For zero-sum games, we have efficient solution techniques. Unfortunately, there are interesting games that are too large to solve. Here, a popular approach is to solve an abstract game that models the original game. We assume that more accurate the abstract games result in stronger strategies. There is substantial evidence to support this assumption. We begin by formalizing abstraction and refinement, a notion of expressive power for abstractions. We then show the assumption fails to hold under two criteria. The first is exploitability, which measures performance in the worst-case. The second is called the domination value, which measures how many mistakes a strategy makes. Despite these pathologies, we notice that larger strategies tend to make fewer mistakes and perform better in tournaments. Finally, we introduce strategy grafting, a technique that uses sub-game decomposition, which allow us to create good strategies in much larger spaces than previously possible.
| Identifer | oai:union.ndltd.org:LACETR/oai:collectionscanada.gc.ca:AEU.10048/611 |
| Date | 11 1900 |
| Creators | Waugh, Kevin |
| Contributors | Bowling, Michael (Computing Science), Schuurmans, Dale (Computing Science), Sturtevant, Nathan (Computing Science), Hooper, Peter (Mathematics and Statistics) |
| Source Sets | Library and Archives Canada ETDs Repository / Centre d'archives des thèses électroniques de Bibliothèque et Archives Canada |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Format | 694280 bytes, application/pdf |
| Relation | Abstraction Pathologies in Extensive Games, Autonomous Agents and Multi-agent Systems 2009, Strategy Grafting in Extensive Games, Neural Information Processing Systems, 2010 |
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