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 . 2 02 02 02 Flash point TOC F T 79 100 150 150 150 Distillation test T 78 Distillate percentage by volume of tota l distillate to 680F to 437F 25 10 to 500F 40 70 15 55 35 15 to 600F 75 93 60 87 45 80 15 75 Residue from distillation volume Heuristic - a “rule of thumb” used to help guide search. often, something learned experientially and recalled when needed. Heuristic Function - function applied to a state in a search space to indicate a likelihood of success if that state is selected. 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. Bmin/A min *maj Bmin/A min *maj n7* Bmin/A min n7* Bmin/A min *maj n7F *maj *maj *maj n7Bm Bmin/A min *maj n7* Bmin/A min *maj n7F *maj n7Bm *maj n7Bm *maj n7Bm */A A/* n11Bmin *maj AWA add9 Ain't No NPT Test Gauges TOO SIMILIAR to Riser Margin For Crew To Ignore. Pressure of Reservoir Pushing “UP” is . ~1,400 psi. Pressure of Riser Mud Pushing “DOWN” is . ~1,400 psi. Water Depth. NPT Test Gauges TOO SIMILIAR to Riser Margin For Crew To Ignore. Pressure of Reservoir Pushing “UP” is . ~1,400 psi. Pressure of Riser Mud Pushing “DOWN” is . ~1,400 psi. Water Depth. 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. 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 . CPSC 481: HCI I. Fall 2014. 1. Anthony Tang with acknowledgements to Saul Greenberg and Ehud . Sharlin. Learning Objectives. By the end of this class, you should be able to:. » understand and describe . unknown environment. Athanasios Ch. Kapoutsis. , Christina M. . Malliou. , Savvas A. Chatzichristofis and Elias B. . Kosmatopoulos. School of Electrical and Computer Engineering,. Democritus University of Thrace, Xanthi, Greece. 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. . is collaborating with JSTOR to digitize preserve and extend access to7KH3KLHOWDDSSDQhttp//wwwjstororgKDWV3UREOHP6ROYLQJXWKRUVf0LFKDHO0DUWLQH6RXUFH7KH3KLHOWDDSSDQ9RO1RSUfSS3XEOLVKHGE3KLHOWDDSSDQWHUQDWL often, something learned experientially and recalled when needed. Heuristic Function - function applied to a state in a search space to indicate a likelihood of success if that state is selected. heuristic search methods are known as “weak methods” because of their generality and because they do not apply a great deal of knowledge .