Week3 - CSE 3402 Intro to Artificial Intelligence Informed...

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•12-01-13 •1 1 CSE 3402 Winter 2012 Fahiem Bacchus & Yves Lesperance CSE 3402: Intro to Artificial Intelligence Informed Search I Required Readings: Chapter 3, Sections 5 and 6, and Chapter 4, Section 1. 2 CSE 3402 Winter 2012 Fahiem Bacchus & Yves Lesperance Heuristic Search. In uninformed search, we don’t try to evaluate which of the nodes on the frontier are most promising. We never “look-ahead” to the goal. E.g., in uniform cost search we always expand the cheapest path. We don’t consider the cost of getting to the goal. Often we have some other knowledge about the merit of nodes, e.g., going the wrong direction in Romania.

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•12-01-13 •2 3 CSE 3402 Winter 2012 Fahiem Bacchus & Yves Lesperance Heuristic Search. Merit of a frontier node: different notions of merit. If we are concerned about the cost of the solution, we might want a notion of merit of how costly it is to get to the goal from that search node. If we are concerned about minimizing computation in search we might want a notion of ease in finding the goal from that search node. We will focus on the “cost of solution” notion of merit. 4 CSE 3402 Winter 2012 Fahiem Bacchus & Yves Lesperance Heuristic Search. The idea is to develop a domain specific heuristic function h(n). h(n) guesses the cost of getting to the goal from node n. There are different ways of guessing this cost in different domains. I.e., heuristics are domain specific.
•12-01-13 •3 5 CSE 3402 Winter 2012 Fahiem Bacchus & Yves Lesperance Heuristic Search. Convention: If h(n 1 ) < h(n 2 ) this means that we guess that it is cheaper to get to the goal from n 1 than from n 2 . We require that h(n) = 0 for every node n that satisfies the goal. Zero cost of getting to a goal node from a goal node. 6 CSE 3402 Winter 2012 Fahiem Bacchus & Yves Lesperance Using only h(n) Greedy best-first search. We use h(n) to rank the nodes on open. Always expand node with lowest h-value. We are greedily trying to achieve a low cost solution. However, this method ignores the cost of getting to n, so it can be lead astray exploring nodes that cost a lot to get to but seem to be close to the goal: S n1 n2 n3 Goal cost = 10 cost = 100 h(n3) = 50 h(n1) = 200

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•12-01-13 •4 7 CSE 3402 Winter 2012 Fahiem Bacchus & Yves Lesperance A* search Take into account the cost of getting to the node as well as our estimate of the cost of getting to the goal from n. Define f(n) = g(n) + h(n) g(n) is the cost of the path to node n h(n) is the heuristic estimate of the cost of getting to a goal node from n. Now we always expand the node with lowest f- value on the frontier. The f-value is an estimate of the cost of getting to
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This note was uploaded on 02/13/2012 for the course CSE 3402 taught by Professor Yves during the Winter '12 term at York University.

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Week3 - CSE 3402 Intro to Artificial Intelligence Informed...

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