1. Algorithms for use with Foosbot By: Michael Meadows Assisted By: James Heard

2. Goals Creation of distinct evolutionary solutions to AI for offense and defense on the table Established method to maximize the tracking efficiency

3. Previous Functional Foosbots German Made Kickstar (20,000 Euro)‏ -Used tracking from various angles University of Illinois -Non evolutionary fixed algorithms -Same Webcam (MS Life Cam)‏

4. Original Scope and Definition of Success Basic success if the casual player and be beaten more than 10% of the time. Full success at 40% Secondary success if after primary objective there is still a trend of improvement

5. Current Scope Able to track ball to within ½ diameter with less than.5 second delay.

6. Design Success Criteria Can track game in real time and maintain ball position Can track with resolution at least ½ of ball diameter or better Can predict next location with 80% accuracy

7. Expected Results Early results: –Map of balls known paths –Basic reel movement Later results: Capacity to move into balls path Consistent reactions

8. Timeline 2 nd Quarter Functional camera tracking Path memory 3 rd Quarter Basic defense 4 th Quarter Testing and expansion

9. Languages Processing for interpretation of Video input Java for managing the other relevant algorithms C for additional integration int searchColor1 = color( 128, 255, 0 ); int searchColor2 = color( 255, 0, 0 );

10. Tracking Tweaks Customized the resolution Limited the search High contrast colors, larger accepted color range.

11. Defense Overview Recorded path history (Map)‏ Averaging of risk and likelihood Tweaks – Fake outs

12. Limitations Ball tracking has some delay Reel control has imperfections Inability to improvise makes offensive dribbling likely unachievable

13. Testing and Evaluation The first stage will be judged by viewing the created and printed Map Will also be evaluated based on speed and accuracy of the ball being tracked.

14. Results Research confirms practicality of project Largest limitations in tracking speed Strong difficulty with offense AI confirmed

15. Changes to Plan Offense will not be developed as originally planned. Physical integration testing will not be feasible due to funding cuts.

16. Conclusions Major physical limitations Delays in reading writing to the map