Human-inspired robotic forgetting: Filtering to improve estimation accuracy
Perfect memory and recall provides a mixed blessing. While flawless recollection of episodic data allows for increased reasoning, photographic memory can hinder a robot's ability to operate in real-time dynamic environ-ments. Human-inspired forgetting methods may enable robotic systems to rid themselves of out-dated, irrelevant, and erroneous data. This paper presents the ActSimple al-gorithm and an associated experimental analysis. The Act-Simple algorithm is a novel approach to improving robotic performance by filtering data available to existing algo-rithms. The experimental analysis tested the effectiveness of five forgetting algorithms in a WiFi signal strength es-timation task. The results suggest that forgetting can im-prove estimation accuracy while reducing the number of sensor readings required. The simplified version of Act-Simple outperformed the other forgetting methods and ap-pears to be a flexible and adaptable means of incorporating human-inspired forgetting into robotic systems.