PPT-1 Informed (Heuristic) Search Idea: be smart about what paths

1 Informed Heuristic Search Idea be smart about what paths to try 2 Blind Search vs Informed Search Whats the difference How do we formally specify this A node is selected for expansion based on an evaluation function that estimates cost to goal. 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. Idea: give the algorithm “hints” about the desirability of different states . Use an . evaluation function. . to rank nodes and select the most promising one for expansion. Greedy best-first search. 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 . 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 . 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. 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. 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. 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 . Continued. Before we continue. Breadth-First. Depth-First. Uniform Cost. Iterative-Deepening. Before we continue. Breadth-First. S,A,B,D,C,G. Depth-First. S,A,C,D,B,G. Uniform Cost. S,A,B,D,C,G. Iterative-Deepening. 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 . 10. . Tutorial. 1. Today’s Activity: Heuristic Evaluation. General steps:. Perform an independent assessment of which heuristics a system violates. With a small group of 4, discuss and aggregate your individual results, listed by heuristic.