USING CIGAR FOR FINDING
EFFECTIVE GROUP BEHAVIORS IN
RTS GAME
:
Authors Siming Liu, Sushil Louis and Monica Nicolascu
[email protected], [email protected], [email protected]
http://www.cse.unr.edu/~simingl
Evolutionary Computing Systems Lab (ECSL),
University of Nevada, Reno
1
Outline
 RTS Games
 Prior Work
 Methodology
 Representation
 Influence Map
 Potential Field
 Techniques
 Genetic Algorithm
 Case-injected GA
 Results
 Conclusions and
Future Work
 Performance Metrics
Evolutionary Computing Systems Lab (ECSL), University of Nevada, Reno
2
Real-Time Strategy Game
 Real-Time
 Strategy
 Economy
 Technology
 Army

Challenges in AI research
1.
2.
3.
4.
Decision making under uncertainty
Opponent modeling
Spatial and temporal reasoning
…
 Player
 Macro
 Micro
 StarCraft
 Released in 1998
 Sold over 11 million
copies
Evolutionary Computing Systems Lab (ECSL), University of Nevada, Reno
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Previous Work
 Case based planning (David
Aha 2005, Ontanon 2007, )
 What we do
 Skirmish
 Case injected GA(Louis,
 Spatial Reasoning
Miles 2005)
 Flocking (Preuss, 2010)
 MAPF (Hagelback,
2008)
 Micro
 Compare CIGAR to GA
Evolutionary Computing Systems Lab (ECSL), University of Nevada, Reno
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CIGAR
 Case-Injected Genetic AlgoRithm
 Case-based reasoning
 Problem similarity to solution similarity
Evolutionary Computing Systems Lab (ECSL), University of Nevada, Reno
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Scenarios
 Same units
 8 Marines, 1 Tank
 Plain
 No high land, No choke point, No obstacles
 Position of enemy units
 5 scenarios
Evolutionary Computing Systems Lab (ECSL), University of Nevada, Reno
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Representation – IM & PF
 Influence Map



Marine IM
Tank IM
Sum IM
 Potential Field



Attractor
Friend Repulsor
Enemy Repulsor
Evolutionary Computing Systems Lab (ECSL), University of Nevada, Reno
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Representation - Encoding
Parameters bits
 Influence Maps

2 IMs, 4 parameters
IMs
 Potential Fields

3 PFs, 6 parameters
 Bitstring / Chromosome

Total: 48 bits
WM
RM
PFs
WM
5
RM
4
WT
5
RT
4
cA
6
cFR
6
cER
6
eA
4
eFR
4
eER
4
……
cA
eA
1 0 1 0 1 0 1 0 1 0 1 0 1 0 ……1 0 1 1 0 0 1 0 1 0 1 0 1 0
48 bits
Evolutionary Computing Systems Lab (ECSL), University of Nevada, Reno
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Metric - Fitness

When engaged, fitness rewards



More surviving units
More expensive units
Short game
Param
eters
Description
Default
SM
Marine
100
ST
Tank
700
Stime
Time Weight
100
Sdist
Distance Weight
100
(1)

Without engagement, fitness rewards

Movements in the right direction
(2)
Evolutionary Computing Systems Lab (ECSL), University of Nevada, Reno
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Methodology - GA
 Pop. Size 40, 60 generations
 CHC selection (Eshelman)
 0.88 probability of crossover
 0.01 probability of mutation
Evolutionary Computing Systems Lab (ECSL), University of Nevada, Reno
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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
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Results – GA vs CIGAR
 On Concentrated scenario.
 The third scenario: Intermediate, Dispersed
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Results - Quality
Evolutionary Computing Systems Lab (ECSL), University of Nevada, Reno
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Results - Speed
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Best Solution in Intermediate
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Conclusions and Future Work
 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
Evolutionary Computing Systems Lab (ECSL), University of Nevada, Reno
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Acknowledgements
 This research is supported by ONR grants
 N000014-12-I-0860
 N00014-12-C-0522
 More information (papers, movies)
 [email protected]
(http://www.cse.unr.edu/~simingl)
 [email protected]
(http://www.cse.unr.edu/~sushil)
 [email protected]
(http://www.cse.unr.edu/~monica)
Evolutionary Computing Systems Lab (ECSL), University of Nevada, Reno
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