1. GoogolHex CS4701 Final Presentation Anand Bheemarajaiah Chet Mancini Felipe Osterling

2. Problem Definition Build an intelligent agent that can play the 2-player modified Chinese Checkers game. The board setup consists of individual tiles that are hexagonal and a set of pieces that must move from their start positions by moving individually or “jumping” one another to a set of end positions. The motivation for this project is to implement and evaluate various AI ideas into an intelligent agent that plays the game against other intelligent agents.

3. Summary - Tournament Performance Our agent has performed at about the average or slightly below.

4. Method - Board Representation Since the Greedy player provided implemented the same design concepts we specified in our initial presentation, we decided to only slightly modify the provided Board object. We added some helper methods, and we added an extra jump function that maintained a depth counter of the current depth of a player.

5. Method - Agent As specified in our initial presentation, we decided to continue with an AB-pruned Minimax adversarial search to allow us to find optimal moves within a reasonable visibility.

6. Limiting Minimax Search Clearly, a basic Minimax search would explode far too quickly with the size of the board and number of moves. We still identified an adversarial search as the most straightforward solution to find intelligent moves.

7. Limiting Minimax Search (cont) Limit the search depth to 6. Don't bother searching tree of the two backwards-oriented directions from each peg. Don't bother moving towards sides of board. After realizing a depth of 4 was far preferable, we decided to limit depth to 2, and continue search again of depth 2 on most optimistic paths. This gets a functional depth of 4 without the time complexity (and a small optimality tradeoff risk).

8. Method - Search Heuristic Design We use a composite heuristic by weighting and summing the following weighted sub-heuristics: Simple - Distances to the farthest free slot, using the Board.dist(...) implementation provided. Distance - The total Euclidean distance from the goal (i.e. the sum of the Euclidean distances of each player from the goal) Total Moves - Inverse of total number of moves taken (making a move subtracts) Marbles At Goal - Strongly weight actually getting marbles into goal slots.

9. Related Work Our board representation ideas came from Paula Ulfahake at Lund University (who also implemented her agent with a modified Minimax search)

10. System Architecture OOP Design GoogolHexPlayer extends standard player interface. Move Logic with Strategy Pattern. Provide standard "getMove" interface method for strategies. Provide standard "getValue" inteface method for heuristics.

11. Experimental Evaluation In our testing our agent consistently won against the greedy player by a considerable margin--always several moves ahead.

12. Shortcomings/Future Work We implemented a neural network, but were not able to use it effectively in our approach. We would like to further investigate different evaluations and classifications of training data in order to optimize our performance or experiment in more detail in learning algorithms.

13. Shortcomings/Future Work (cont) Our project would have been much easier to test and debug with a unit test framework we could use against the board and heuristic methods. This is a somewhat involved setup process and learning curve for JUnit and Eclipse, but it could have been quite beneficial.

14. Acknowledgements We would like to thank our TA, Jason Yosinski for his assessments and suggesions during the project.

15. Conclusion In this project we have developed an agent that fulfills our goals, attempted a variety of AI-related strategies, and ended up with a solid search for finding good moves in Chinese Checkers.