On the impact of sensors update in declarative AI for videogames

Declarative methods such as Answer Set Programming show potential in cutting down development costs in commercial videogames and real-time applications in general. Sensors update is one of the major bottlenecks preventing their adoption in such dynamic domains. In this work we show a new optimized approach for the sensors update cycle deployed in our ThinkEngine, a framework in which a tight integration of declarative formalisms within the typical game development workflow is made possible in the context of the Unity game engine. ThinkEngine allows to wire declarative AI modules to the game logic and to move the computational load of reasoning tasks outside the main game loop using an hybrid deliberative/reactive architecture. In this paper, we discuss the crucial role of the sensors update cycle in the run-time performance of our framework and then we propose a new, optimized, sensors update workflow. After describing the new approach, we report about performance improvements.