PPT-Min-Conflicts Heuristic for Solving Constraint Satisfaction Problems

Rhea McCaslin The GDS Network Guarded Discrete Stochastic neural network developed by Johnston and Adorf 2 Hubble Space Telescope Scheduling Problem PROBLEM Between 10000 30000 astronomical observations per year . 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. 9: . Search. 8. Victor R. Lesser. CMPSCI 683. Fall . 2010. Today’s Lecture. Another Form of Local Search. Repair/Debugging in Constraint Satisfaction Problems. GSAT. A Systematic Approach to Constraint Satisfaction Problems. 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 . 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. John W. Chinneck, M. . Shafique. Systems and Computer Engineering. Carleton University, Ottawa, Canada. Introduction. Goal: . Find a . good quality. integer-feasible MINLP solution . quickly. .. Trade off accuracy for speed. in this presentation were developed by Rowan professor Mark Hale. Professor Hale is a Cognitive . Psychologist and . Human . Factors . Specialist and very gratefully contributed his material to this course in . What have we learned and what are we expected to know?. Overview. Introduction. Modelling. in . MiniZinc. Finite Domain Constraint Solving. Search. Linear Programming and Network Flow. Mixed Integer Programming. 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. . Rhea . McCaslin. The GDS Network. Guarded Discrete Stochastic – neural network developed by Johnston and . Adorf. 2. Hubble Space Telescope. Scheduling Problem. PROBLEM: Between 10,000 – 30,000 astronomical observations per year . is collaborating with JSTOR to digitize preserve and extend access to7KH3KLHOWDDSSDQhttp//wwwjstororgKDWV3UREOHP6ROYLQJXWKRUVf0LFKDHO0DUWLQH6RXUFH7KH3KLHOWDDSSDQ9RO1RSUfSS3XEOLVKHGE3KLHOWDDSSDQWHUQDWL