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Billiards Adviser as a Search in a Continuous Domain with Significant Uncertainty
Thomas Mueller
Dartmouth TR2003-448

Abstract: Typical search algorithms are limited to problems in which there is a certain number of moves for any given state, and the effect of each move is well known. In order to overcome this limitation, we consider the problem of determining the optimal shot given the positions of balls on a billiards table. Our solution includes the image recognition necessary to determine each ball's position, the calculation of the optimal shot, and the presentation of that shot to the player. The focus of the paper is on the second part - determining the angle and force with which the player should attempt to hit the cue ball for each shot in order to sink all of the other balls with the fewest shots. The solution to this problem is unique from other game search algorithms in that it must take into account the infinite number of possible shots given any configuration of balls as well as the fact that the player is not likely to hit the ball exactly how he attempts to do so. We compare the performance of our algorithm with one that ignores the latter fact to show that our modifications do in fact improve performance for a search in a continuous domain with significant uncertainty.

Note: Advisor: Zack Butler


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   Thomas Mueller, "Billiards Adviser as a Search in a Continuous Domain with Significant Uncertainty." Dartmouth Computer Science Technical Report TR2003-448, May 2003.


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