Absent-minded Robots Remember What Matters
Robots could mimic human forgetfulness to filter out less useful information.
We are constantly inundated with
new information, and to manage it effectively it’s sometimes necessary to forget old, irrelevant memories.
Researchers at Vanderbilt
University have now developed an algorithm that mimics this kind of forgetfulness
in robots, as a way to filter out less useful information.
“Forgetting is a critical
capability when operating in dynamic environments,” says PhD student Sanford
Freedman, who presented
the group’s data filtering-software, called ActSimple, in a paper published at
the IASTED Robotics and Applications conference held
this week in Cambridge,
MA.
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ActSimple draws on two facets of human
memory: time-based decay, or the way that memories disappear over time,
and interference, which is the failure to recall information due to other memories competing
for attention. ActSimple assigns different pieces of data values
depending on how often they are used, and how similar it is to other pieces
of information.
To test the software, the
researchers used it to control a simulated robot that measured the strength of
WiFi signals in a virtual environment. The robot recorded WiFi readings on a scale
of 1-100, as it moved through the virtual setting and these WiFi readings also had different levels of noise (errors) associated with them. At intervals, the robot relied on its memory to create an estimated WiFi signal map by
recalling signal strength information it had gathered and stored. The
researchers tested ActSimple against four other algorithms,
including one that strictly disregarded the oldest information, and another
that out filtered random information.
The Team found that on average,
ActSimple created the most reliable estimated WiFi map. Interestingly, when the robot “remembered”
everything–that is, used all of its gathered information (errors and all)–it generated
the least accurate map overall.
