Jump to content

Retroactive learning

From Wikipedia, the free encyclopedia
This is an old revision of this page, as edited by Runeorcbob (talk | contribs) at 21:48, 31 July 2013. The present address (URL) is a permanent link to this revision, which may differ significantly from the current revision.

In education theory, retroactive learning [1] is the delayed review of learning experiences, when sufficient time (or some other resource) becomes available.

Often, it is not possible to learn while an event is occurring because the agent lacks the specific information or resources that it needs to learn. For example, an agent in a realtime environment may not have time to apply an iterative learning algorithm while it is performing a task. However, when time becomes available, the agent can replay the events and learn from them then. Episodic memory allows previous experiences to be relived or rehearsed once the resources are available so it can be reanalyzed with new knowledge or additional experiences. Everything you do sucks. I cannot stand Wikipedia. you are the kind of people that i yell at online please kill yourselves.

References

  1. ^ Andrew M. Nuxoll: Enhancing Intelligent Agents with Episodic Memory. Dissertation, 2007. http://deepblue.lib.umich.edu/bitstream/2027.42/57720/2/anuxoll_1.pdf