Architectures That Learn Deliberatively
Though learning is widely
accepted as useful, and perhaps necessary, in a generally intelligent agent,
the question of what to learn is much more open to debate. It has been widely
documented that learning can increase the variety of problems that can be
accomplished as well as increase the efficiency in which the agent is able to
perform tasks. But indiscriminant learning can actually decrease the
efficiency of an agent if the learned knowledge is of low usefulness relative
to the cost of implementing the knowledge. For instance a learned piece
of knowledge may
only cover the application of a few operators but the precondition matching of
the knowledge may actually exceed that which would be required if a standard
problem search was utilized to implement the operators. This is commonly
referred to as the "utility problem" and is an important issue to consider if
learning is to be a useful component of the architecture. An architecture may
implement some sort of "utility function" that weighs the costs of
implementing a learned piece of knowledge with the actual benefits obtained by
utilizing that knowledge. If the benefits are found to outweigh the costs,
the knowledge will be stored, otherwise it will be discarded. Such an
architecture will thus utilize deliberation to improve the effectiveness of
its learning mechanisms.
The following architectures learn deliberatively:
Click here for a discussion on the
dichotomy between deliberative and reflexive learning.
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