Learning Information Acquisition for Multitasking Scenarios in Dynamic Environments

Real world environments are so dynamic and unpredictable that a goal-oriented autonomous system performing a set of tasks repeatedly never experiences the same situation even though the task routines are the same. Hence, manually designed solutions to execute such tasks are likely to fail due to such variations. Developmental approaches seek to solve this problem by implementing local learning mechanisms to the systems that can unfold capabilities to achieve a set of tasks through interactions with the environment. However, gathering all the information available in the environment for local learning mechanisms to process is hardly possible due to limited resources of the system. Thus, an information acquisition mechanism is necessary to find task-relevant information sources and applying a strategy to update the knowledge of the system about these sources efficiently in time. A modular systems approach may provide a useful structured and formalized basis for that. In such systems different modules may request access to the constrained system resources to acquire information they are tuned for. We propose a reward-based learning framework that achieves an efficient strategy for distributing the constrained system resources among modules to keep relevant environmental information up to date for higher level task learning and executing mechanisms in the system. We apply the proposed framework to a visual attention problem in a system using the iCub humanoid in simulation.