Adaptive AI Engine for RTS Games

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Paper read: Call for AI Research in RTS Games

Posted by MHesham on November 2, 2010

Michael Buro. 2004. Call for AI Research in RTS Games. In Proceedings of the AAAI Workshop on AI in Games.

The paper discuss AI challenges in the real-time strategy games and presents a research agenda aimed at improving AI performance in this computer games.

RTS Games and AI Research

The current AI performance in commercial RTS games is poor. The main reasons that the AI research in RTS games is lagging behind development related fields such as classic board games:

  • RTS games feature hundreds or thousands of interacting objects, incomplete information, and fast-paced micro actions. On the other hand World-class game AI systems exist for turn-base perfect information games (e.g chess).
  • Video games companies create games under server time constraints, and don’t have the resources to engage in AI research.
  • Multi-player games do not require world-class AI performance in order to be commercially successful as long as players are more interested in on-line games.
  • RTS games are complex, which means that it is not easy to set-up an RTS game infrastructure to conduct AI experiments.

The domains where  human ability to abstract, generalize, learn, adapt and reason shine, the current RTS games fail.

Motivations for AI research in RTS games

  • RTS games constitute well-defined environments to conduct experiments in and offer a straight-forward objective ways of performance measuring.
  • RTS games can be customized to focus on specific aspects, such as local fights, scouting, resource management.
  • Strong game AI will make a different in future commercial RTS games, because graphics improvements are begging to saturate.
  • The current state of AI in RTS games is so back that there are a lot of low-hanging fruits waiting to be picked. Examples include game interface that alleviate the tedious tasks such as concentrating fire in combat.
  • Progress in AI research in RTS games is of interest for the military which uses battle simulations in training programs and purse research in autonomous weapon systems.

Research Agenda

The main goal behind the proposed research agenda is not to increase the entertainment of RTS games but to develop the strongest RTS game AI possible.

In order to repeat the success of AI in class games, the following keys are required:

  • Adversarial planning under uncertainty
    • Because the huge number of actions that had to be taken at any time and the implied complexity of RTS games, the agent can’t think at this level but in more abstracted level.
    • Agent has to search in the abstract world space and translate found solutions back into the original state.
    • All high-level decision such as what to build, when to attack are based on abstract search space augmented by beliefs about the abstract world.
    • Because the environment is hostile and dynamic, adversarial real-time planning approaches needed to be investigated, or there is no hope for RTS game AI to defeat human at commander level.
  • Learning and opponent modeling
    • One of the biggest shortcomings of current (RTS) games AI systems is their inability to learn quickly. Human players need to play a couple of games against the AI agent to exploit its style and weakness in its strategy, New efficient machine learning techniques have to be developed to tackle this problem.
    • AI would be able to discover the human player bias toward a certain unit types and strategy, and use this information to adapt its plan.
  • Spatial and temporal reasoning
    • Understanding the importance of static terrain like choke points and dynamic spatial properties such as visibility and enemy influence will influence in generating a successful plan.
    • The temporal relationship among various actions is to be understood well by the playing agent.
    • Combining terrain knowledge and simple heuristics about actions is sufficient to pick the best course of action.

Because AI is not as good as humans in planning, learning and reasoning, at least it can help humans play RTS games. However, there are numerous other ways of improving game performance which can be easily integrated in RTS game interfaces. As an example, in RTS games when attacking a group of units player has to concentrate fire or to intercept fleeing-units. This can be done by developing AI systems that handle this low-level unit management (micromanagement) and let human concentrate on the high-level decisions.

The need for an open source test-bed

Before this vision can become reality, the necessary infrastructure has to be developed. RTS game companies are reluctant to add interfaces to their products which would allow researchers to play RTS games remotely and to gauge the playing strength of RTS AI systems by means of tournaments. Therefore a free-software RTS game was developed, this game is called ORTS (Open-source RTS)

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