Interactive Evolutionary Computation for Strategy Discovery in Multi-Phase Operations

Complex adversarial operations typically involve the allocation of finite resources to meet a set of objectives over a number of phases. This poses a challenge for AI-based strategy discovery. A strategy for one phase cannot be developed in isolation as the resources available in any one phase are dependent on the outcome of previous phases. Our proposed solution is to combine an evolutionary algorithm search with human-guided evaluation. The approach uses simulation-based fitness evaluation, where a human operator can view the fittest solution after every set number of generations. The operator can 'lock in' strategies for particular phases, and 'suggest' alternative strategies to guide further evolution. Key to our approach is a representation encoding that allows relative proportions of resources to be represented where actual levels may not be known a priori. We evaluate our solution on a three-phase scenario of a real-time strategy game and compare the effectiveness of strategies that were purely human-devised, purely evolved, and those resulting from the human-evolution collaboration. The collaborative approach shows promising results in being able to find an optimum solution earlier.