Lecture-6

# Lecture-6 - Artificial Intelligence CS 165A 165A Thursday...

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Artificial Intelligence S 165A CS 165A Thursday, Jan 20, 2011 dvanced Search (Ch 4) Advanced Search (Ch. 4) 1

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Review • Optimality of A* et OPT be the optimal path cost Let OPT be the optimal path cost. – All non-goal nodes on this path have f OPT. Positive costs on edges – The goal node on this path has f = OPT. • A* search does not stop until an f-value of OPT is reached. ll other goal nodes have an f cost higher than OPT – All other goal nodes have an f cost higher than OPT. • All non-goal nodes on the optimal path are eventually expanded. – The optimal goal node is eventually placed on the priority queue, and reaches the front of the queue. 3

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Iterative Improvement Algorithms •A n iterative improvement algorithm starts with a (possibly random) proposed solution, and then makes modifications to improve its quality – Each state is a (proposed) solution – Usually keeps information on current state only S 0 S 1 S 2 S n • Generally for problems in which non-optimal solutions are known, or easily generated ask: Find the solution that est tisfies the goal test Task: Find the solution that best satisfies the goal test – State space for an IIA = set of all (proposed) solutions – Examples: VLSI layout, TSP, n -queens ot appropriate for all problems! 4 Not appropriate for all problems!
Example n- Queens problem: Put n queens on an n x n chess board with no two queens on the same row, column, or diagonal Start - 1 iteration - Goal 5 5 3 0

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Example • Traveling Salesman Problem (TSP) – Start with any path through the cities – Change two (or k) links at a time 6
• Two classes of iterative improvement algorithms – Hill-climbing – Simulated annealing • Analogy: – You are placed at a random point in some unfamiliar terrain (the solution space) and told to reach the highest peak. It is dark, and

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Lecture-6 - Artificial Intelligence CS 165A 165A Thursday...

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