Comments on our code:
Our team was comprised of two agents that both worked exclusively in an offensive mode. We extended the provided reflex agent, using seven unique features to score each state. These included the successor state’s score, total distance to food from the agent, minimum distance to food from the agent, total distance to enemy ghosts from the agent, max distance of enemy to food from any other enemy food, total scared times of enemy ghosts, and distance between teammates. A convex hull approach was used in determining maximum distance between foods to reduce calculation time. A parallelized genetic algorithm was then implemented to determine an optimal weighting function for these features. Fitness was determined by pairing each weight function against the provided baselineTeam.

Comments on the game play:
Team Un did not have enough time to prepare, and we helped them with some minor debugging before competing. Four of the five games ended by time running out. This was due to the fact that team Un’s defensive ghost agents never actually were able to capture either of our offensive pacmen as well as our pacmen being too “scared” of the ghost defenders. This resulted in our pacmen standing still and their ghosts moving back and forth between three or four spaces in close proximity of one of our pacmen. We believe that the combination of our weight on distance from ghosts in addition to our weight for distance from teammates caused this. With that said, we did find a bug (which can be found in the Angel forums) that might exacerbate or even create this problem.