Title: Develop an AI agent for a simple Android-based boardgame

Proposer: Hans-Wolfgang Loidl

Suggested supervisors: Hans-Wolfgang Loidl

Goal:

Implement an AI that plays one side of a simple, checkers-like boardgame that is running on Android devices.

Description:

Mobile devices increasingly attract attention as platforms for implementing boardgames. Such boardgames feature a principled game-design that offers high re-play value. An implementation on Android (or other mobile OSs) brings these games to a wide community of users, looking for simple, solitaire or networked games for entertainment.

The goal of this project is to develop an AI agent for an existing Android implementation of the game "Guerilla Checkers" [1] by Brian Train [2]. This is a checkers-like boardgame with an asymmetric player set-up, a different victory conditions for each side. The current implementation [3], by Richard Gould, allows 2 players to play the game on an Android device, however it lacks any AI component to play against the computer.

The project will proceed in the following phases.

• Literature survey on game design, machine learning and AI techniques; review of the existing game model and code base
• Design of basic AI functionality (e.g. rule based) and its interaction with the game model
• Implementation of the basic AI functionality
• Design of improved AI functionality, drawing on machine-learning techniques
• Implementation of improved AI functionality
• Evaluation of the effectiveness of the improved vs. the basic functionality

Resources required: Android platform with C# running on mono and Riak as database back-end; interaction with the existing game engine is required

Degree of difficulty: moderate

Background needed: Good general programming skills; some background in AI and machine-learning; interest in historical background is useful

References:

• [1] Guerilla Checkers and rules explained and the page on BoardGameGeek

• [2] Brian Train's web page