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An overview of various search algorithms used in artificial intelligence, including depth-first search (dfs), breadth-first search (bfs), uniform cost search (ucs), and a* search. It covers the key concepts, strategies, and properties of these algorithms, as well as their implementation details. The document also discusses the importance of heuristics in informed search algorithms like a* and the optimality guarantees of a* under certain conditions. The content is suitable for university-level courses in artificial intelligence, computer science, or related fields, and could be useful for students as study notes, lecture notes, or exam preparation material.
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Instructor: Roger Stanev, Ph.D.
University of Washington Bothell
(slides adapted from Anca Dragan, Dan Klein, Pieter Abbeel and others from Berkeley)
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Strategy: expand a
deepest node first
Implementation:
Fringe is a LIFO stack
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Strategy: expand a
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Fringe is a priority queue
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Cost
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Start Goal
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c ≤ 1
[Demo: empty grid UCS (L2D5)]
[Demo: maze with deep/shallow
water DFS/BFS/UCS (L2D7)]
Video of Demo Maze with Deep/Shallow Water --- DFS, BFS, or UCS? (part
Video of Demo Maze with Deep/Shallow Water --- DFS, BFS, or UCS? (part