| dc.rights.license | CC-BY-NC-ND | |
| dc.contributor.advisor | Bodlaender, Hans | |
| dc.contributor.author | Groeneveld, Niels | |
| dc.date.accessioned | 2023-10-31T00:00:55Z | |
| dc.date.available | 2023-10-31T00:00:55Z | |
| dc.date.issued | 2023 | |
| dc.identifier.uri | https://studenttheses.uu.nl/handle/20.500.12932/45453 | |
| dc.description.abstract | Games like Chess, Go or Tic-Tac-Toe have been around for a long time.
But programmable computers have only been accessible for the last 50 years.
One of the areas that has been researched, is the science of rational decision making (e.g. decision theory) while other rational actors are also making decisions, and impacting the world (e.g. game theory).
The pursuit of letting computers mimic rationality has also been applied to games like Chess, through solving the game’s underlying game tree.
In this work, we introduce MTD-H, an alteration of the MTD-bi algorithm, used for solving game trees for games like chess.
We evaluate a chess engine implementing this MTD-H algorithm, and compare it to other solving methods as well. | |
| dc.description.sponsorship | Utrecht University | |
| dc.language.iso | EN | |
| dc.subject | It introduces a way to solve game trees, that build upons previous work. This new method introduces a game tree's conspiracy number (a property of a game tree), to calculate the final outcome of the game tree with less work (it needs to look at less nodes in the game trees). | |
| dc.title | MTD-H: Using Conspiracy Numbers To Solve Game Trees In A More Informed Manner | |
| dc.type.content | Master Thesis | |
| dc.rights.accessrights | Open Access | |
| dc.subject.courseuu | Artificial Intelligence | |
| dc.thesis.id | 25593 | |
