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Get A Theory of Heuristic Information in Game-Tree Search PDF

By Chun-Hung Tzeng

ISBN-10: 3642613683

ISBN-13: 9783642613685

ISBN-10: 3642648126

ISBN-13: 9783642648120

Searching is a vital approach in so much AI platforms, particularly in these AI construction structures along with a world database, a suite of creation principles, and a keep an eye on process. end result of the intractability of uninformed seek strategies, using heuristic info is important in so much looking approaches of AI structures. this significant idea of heuristic informatioD is the principal subject of this publication. We first use the 8-puzzle and the sport tic-tac-toe (noughts and crosses) as examples to assist our dialogue. The 8-puzzle involves 8 numbered movable tiles set in a three x three body. One mobilephone of the body is empty in order that it really is attainable to maneuver an adjoining numbered tile into the empty telephone. Given tile configurations, preliminary and target, an 8-puzzle challenge comprises altering the preliminary configuration into the objective configuration, as illustrated in Fig. 1.1. an answer to this challenge is a chain of strikes prime from the preliminary configuration to the aim configuration, and an optimum resolution is an answer having the smallest variety of strikes. now not all difficulties have strategies; for instance, in Fig. 1.1, challenge 1 has many strategies whereas challenge 2 has no answer at all.

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Additional resources for A Theory of Heuristic Information in Game-Tree Search

Example text

3 The *-MIN Procedure Reibman and Ballard (1983a, b) proposed a new back-up procedure, called the *-MIN procedure. The main idea in this new procedure is to model the opponent's (MIN's) behavior explicitly. In the conventional minimaxing process, it is implicitly assumed that. the opponent (MIN) is to minimize the same heuristic values as MAX is to maximize. These values are treated as if they were actual payoffs or actual minimax values. , called the predicted strength. This predicted strength is the probability that, given a choice of b moves, the opponent (MIN) will choose the nth best move over the (n + 1)th best move.

Let Q = { X;} (1 :$;; i < n) be discrete, and let P(X i) be the measure of the singleton containing Xi. Then any subset is measurable, and its measure is the sum of the measures of all points in the subset. For the whole space, the sum is always 1: 1 I P(X i ) = 1. ~i

3. Values returned by a static evaluation function at the sons of a node A. Let P be the conditional probability that A is a WIN node, given those p;'s. There are two different cases to consider. Case 1: A is a MAX node. , not a WIN) node if and only if all of its sons are LOSS nodes. Therefore, the conditional probability that A is a LOSS node is I-p=n (I-Pi) i if all of its sons are independent. The probability P of a WIN at A is (I) p=l-n (l-pJ. i Case 2: A is a MIN node. Then A is a WIN node if and only if all of its sons are WIN nodes.

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A Theory of Heuristic Information in Game-Tree Search by Chun-Hung Tzeng

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