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!