Multi-Objective Optimization and Decision-Making in Context Steering

Decision-making for autonomous movement of agents is one of our research topics. In one our recent works, we have developed algorithms for such agents to simultaneously optimize several objectives occuring in their local environments. Such behaviorcan be achieved with steering algorithms, which have originally been designed for moving numerous agents simultaneously where occasional uncertainties are not noticeable by players. Nevertheless, concentrating on single individuals can reveal major flaws in their movement patterns such as oscillatory movement. For avoiding such problems, computer-game makers are forced to develop higher-level abstractions for handling game-relevant special cases. Thus, eliminating the initial benefit of steering behaviors to be highly modular, lightweight, and controllable. This work enhances the context steering approach by Fray, which introduced discretized contextual information in the aggregation of a steering behavior's components. We combine this method with multi-criteria decision-making for controlling the agent's velocity direction and magnitude.


More information here: 

  • Alexander Dockhorn, Sanaz Mostaghim, Martin Kirst and Martin Zettwitz
  • Multi-Objective Optimization and Decision-Making in Context Steering
  • Accepted at IEEE Conference on Games, 2021



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