ARIMAA: developing a higher ranked fall back move generator using a relational database

Our approach to playing the game of Arimaa understands that the game was created to showcase the limits of brute force computing power. Using a relational database, we will be able to view similar situations that have already been played out, inventory a number of suitable reactions and make the best move given a number of attributes. This results in us making a decision that can theoretically result in a win, and hopefully will. Measurable positive results have been procured specifically from the area of concentration component of the research. In the area of concentration component we target a specific square on the board based on prior moves. This square becomes the focal point of our research that we develop an attribute index for. After querying the database to see if we can find a previous game that contained this exact same area of concentration, we either make a move or fall back. If we fall back, we take into account our developed shrinking method where we target specific pieces whose strengths essentially do not matter in terms of making a move. It is with this action that we have developed measurable positive results that, with further research, may amount to a permanent fixture as a better fall back move generator.

Name thesis:degree_name

M.S. Computer Science

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Computer Science

Year dc:date.available

2014

Author dc:creator

• McKee, Patrick

Contributors dc:contributor

• Tinkham, Nancy

• Game theory--Computer simulation; Games of strategy (Mathematics)
• Computer Sciences

Repository record dc:identifier

https://rdw.rowan.edu/etd/547

OAI identifier oai:identifier

oai:rdw.rowan.edu:etd-1546