Recent advances in the field of educational technology have promoted the re-purposing of entertainment-oriented games and software for educational applications. This thesis extends a project developed at Texas A&M University called Room 309, a re-purposed modification of Valve Software’s Source Development Kit that models classroom scenarios to pre-service teachers. To further explore effectiveness in the area of re-playability, this work incorporates emergent game behaviors and environments using cellular automata, randomness, and influence maps within the existing nonemergent structure. By introducing these qualities game play is expected to become less predictable, thus increasing the effectiveness of Room 309 as a learning tool.

This thesis will cover the use oftheinfluence map technique applied to the retro game Ms. Pac-Man. A game thatis easy to learn but hard to master. The Ms. Pac-Man controller is implemented with five main parameters that alters the behaviour of the controller while the Ghost controller have three parameters. The experimental results of the controllers is explored to using the alterations of the parameters to find its peak of performance. The conclusion from using the influence map for this game shows that you can easy achieve a certain degree of success fairy easily but as with the game itself it is hard to master same goes for developing a sophisticated controller for this game.

Context An influence map and potential fields are used for finding path in domain of Robotics and Gaming in AI. Various distance measures can be used to find influence maps and potential fields. However, these distance measures have not been compared yet. ObjectivesIn this paper, we have proposed a new algorithm suitable to find an optimal point in parameters space from random parameter spaces. Finally, comparisons are made among three popular distance measures to find the most efficient. Methodology For our RQ1 and RQ2, we have implemented a mix of qualitative and quantitative approach and for RQ3, we have used quantitative approach. Results A* distance measure in influence maps is more efficient compared to Euclidean and Manhattan in potential fields. Conclusions Our proposed algorithm is suitable to find optimal point and explores huge parameter space. A* distance in influence maps is highly efficient compared to Euclidean and Manhattan distance in potentials fields. Euclidean and Manhattan distance performed relatively similar whereas A* distance performed better than them in terms of score in Ms. Pac-Man (See Appendix A).