While in traditional computer game playing agents were designed solely for the purpose of playing one single game, General Game Playing is concerned with agents capable of playing classes of games. Given the game's rules and a few minutes time, the agent is supposed to play any game of the class and eventually win it.
Since the game is unknown beforehand, previously optimized data structures or human-provided features are not applicable. Instead, the agent must derive a strategy on its own.
One approach to obtain such a strategy is to analyze the game rules and create a state evaluation function that can be subsequently used to direct the agent to promising states in the match.
In this thesis we will discuss existing methods and present a general approach on how to construct such an evaluation function.
Each topic is discussed in a modular fashion and evaluated along the lines of quality and efficiency, resulting in a strong agent.:Introduction
Game Playing
Evaluation Functions I - Aggregation
Evaluation Functions II - Features
General Evaluation
Related Work
Discussion
| Identifer | oai:union.ndltd.org:DRESDEN/oai:qucosa:de:qucosa:26086 |
| Date | 22 June 2012 |
| Creators | Michulke, Daniel |
| Contributors | Thielscher, Michael, Edelkamp, Stefan, Technische Universität Dresden |
| Source Sets | Hochschulschriftenserver (HSSS) der SLUB Dresden |
| Language | English |
| Detected Language | English |
| Type | doc-type:doctoralThesis, info:eu-repo/semantics/doctoralThesis, doc-type:Text |
| Rights | info:eu-repo/semantics/openAccess |
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