1. Evolutionary Computing Systems Lab (ECSL), University of Nevada, Reno 1 Authors : Siming Liu, Christopher Ballinger, Sushil Louis simingl@cse.unr.edu http://www.cse.unr.edu/~simingl

2. Outline  Motivation  Prior work  Starcraft2 (the RTS game)  Data extraction and cleanup  Methodology  Weka, machine learning toolkit  Results  Conclusions and Future Work  Acknowledgements Evolutionary Computing Systems Lab (ECSL), University of Nevada, Reno 2

3. Motivation  RTS games are good testbeds for AI research  SCII is very popular, well balanced, and  Designing a good RTS game player will advance AI research significantly (like chess and checkers did)  We can use player identification to  Learn strategies from good players  Learn counter strategies for specific player  Design better computer players (Game AI) Evolutionary Computing Systems Lab (ECSL), University of Nevada, Reno 3

4. Previous Work Evolutionary Computing Systems Lab (ECSL), University of Nevada, Reno 4

5. Starcraft II RTS games: Manage economy To build army Many types of units Each type has strengths and weaknesses Getting the right mix is key Research upgrades/abilities To destroy enemy SCII 2010, 3 mil+ sold Koreans rule and are rich Evolutionary Computing Systems Lab (ECSL), University of Nevada, Reno 5

6. Data collection and cleanup SC2 Gears and issues Because SC2 is new 1. No api (unlike SC1) 2. Very limited info But, if you do well even with limited information, how much better will you be when you have better data Noisy, limited number of replays Preliminary work: Only one player Only weka Evolutionary Computing Systems Lab (ECSL), University of Nevada, Reno 6

7. Methodology 1. Get raw data from SC2Gears 2. Extract features 3. Apply Weka methods 4. Analyze performance 5. Modify features 6. Go to 3 Evolutionary Computing Systems Lab (ECSL), University of Nevada, Reno 7 1. Decision trees 1. Important features close to root 2. Random forest 1. Best performance 3. ANNs 1. Opaque, Ok performance 4. Boosting 1. Used by random forest of decision trees 5. …

8. Results Evolutionary Computing Systems Lab (ECSL), University of Nevada, Reno 8

9. Results (round 2) Evolutionary Computing Systems Lab (ECSL), University of Nevada, Reno 9

10. Conclusions and Future Work Evolutionary Computing Systems Lab (ECSL), University of Nevada, Reno 10

11. Acknowledgements  This research is supported by ONR grants N00014-08-1-0781 and N00014-09-1-1121.  More information (papers, movies)  pippa@cse.unr.edu (http://www.cse.unr.edu/~pippa) pippa@cse.unr.edu  sushil@cse.unr.edu (http://www.cse.unr.edu/~sushil) sushil@cse.unr.eduhttp://www.cse.unr.edu/~sushil  New game engine: http://lagoon.cse.unr.edu Evolutionary Computing Systems Lab (ECSL), University of Nevada, Reno 11