Humans selectively process and store details about the vicinity based on their knowledge about the scene, the world and their current task. In doing so, only those pieces of information are extracted from the visual scene that is required for solving a given task. In this paper, we present a flexible system architecture along with a control mechanism that allows for a task-dependent representation of a visual scene. Contrary to existing approaches, our system is able to acquire information selectively according to the demands of the given task and based on the system’s knowledge. The proposed control mechanism decides which properties need to be extracted and how the independent processing modules should be combined, based on the knowledge stored in the system’s long-term memory. Additionally, it ensures that algorithmic dependencies between processing modules are resolved automatically, utilizing procedural knowledge which is also stored in the long-term memory. By evaluating a proof-of-concept implementation on a real-world table scene, we show that, while solving the given task, the amount of data processed and stored by the system is considerably lower compared to processing regimes used in state-of-the-art systems. Furthermore, our system only acquires and stores the minimal set of information that is relevant for solving the given task
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