Deliberative Learning

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 with 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 functions 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. However, architectures which learn "reflexively" have the advantages of additional simplicity and cognitive plausibility.

Architectures having this agent property include:

Go to the List of Common Agent Properties.

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