|Institution:||KTH Royal Institute of Technology|
|Keywords:||Natural Sciences; Computer and Information Science; Computer Science; Naturvetenskap; Data- och informationsvetenskap; Datavetenskap (datalogi)|
|Full text PDF:||http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-168306|
This paper studies the performance of height-based weighing functions and compares the results to using the commonly used non height-based weighing functions for holes. For every test performed, the heuristic methods studied in this paper performed better than the commonly used heuristic function. This study also analyses the effect of adding levels of prediction to the heuristic algorithm, which increases the average number of cleared lines by a factor of 85 in total. Utilising these methods can provide increased performance for a Tetris AI. The polynomic weighing functions discussed in this paper provide a performance increase without increasing the needed computation, increasing the number of cleared lines by a factor of 3. The breadth-first search provide a bigger performance increase, but every new level of prediction requires 162 times more computation. Every level increases the number of cleared lines by a factor of 9 from what has been observed in this study.