Evolutionary Computing Systems Lab (ECSL), University of Nevada, Reno 1 Authors : Siming Liu, Sushil Louis and Monica Nicolascu

Outline  RTS Games  Prior Work  Methodology  Representation  Influence Map  Potential Field  Performance Metrics  Techniques  Genetic Algorithm  Case-injected GA  Results  Conclusions and Future Work 2 Evolutionary Computing Systems Lab (ECSL), University of Nevada, Reno

Real-Time Strategy Game  Real-Time  Strategy  Economy  Technology  Army  Player  Macro  Micro  StarCraft  Released in 1998  Sold over 11 million copies Evolutionary Computing Systems Lab (ECSL), University of Nevada, Reno 3  Challenges in AI research 1. Decision making under uncertainty 2. Opponent modeling 3. Spatial and temporal reasoning 4. …

Previous Work  Case based planning (David Aha 2005, Ontanon 2007, )  Case injected GA(Louis, Miles 2005)  Flocking (Preuss, 2010)  MAPF (Hagelback, 2008) 4 Evolutionary Computing Systems Lab (ECSL), University of Nevada, Reno  What we do  Skirmish  Spatial Reasoning  Micro  Compare CIGAR to GA

CIGAR 5 Evolutionary Computing Systems Lab (ECSL), University of Nevada, Reno  Case-Injected Genetic AlgoRithm  Case-based reasoning  Problem similarity to solution similarity

Scenarios  Same units  8 Marines, 1 Tank  Plain  No high land, No choke point, No obstacles  Position of enemy units  5 scenarios 6 Evolutionary Computing Systems Lab (ECSL), University of Nevada, Reno

Representation – IM & PF  Influence Map  Marine IM  Tank IM  Sum IM  Potential Field  Attractor  Friend Repulsor  Enemy Repulsor 7 Evolutionary Computing Systems Lab (ECSL), University of Nevada, Reno

Representation - Encoding  Influence Maps  2 IMs, 4 parameters  Potential Fields  3 PFs, 6 parameters  Bitstring / Chromosome  Total: 48 bits 8 Evolutionary Computing Systems Lab (ECSL), University of Nevada, Reno …… WMWM RMRM …… 48 bits eAeA cAcA Parametersbits WMWM 5 RMRM 4 WTWT 5 RTRT 4 cAcA 6 c FR 6 c ER 6 eAeA 4 e FR 4 e ER 4 IMs PFs

Metric - Fitness  When engaged, fitness rewards  More surviving units  More expensive units  Short game 9 Evolutionary Computing Systems Lab (ECSL), University of Nevada, Reno  Without engagement, fitness rewards  Movements in the right direction (1) (2) Param eters DescriptionDefault SMSM Marine100 STST Tank700 S time Time Weight100 S dist Distance Weight100

Methodology - GA  Pop. Size 40, 60 generations  CHC selection (Eshelman)  0.88 probability of crossover  0.01 probability of mutation 10 Evolutionary Computing Systems Lab (ECSL), University of Nevada, Reno

Methodology – CIGAR  Case-Injected Genetic Algorithm (CIGAR)  GA parameters are the same  Extract the best individual in each generation  Solution similarity  Hamming distance  Injection strategy  Closest to best  Replace 10% worst  Every 6 generations Evolutionary Computing Systems Lab (ECSL), University of Nevada, Reno 11

Results – GA vs CIGAR  On Concentrated scenario.  The third scenario: Intermediate, Dispersed 12 Evolutionary Computing Systems Lab (ECSL), University of Nevada, Reno

Results - Quality 13 Evolutionary Computing Systems Lab (ECSL), University of Nevada, Reno

Results - Speed 14 Evolutionary Computing Systems Lab (ECSL), University of Nevada, Reno

Best Solution in Intermediate Evolutionary Computing Systems Lab (ECSL), University of Nevada, Reno 15

Conclusions and Future Work Evolutionary Computing Systems Lab (ECSL), University of Nevada, Reno 16  Conclusions  CIGARs find high quality results as reliable as genetic algorithms but up to twice as quickly.  Future work  Investigate more complicated scenarios  Evaluate our AI player against state-of-the-art bots

Acknowledgements  This research is supported by ONR grants  N I-0860  N C-0522  More information (papers, movies)  (  (  ( 17 Evolutionary Computing Systems Lab (ECSL), University of Nevada, Reno