PPT-Review Constraint Satisfaction

RampN 6164 except 633 What is a CSP Backtracking search for CSPs Choose a variable then choose an order for values Minimum Remaining Values MRV Degree Heuristic DH Least Constraining Value LCV. J Watson Research Center and Yury Makarychev Microsoft Research New England In this paper we present two approximation algorithms for the maximum constraint satisfaction problem with variables in each constraint MAX CSP Given a 1 satis64257able 2CSP some pattern of mines in the blank squares that give rise to the numbers seen." Obviously,instances of the minesweeper problem have an answer of yes or no, as most problems studiedin computational co Introduction and Backtracking Search. This lecture:. CSP Introduction and Backtracking Search. Chapter 6.1 – 6.4, except 6.3.3. Next lecture:. CSP Constraint Propagation & Local Search. Chapter 6.1 – 6.4, except 6.3.3. CSD 15-780: Graduate Artificial Intelligence. Instructors: . Zico. . Kolter. and Zack Rubinstein. TA: Vittorio . Perera. 2. Constraint satisfaction problems. A . constraint satisfaction problem. (CSP): A set of . Rob Hall . 10/14/2010. 1. This Recitation. Review of Lagrange multipliers (basic undergrad calculus). Getting to the dual for a QP. Constrained norm minimization (for SVM). Midterm review. 2. Minimizing a quadratic. 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 . Consumer. Behavior. Chapter 3. Discussion Topics. The concept of consumer utility (satisfaction). Evaluation of alternative consumption bundles using . indifference. curves. What is the role of your budget constraint in determining what you purchase?. . Sponsored by: .  . Florida Office of Rural Health. .  . Georgia Office of Rural Health. .  . Hometown Health. SHIP Grant Webinar. March 14, 2013. PATIENT/FAMILY SATISFACTION. AGENDA:. Search when states are factored. Until now, we assumed states are black-boxes.. We will now assume that states are made up of “state-variables” and their “values”. Two interesting problem classes. Introduction and Backtracking 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). Problems. . vs. . . Finite State Problems . Finite . State Problems (FSP). FSP can . be solved by searching in a space of . simple states. . . Finite states are . evaluated by domain-specific heuristics (rules) and tested to see whether they were goal states. . How is a CSP defined?. How do we solve CSPs?. Backtracking . search algorithm. Improving backtracking efficiency:. Which . variable should be assigned next?. In what order should its values be tried?. Eric . Karmouch. , . Amiya. . Nayak. Paper Presentation by Michael . Matarazzo. (mfm11@vt.edu). A Distributed Constraint Satisfaction Problem Approach to Virtual Device Composition. Eric . Karmouch. Problems. . vs. . . Finite State Problems . Finite . State Problems (FSP). FSP can . be solved by searching in a space of . simple states. . . Finite states are . evaluated by domain-specific heuristics (rules) and tested to see whether they were goal states. .