PPT-Constraint Satisfaction Problems (CSPs)

Introduction and Backtracking Search This lecture topic two lectures Chapter 61 64 except 633 Next lecture topic two lectures Chapter 71 75 Please read lecture topic material before and after each lecture on that topic. 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 fr Abstract Many AI problems can be modeled as constraint satisfaction problems CSP but many of them are actually dynamic the set of constraints to consider evolves because of the environment the user or other agents in the framework of a dis tribute 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. Satisfiability. and Constraint Satisfaction Problems. by Carla P. Gomes, Bart Selman, . Nuno. . Crato. and . henry. . Kautz. Presented by . Yunho. Kim. Provable Software Lab, KAIST. Contents. Heavy-Tailed Phenomena in . Marek Perkowski. Projects for the new ECE 574 class. Project based class. Graduate class - . no prerequisities. C/C++ welcome, . but not mandatory. Verilog/VHDL welcome, . but not mandatory. .. FPGA. 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. 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). 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. . 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. . 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. 1 Constraint Satisfaction Problems Soup Total Cost < $30 Chicken Dish Vegetable Rice Seafood Pork Dish Appetizer Must be Hot&Sour No Peanuts No Peanuts Not Chow Mein Not Both Spicy Constraint Network 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 .