Recent work in the field of game AI has been leaning towards creating agents able to play games and compete with human professionals. There exists several approaches to build such agents. Whether they are built by hand or by hard coding or using some learning techniques such as Reinforcement Learning (RL) or Genetic Programming (GP). However, Such approaches are based purely on observations.
The aim in General Game Playing (GGP) is to create programs called agents that are able to play a yet unknown game after they are given the rules. These programs must thus be as intelligent and as independent as possible to solve problems on their own. A similar research field is represented by Golog and the Situation-Calculus that are well-studied languages and allow reasoning about dynamic domains and diversified sets of problems.
All agents must be able to reason about actions. Hence one approach is to use the Situation-Calculus and Golog to represent games and to express strategies in such games. By introducing Golog to GGP we allow results and methods from both areas to be applied in GGP and vice versa. Accordingly we present an exemplary Golog setup and how to realize it in order to build a General Game Player. For this purpose we discuss how one can overcome the difficulties of translating a formalized game description to its corresponding Golog representation and show how simulations of games can be utilized with methods from Programming by Demonstration (PbD) in order to dynamically develop and maintain executable game strategies during runtime.
We show that our proof of concept agent GologPlayer is able to play turn-based games, supports most Game Description Language (GDL) features and show that it is able to beat randomly playing agents with a success rate of about 70%.
Based on the framework provided for the Angry Birds AI Competition the task is to develop and implement an agent that is capable to successfully play the game of Angry Birds.
Computer games have gained attention as a testbed for research in AI and related disciplines early already. A recent addition to the set of games that is being used in this regard is Angry Birds. It offers several interesting aspects in terms of research such as reasoning about physical effects and qualitative spatial reasoning, to name just two. The KBSG has recently engaged in this new testbed, participating as Team Akbaba in the Angry Birds AI Competition founded in 2012.
The thesis presents the implementation and evaluation of a General Game Player based on evolutionary algorithms.