Lecture 13: Optimizing a Search
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Description
Uniform cost search takes into account the cost associated with an action, and can be implemented with a priority queue. Heuristics extimate the cost of the remaining path to the goal; the Manhattan distance is an example of an admissible heuristic.
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Discussion of imperative, functional, and object-oriented programming styles. State machines are introduced, using a turnstile as an example.
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Two search algorithms are introduced, depth-first search and breadth-first search. Pruning rules (including dynamic programming) are also considered, so that the same state is not visited repeatedly.
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Introduction to probability theory, with the goals of making precise statements about uncertain situations and drawing reliable inferences from unreliable observations. A hidden Markov model is then applied to robot navigation.
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