04-local_search - Scaling Up Foundations of Artificial...

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1 Foundations of Artificial Intelligence Local Search CS472 – Fall 2007 Filip Radlinski Scaling Up So far, we have considered methods that systematically explore the full search space, possibly using principled pruning (A* etc.). The current best such algorithms (RBFS / SMA*) can handle search spaces of up to 10 100 states ~ 500 binary valued variables. But search spaces for some real-world problems might be much bigger - e.g. 10 30,000 states. Here, a completely different kind of search is needed. Local Search Methods Example QuickTime™ and a TIFF (LZW) decompressor are needed to see this picture. QuickTime™ and a TIFF (LZW) decompressor are needed to see this picture. Optimization Problems We're interested in the Goal State - not in how to get there. Optimization Problem: - State: vector of variables - Objective Function: f : state - Goal: find state that maximizes or minimizes the objective function Examples: VLSI layout, job scheduling, map coloring, N-Queens. Example Local Search Methods Applicable to optimization problems. Basic idea: - use a single current state - don't save paths followed - generally move only to successors/neighbors of that state Generally require a complete state description .
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