PPT-ANALYSIS OF A LOCAL SEARCH HEURISTIC FOR FACILITY LOCATION

Madhukar R Korupolu C Greg Plaxton Rajmohan Rajaraman Proceedings of the ninth annual ACMSIAM Symposium on Discrete Algorithms SODA 1998 LOCAL SEARCH TECHNIQUE. 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. Sources. :. My own. Joseph . Siefers. presentation 2008. Case Study—The 8-Puzzle. Case Study: The 8-Puzzle. http://www.permadi.com/java/puzzle8/. Case Study: The 8-Puzzle. Rules of the game:. Move free space {left, up, down, right} within boundaries of puzzle.. Initialize. . the . frontier . using the . starting state. While the frontier is not empty. Choose a frontier node to expand according to . search strategy . and take it off the frontier. If the node contains the . 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. 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. . the . frontier . using the . starting state. While the frontier is not empty. Choose a frontier node to expand according to . search strategy . and take it off the frontier. If the node contains the . 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. 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 . Winter 2018. Introduction to Artificial Intelligence. Prof. Richard Lathrop. Reading: R&N 3.5-3.7. Outline. Review limitations of uninformed search methods . Informed (or heuristic) search. Problem-specific heuristics to improve efficiency . Introduction to Artificial Intelligence. Prof. . Richard Lathrop. Reading: R&N 3.5-3.7. Outline. Review limitations of uninformed search methods . Informed (or heuristic) search. Problem-specific heuristics to improve efficiency . 1 Informed (Heuristic) Search Idea: be smart about what paths to try. 2 Blind Search vs. Informed Search What’s the difference? How do we formally specify this? A node is selected for expansion based on an evaluation function that estimates cost to goal. Continued. Before We Start. HW1 extended to Monday. Submit online (now working) and bring paper print out. Questions?. Competency Demo next Wednesday. Study Guide Posted. We will have some discussion time on Monday. 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 . 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 .