Architectures that Handle Lots of Knowledge
Many real world and simulated environments are rich in detail
and other information. The ability to incrementally add knowledge
without significant slowdown is important for agents in these
environments to function.
This information can be difficult or impossible to capture during
development, so learning
is frequently employed to capture domain knowledge as the agent
experiences its environment.
The following architectures were designed to operate in a
knowledge-rich environment:
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