Constraint Satisfaction Problems

Constraint Satisfaction Problems. Chapter 5 Section 1 – 3. Outline. Constraint Satisfaction Problems (CSP) Backtracking search for CSPs Local search for CSPs. Constraint satisfaction problems (CSPs). Standard search problem:

Constraint Satisfaction Problems

Constraint Satisfaction Problems. Chapter 5 Section 1 – 3. Grand Challenge: http://www.grandchallenge.org/. Constraint satisfaction problems (CSPs). CSP: state is defined by variables X i with values from domain D i

Distributions of Randomized Backtrack Search

Distributions of Randomized Backtrack Search. Key Properties: I Erratic behavior of mean II Distributions have “ heavy tails ”. 2000. 500. Erratic Behavior of Search Cost Quasigroup Completion Problem. 3500!. sample mean. Median = 1!. number of runs. 1. Number backtracks.

Foundations of Constraint Processing CSCE421/821, Spring 2009

Lookahead Schemas. Foundations of Constraint Processing CSCE421/821, Spring 2009 www.cse.unl.edu/~choueiry/S09-421-821/ All questions to cse421@cse.unl.edu Berthe Y. Choueiry (Shu-we-ri) Avery Hall, Room 123B choueiry@cse.unl.edu Tel: +1(402)472-5444. Outline. Looking ahead Schemas

Decision Procedures An Algorithmic Point of View

Decision Procedures An Algorithmic Point of View. BDDs Modified by Aditya Kanade E0 223 – Indian Institute of Science. Part I. Reminders - What is Logic Proofs by deduction Proofs by enumeration Soundness and Completeness Deciding Propositional Logic SAT tools BDDs. . . . . .

Grouping Heuristics for Word-Level Decision Diagrams

Grouping Heuristics for Word-Level Decision Diagrams. Rolf Drechsler Marc Herbstritt Bernd Becker Institute of Computer Science University of Freiburg 79110 Freiburg, Germany. Outline. Introduction Word-Level Decision Diagrams (WLDDs) Variable Grouping Topology-based Heuristic

On the Relation between SAT and BDDs for Equivalence Checking

On the Relation between SAT and BDDs for Equivalence Checking. Sherief Reda Rolf Drechsler Alex Orailoglu. Computer Science & Engineering Dept. Institute of Computer Science University of Bremen. Computer Science & Engineering Dept. University of California, San Diego.

Network Protocols

NETW 703. Network Protocols. BDDs & Theorem Proving Binary Decision Diagrams. Dr. Eng. Amr T. Abdel-Hamid. Lectures are based on slides by: K. Havelund & Agroce , Reliable Software: Testing and Monitoring, CMU. E. Clarke, Formal Methods, to be updated by course name

Artificial Intelligence

Artificial Intelligence. Search: 4 Search Heuristics. Ian Gent ipg@cs.st-and.ac.uk. Artificial Intelligence. Search 4. Part I : Depth first search for SAT Part II: Davis-Putnam Algorithm Part III: Heuristics for SAT. Search: the story so far.

CS137: Electronic Design Automation

CS137: Electronic Design Automation. Day 13: May 17, 2004 Modern SAT Solvers (Chaff). Today. SAT Davis-Putnam Data Structures Optimizations Watch2 VSIDS ?restarts. Problem. SAT: Boolean Satisfiability Given: logical formula f in CNF