PPT-Selecting the Appropriate Consistency Algorithm for CSPs

Using Machine Learning Classifiers   Daniel J Geschwender Shant Karakashian Robert J Woodward Berthe Y Choueiry Stephen D Scott Department of Computer Science amp Engineering University of NebraskaLincoln. 1. CSPs: Arc Consistency. & Domain Splitting . Jim Little. UBC . CS 322 – Search 7. October 1, . 2014. Textbook . §4.5-4.6. Slide . 2. Lecture Overview. Recap (CSP as search & Constraint Networks). CONTENTS The Algorithm The Consistency Score Regulator Eect Example From a Real Dataset Conclusion References INTRODUCTION The goal of the Regulator Eects analytic in IPA is to provide insight into by a . set of . variables. {A,B,C,…}, a set . of domain . values. for these . variables, and a . set of . constraints. {R. 1. ,R. 2. ,R. 3. ,…} restricting . the allowable combinations of values for . Minimality. and . n. -consistency . are not comparable. Proof by counter examples provided by . Shant. . Karakashian. October 8, 2012. Properties. Minimality. Every consistent combination of two . vvp’s. by Higher Level Consistency . and Tree Decomposition. Shant. . Karakashian. Dissertation Defense. Collaborations. : . Bessiere. , . Geschwender. , . Hartke. , . Reeson. , Scott, Woodward. . Support. .  . Daniel J. Geschwender, Robert J. Woodward,. . Berthe Y. Choueiry. Constraint Systems Laboratory • Department . of Computer Science & Engineering • University of Nebraska-Lincoln. Constraint Satisfaction Problem:. H. -Homomorphisms. Yuichi Yoshida . (. Problems. (Chapter 6). Two classes of search problems. Assumptions: single agent, deterministic, fully observable, discrete environment. Search for . planning. The path to the goal is the important thing. ). C. onstraint Propagation and Local Search. This lecture topic (two lectures). Chapter 6.1 – 6.4, except 6.3.3. Next lecture topic (two lectures). Chapter 7.1 – 7.5. (Please read lecture topic material before and after each lecture on that topic). Constraint satisfaction problems (CSPs). Definition:. State. is defined by . variables. . X. i. with . values. from . domain. . D. i. Goal test. . is a set of . constraints. specifying allowable combinations of values for subsets of variables. Dr. Mark H. Mortensen. 66.490.211 and 212. Tues &Thurs 2:00 to 3:15. . 3:30 to 4:45. Manning School of Business. Today. Turn in #4 Dunkin Donuts Case Study. Attendance. Discussion on . SWOT. Constraint Satisfaction. Daniel Weld. Slides . adapted from Dan Klein, Stuart . Russell, Andrew Moore & Luke . Zettlemoyer. . Recap: . Search Problem. States . configurations . of the . world. Successor function: . 1. CSPs: Arc Consistency. & Domain Splitting . Computer Science cpsc322, Lecture 13. (Textbook . Chpt. 4.5 ,. 4.6). May, 30, 2017. CPSC 322, Lecture 13. Slide . 2. Lecture Overview. Recap (CSP as search & Constraint Networks). Constraint Satisfaction Problems. Instructor: Anca Dragan. A few Slides about Integer CSPs have been added by Christoph F. Eick (UH). University of California, Berkeley. [These slides adapted from Dan Klein and Pieter .