Comparing heuristic search methods for finding effective group behaviors in RTS game
We compare genetic algorithms against hill-climbers for generating competitive unit micro-management for winning real-time strategy game skirmishes. Good group positioning and movement, which are part of unit micro-management can help win skirmishes against equal numbers and types of opponent units or even when outnumbered. In this paper, we use influence maps to generate group positioning and potential fields to guide unit movement. We tested the behaviors obtained from genetic algorithm and two types of hill-climbing search against the default Starcraft AI using the brood war API. Preliminary results show that while our hill-climbers quickly find influence maps and potential fields that generate quality positioning and movement in our simulations, they only find quality solutions fifty to seventy percent of the time. On the other hand, genetic algorithms evolve high quality solutions a hundred percent of the time, buttake significantly longer.
Liu, Siming, Sushil J. Louis, and Monica Nicolescu. "Comparing heuristic search methods for finding effective group behaviors in RTS game." In 2013 IEEE Congress on Evolutionary Computation, pp. 1371-1378. IEEE, 2013.
2013 IEEE Congress on Evolutionary Computation, CEC 2013