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Is conflict directed backjumping a waste of time?
AR 33 Barbara M Smith (up to 2001)
Chen & van Beek 2001
Chen & van Beek cbj a waste of time Random problems, no structure
Chen & van Beek
10 cases where GAC beat GAC-CBJ When cbj gets beaten it gets beaten by a very small amount i.e. cbj has a small overhead Chen & van Beek
When cbj wins it can win by a HUGE margin cbj is robust Chen & van Beek
The bottom line Chen & van Beek
Bayardo & Schrag SAT: relsat(i) uses cbj + relevance bounded learning, retaining conflict sets of size ≤ i as clauses,
Cbj. What’s a csp? a set of variables each with a domain of values a collection of constraints (I’m going to assume binary for the present) assign each.
网上报账系统包括以下业务： 日常报销 差旅费报销 借款业务 1. 填写报销内容 2. 选择支付方式 （或冲销借款） 3. 提交预约单 4. 打印预约单并同分类粘 贴好的发票一起送至财务 处 预约报销步骤： 网上报账系统 薪酬发放管理系统 财务查询系统 1.
I am Patrick Prosser I am a senior lecturer at Glasgow I teach AF2 & CP4 I am a member of the algorithms group the apes (distributed, not disbanded) I.
Person vs. ? By: TR20 and CM13 Start. Instructions 1.) Pick the correct picture about what type of conflict is going on. 2.) If you get the question try.
I am Patrick Prosser I am a senior lecturer at Glasgow I teach algorithms & data structures in java I am a member of the algorithms group the apes (distributed,
Foundations of Constraint Processing, Fall 2005 Sep 20, 2005BT: A Theoretical Evaluation1 Foundations of Constraint Processing CSCE421/821, Fall 2005:
Constraint Satisfaction Problems Tuomas Sandholm Carnegie Mellon University Computer Science Department [Read Chapter 6 of Russell & Norvig]
Bayes’ Rule Anchors: Olyvia Dean Viral Patel Eric Van Beek Group: Helium δ November 6, 2007.
Constraint Satisfaction Problems Tuomas Sandholm Carnegie Mellon University Computer Science Department Read Chapter 6 of Russell & Norvig.
Scatter Diagrams Isabel Smith. Why do we use scatter diagrams? We use scatter diagrams to see whether two sets of data are linked, e.g. height and shoe.
Constraint Satisfaction Problems Tuomas Sandholm Carnegie Mellon University Computer Science Department [Read chapter 5 of Russell & Norvig]
Eliminating non- binary constraints Toby Walsh Cork Constraint Computation Center.
Towards theoretical frameworks for comparing constraint satisfaction models and algorithms Peter van Beek, University of Waterloo CP 2001 · Paphos, Cyprus.
1 Backdoor Sets in SAT Instances Ryan Williams Carnegie Mellon University Joint work in IJCAI03 with: Carla Gomes and Bart Selman Cornell University.
Greeting Task – Keyword Recap Unscramble the Medical Technology Keywords syrougarc cninglo ihdrby eombyr neeg ayetrhp alictfriia nsnimiaoetni rsivao igbslni.
Make sure you spell it correctly!. Rhythmic Relationships When dealing with rhythm, it is important to understand the relationships between rhythmic.
Government Powers (Division of Powers) National Government State Government Powers Granted Powers Denied Delegated Powers Reserved Powers Concurrent Powers.
GRASP: A Search Algorithm for Propositional Satisfiability EE878C Homework #2 2002/11/1 KAIST, EECS ICS Lab Lee, Dongsoo.
Pricing Combinatorial Markets for Tournaments Presented by Rory Kulz.
Randomzied Unique k-SAT R. Paturi, P. Pudlak, M.E. Saks and F. Zane An Improved Exponential-time Algorithm for k-SAT Journal of the ACM, Vol 52, No. 3,
Non-binary constraints Toby Walsh Dept of CS University of York England.
