Universal Algorithm for Creating A Small Scale Reusable Simulation Data in Real-time Strategy Games

Real-time strategy games are of such high complexity that consideration of trying to brute force all actions and states is not only impractical, but impossible. Approximations, information abstractions, and models are, therefore, the necessity when creating game bots that play this genre of games. To create such bots, the detailed data is needed to base them on. This article introduces a universal algorithm that creates reusable simulation data of one attacking unit on a building and tests the feasibility of doing such a task. This paper concludes that capturing all relevant data in a sub-segment of real-time strategygames is feasible. Gathered data holds valuable information and can be reused in new research without the need of repeating the simulations

On the Complexity of Two-Player Attrition Games Played on Graphs

The attrition game considered in this study is a graph based strategic game which is a movement-prohibited analogue of small-scale combat situations that arise frequently in popular real-time strategy video games. We present proofs that the attrition game, under a variety of assumptions, is a computationally hard problem in general. We also analyze the 1 vs. n unit case, for which we derive optimal target-orderings that can be computed in polynomial time and used as a core for heuristics for the general case. Finally, we present small problem instances that require randomizing moves — a fact that at first glance seems counter-intuitive