1 / 14

CC+

CC+. Swinburn Miranda – Computer Science William Moseson – Engineering Physics Ningchuan Wan – Computer Science. Introduction. Goal: To develop AI that plays Chinese checkers at a high level.

arthur-harris
Download Presentation

CC+

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. CC+ Swinburn Miranda – Computer Science William Moseson – Engineering Physics Ningchuan Wan – Computer Science

  2. Introduction • Goal: To develop AI that plays Chinese checkers at a high level. • Developed an algorithm to make intelligent moves based on heuristic based searches and learning. • Motivated by experience gained and desire to win.

  3. Method - Heuristic • Distance Max • Total distance of our pieces from the starting point • Penalizes stragglers • Penalizes pieces outside a certain width threshold

  4. Method – Strategy Adjustment Endgame • Adjust the search type being used based on the point in the game. Midgame Opening

  5. Method – Strategy Adjustment • Opening • BFS is applied to move max pieces furthest • Mid Game • BFS for yourself as well as the opponent • Enabling blocking & long moves • End Game • Finding the shortest path to the goal

  6. Method – Gray Piece Placement • Block an opponent’s ladder • Block an opponent’s chain • Block a potentially large jump • Help your pieces at the end of the game

  7. Method - learning • Game logs are used for learning • A parser converts the game log into a feature vector • Feature Vector<y= {1,-1}> <Current_BoardPostionofPiece>:<{0,1,2,3}> … <Next_BoardPostionofPiece >:<{0,1,2,3}>… • The feature vector represents a transition from current state to next • Y is assigned based on whether players transitions led to a final win or no. • SVMLight is used for training and prediction

  8. System Architecture(Proposed) Decision module I/O module Communication Server Knowledge base Log Learning Engine

  9. System Architecture • Decision Module • Evaluates a board state • Outputs a move • Learning Engine • Influences the Decision Module based on historical results • Knowledge Base • Contains information obtained from previous games • Data Sources • Training data

  10. Experimental Evaluation: Methodology • Evaluation of our software is based on its performance • Variables: • Heuristic used • Search Depth • Heuristic weighting • Gray piece placement • Search type

  11. Experimental Evaluation: Results • Tournament Results: • Consistently in 5th , 6thor 7thplace. • Wins 100% of the time against greedy.

  12. Future Work • Learning • Training data size • Heuristic • Include more variables • Gray Pieces • More intelligent placement • More analysis of when and where to place

  13. Conclusions • Developed a AI that out performs a greedy strategy • Gained a better understanding of AI and Machine learning

  14. The Team • Swinburn Miranda – Learning Module • Ningchuan Wan – Heuristic Development • Will Moseson – Gray Piece Placement and testing

More Related