MAC and Combined Heuristics: Two Reasons to Forsake FC (and CBJ?) on Hard Problems Christian Bessière and Jean-Charles Régin Presented by Suddhindra Shukla.
Project proposals. Most involve implementation inside minisat+ Project includes: Reading Implementation Evaluation Short presentation Submission of results.
Constrainedness Including slides from Toby Walsh.
Modelling for Constraint Programming Barbara Smith CP 2010 Doctoral Programme.
Close Reading Reading with a PLAN for Understanding the Material What do you know about active reading?
1 Understanding the Power of Clause Learning Ashish Sabharwal, Paul Beame, Henry Kautz University of Washington, Seattle IJCAI ConferenceAug 14, 2003.
Random Sampling using RAN#. Random Sampling using Ran# The Ran#: Generates a pseudo random number to 3 decimal places that is less than 1. i.e. it generates.
Constraint Models for the Covering Test Problems Authors: Brahim Hnich, Steven D. Prestwich, Evgeny Selensky, Barbara M. Smith Speaker: Pingyu Zhang CSE.
If a sparse, NP-Complete language exists => P = NP Let S be a sparse NP-Complete language Define C(n) = |S ≤n | and C a (n) = |S ≤p a (n) | Define p ℓ.
Greeting Task – Keyword Bingo Draw a 3 x 3 grid and pick 9 words from the list below CHASTITYCONTRACEPTIONHOMOSEXUALITY HETEROSEXUALITYALCOHOLDRUGS RECREATIONALEXPERIENTIALEXPERIMENTAL.
Determining the Sample Size. Doing research costs… Power of a hypothesis test generally is an increasing function of sample size. Margin of error is generally.
A PRODUCT REVIEW OF XYZ BY
CP Summer School Modelling for Constraint Programming Barbara Smith 1.Definitions, Viewpoints, Constraints 2.Implied Constraints, Optimization,
The responsibility of brands in baby category Communicate with a genuine understanding of customers needs. Inform and be impartial, therefore win hearts.
CPM!. Just do it! JChoco We jumped in, and took it for a spin Some counting problems Meeting scheduling problem … you got your hands dirty.
Constraint-based problem solving n Model problem ä specify in terms of constraints on acceptable solutions ä define variables (denotations) and domains.
Backtracking search: look-back Chapter 6. Look-back: Backjumping / Learning Backjumping: In deadends, go back to the most recent culprit. Learning: constraint-recording,
Doc.: IEEE /0294r1 Submission Dynamic Sensitivity Control Channel Selection and Legacy Sharing Date: Authors: Graham Smith, DSP GroupSlide.
Constraint Programming Peter van Beek University of Waterloo.
Heavy-Tailed Behavior and Search Algorithms for SAT Tang Yi Based on 
1 Backdoors To Typical Case Complexity Ryan Williams Carnegie Mellon University Joint work with: Carla Gomes and Bart Selman Cornell University.
Unit 5 Simple Present, Time Clauses, Used To, and Would.
Algorithmic Aspects of Proportional Symbol Maps Sergio Cabello Herman Haverkort Marc van Kreveld Bettina Speckmann IMFM Ljubljana TU Eindhoven Utrecht.
Arc consistency AC5, AC2001, MAC. AC5 A generic arc-consistency algorithm and its specializations AIJ 57 (2-3) October 1992 P. Van Hentenryck, Y. Deville,
Unit 31 Object Relative Clauses (Adjective Clauses with Object Relative Pronouns)
Confidence Intervals For a Sample Mean. Point Estimate singleUse a single statistic based on sample data to estimate a population parameter Simplest approach.
1 P NP P^#P PSPACE NP-complete: SAT, propositional reasoning, scheduling, graph coloring, puzzles, … PSPACE-complete: QBF, planning, chess (bounded), …
© 2007 Ray S. Babcock Tracks Game is played on a (nxn) set of squares. There are three possible moves (labeled A,B,C). Players alternate making a move.
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