evolutionary multi-agent systems for rts games
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Evolutionary Multi-Agent Systems for RTS Games Adrián Palacios
Introduction
• Artificial Intelligence (AI) from RTS games are easy to defeat
• Harder AI are cheating
• Classical solutions like A* and state machines are CPU intensive.
• “It’s about time” to develop new AI methods
Starcraft as test platform • One of the most popular RTS games.
• You can play three races.
• Possibly the most balanced RTS out there.
Starcraft concepts
• Liquipedia definitions:
• Micro: “The ability to control your units individually, in order to make up for pathing or otherwise imperfect AI.”
• Macro: “The ability to produce units, and keep all of your production buildings busy.”
• A good player needs to master both techniques.
• An example of good micro (NaDa vultures):
• http://www.youtube.com/watch?v=YXJ5jGCtTYA
Potential Fields
• Used for controlling agent navigation with static and dynamic obstacles.
• Force fields can be attractive or repulsive.
• Brighter tiles are more attractive.
Multi-Agent Potential Fields
• Six-step methodology for its design (Hagelbäck & Johansson).
• Thomas Willer Sandberg proposes another step for tuning.
• Seven-step methodology for its design:
• Object identification.
• Potential Fields identification.
• Charge assignation to objects.
• Charge parameters tuning.
• Granurality of time and space assignation.
• Agents of the system identification.
• MAS architecture design.
Evolutionary Algorithms (EA)
• Set of parameters = Individuals of the population.
• In each iteration, individuals are recombined and mutated.
• Better candidates obtain higher fitness function values.
• The remaining population will be stronger (Darwin’s natural selection theory).
EMAPF-based AI (fields)
• 8 potential fields identified:
• Maximum Shooting Distance attraction.
• Weapon Cool Down repulsion.
• Centroid Of Squad attraction.
• Center Of the Map attraction.
• Map Edge repulsion.
• Own Unit repulsion.
• Enemy Unit repulsion.
• Neutral Unit repulsion.
EMAPF-based AI (function)
• Fitness function:
• If game ends before running out of time, also:
EMAPF-based AI (results)
• 3 Goliaths vs. 6 Zealots:
• http://www.youtube.com/watch?v=VfI8XN91ggU
• Terran Mix vs. Zerg Mix:
• http://www.youtube.com/watch?v=hETcbgybkoc
• 3 Goliaths vs. 20 Zerglings:
• http://www.youtube.com/watch?v=Q0auIScPCYg
Conclusions
• It is possible to use EA for tuning potential field parameters.
• Trained potential fields show extraordinary results.
• They are comparable with medium-skilled/advanced players.
Future Work
• To use trained potential fields on a Full RTS scenario.
• To develop MAPF-based solutions with different algorithms.
• To study the combination of these techniques with optimization techniques for macro issues (example: BOs).
• To analyze how difficult is for humans to defeat EMAPF-based AI.
Acknowledgements
• Thanks to Thomas Willem Sandberg for making public his work and sending us the maps!