Filtering Data Based on Human-Inspired Forgetting

IEEE TRANSACTIONS ON CYBERNETICS (Impact Factor: 6.22). 01/2012; 41(6):1544 - 1555. DOI: 10.1109/TSMCB.2011.2157142
Source: IEEE Xplore


Robots are frequently presented with vast arrays of diverse data. Unfortunately, perfect memory and recall provides a mixed blessing. While flawless recollection of episodic data allows increased reasoning, photographic memory can hinder a robot's ability to operate in real-time dynamic environments. Human-inspired forgetting methods may enable robotic systems to rid themselves of out-dated, irrelevant, and erroneous data. This paper presents the use of human-inspired forgetting to act as a filter, removing unnecessary, erroneous, and out-of-date information. The novel ActSimple forgetting algorithm has been developed specifically to provide effective forgetting capabilities to robotic systems. This paper presents the ActSimple algorithm and how it was optimized and tested in a WiFi signal strength estimation task. The results generated by real-world testing suggest that human-inspired forgetting is an effective means of improving the ability of mobile robots to move and operate within complex and dynamic environments.

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    • "Artificial intelligence and vision, which determine robots' understanding of the 3D world that surrounds them, also requires additional research (Costa et al., 2011). Finally, information processing, such as the ability to forget in order to purge useless information to avoid overloading sensors, must be improved to make these machines' performance consistent with their mission to operate in complex environments (Freedman and Adams, 2011). "
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