Multi-Objective Optimization and Decision-Making in Context Steering
Alexander Dockhorn, Sanaz Mostaghim, Martin Kirst, Martin Zettwitz · 2021 IEEE Conference on Games (CoG) · 2021
This work concentrates on decision-making for autonomous movement of agents to simultaneously optimize several objectives which occur in their local environment. Such behavior can 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, 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. The resulting approach is tested based on selected scenarios which show that the resulting approach is well suited to improve the agent's smooth and natural movement. Based on our observations we propose suitable parameterizations of the designed method and discuss advantages and disadvantages of made enhancements.