'Variable ordering' presentation slideshows

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

1.09k views • 92 slides

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

1.25k views • 79 slides

Lecture 5: Constraint Satisfaction Problems

Lecture 5: Constraint Satisfaction Problems. ICS 271 Fall 2008. Outline. The constraint network model Variables, domains, constraints, constraint graph, solutions Examples: graph-coloring, 8-queen, cryptarithmetic, crossword puzzles, vision problems,scheduling, design

870 views • 76 slides

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

668 views • 56 slides

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

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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

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Constraint Satisfaction Problems

636 views • 36 slides

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

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Constraint Satisfaction Problems

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Constraint Satisfaction Problems

461 views • 34 slides

Distributions of Randomized Backtrack Search

435 views • 32 slides

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.

444 views • 26 slides

Planning with Constraints Graphplan & SATplan

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

500 views • 24 slides

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

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Decision Procedures An Algorithmic Point of View

339 views • 22 slides

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.

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CMSC 671 Fall 2001

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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 .

1.02k views • 19 slides

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

241 views • 12 slides

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.

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