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We investigate heuristic search algorithms to generate high-quality micromanagement in combat scenarios for real-time strategy (RTS) games. Macro- and micromanagement are two key aspects of RTS games. While good macro helps a player collect more resources and build more units, good micro helps a player win skirmishes and battles against equal numbers and types of opponent units or win even when outnumbered. In this paper, we use influence maps and potential fields as a basis representation to evolve short-term positioning and movement tactics. Unit microbehaviors in combat are compactly encoded into 14 parameters. A genetic algorithm evolves good microbehaviors by manipulating these 14 parameters. We compared the performance of our evolved ECSLBot with two other state-of-the-art bots, UAlbertaBot and Nova , on several skirmish scenarios in a popular RTS game StarCraft . The results show that the ECSLBot tuned by genetic algorithms outperforms UAlbertaBot and Nova in kiting efficiency, target selection, and fleeing. Further experiments show that the parameter values evolved in one scenario work well in other scenarios and that we can switch between preevolved parameter sets to perform well in unseen scenarios containing more than one type of opponent unit. We believe our representation and approach applied to each unit type of interest can result in effective microperformance against melee and ranged opponents and provides a viable approach toward complete RTS bots.

Micromanagement is a very important aspect of real-time strategy (RTS) games. It involves moving single units or groups of units effectively on the battle field, targeting the most threatening enemy units and use the unit's special abilities when they are the most harmful for the enemy or the most beneficial for the player. Designing good micromanagement is a challenging task for AI bot developers. In this paper, we address the micromanagement subtask of positioning units effectively in combat situations. Two different approaches are evaluated, one based on potential fields and the other based on flocking algorithms. The results show that both the potential fields version and the flocking version clearly increases the win percentage of the bot, but the difference in wins between the two is minimal. The results also show that the more flexible potential fields technique requires much more hardware resources than the more simple flocking technique.

International audience; This paper showcases the use of Bayesian models for real-time strategy (RTS) games AI in three distinct core components: micro-management (units control), tactics (army moves and positions), and strategy (economy, technology, production, army types). The strength of having end-to-end probabilisticmodels is that distributions on specific variables can be used to inter-connect different models at different levels of abstraction.We applied this modeling to StarCraft, and evaluated each model independently. Along the way, we produced and released a comprehensive dataset for RTS machine learning.

Real-time strategy (RTS) game play is a combination of strategy and micromanagement. While strategy is clearly important, the success of a strategy can depend greatly on effective micromanagement. Recent years have seen an increase in work focusing on micromanagement in RTS AI, but the great majority of these works have focused on policies for individual units or very specific situations, while very little work has aimed to address the need for a broadly applicable structure for unit group coordination. This paper conceptualizes RTS group level micromanagement as a multiagent task allocation (TA) problem, and proposes the micromanagement task allocation system (MTAS) as a framework to bridge the gap between strategy and unit level micromanagement. MTAS fits into the common layered RTS AI structure, and can thus, in principle, be integrated into any existing system based on this layered structure. We describe the integration of MTAS into E323AI (an existing AI player for the spring RTS engine), and provide empirical results demonstrating that MTAS leads to statistically significant improvement in performance on the popular RTS game Balanced Annihilation.