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What's the Problem?

Before discussing ways to prevent or cure problems with adaptive AI, we must first identify them. Picking out problems involves a combination of external observation and analysis of the internal workings of the system.

No Learning

Symptom: Learning does not occur, or happens inconsistently.

Example: The decision tree for weapon selection usually selects the weakest weapon, despite better alternatives being available.

Diagnostic: The model or the implementation is (partly) broken.


  • Debug the code and design.

  • Verify the source code, comparing it to the theory.

  • Validate the model by step-through analysis.


Symptom: The learning does not match specific results, or degenerates over time.

Example: The reinforcement learning animat does not retreat when it has low health, but instead attempts heroic attacks.

Diagnostic: The system is not equipped to reliably provide the desired control.


  • Use explicit ways to control the learning with supervision.

  • Design an architecture to deal with the control problem without learning.

  • Limit learning to other subsets of behaviors or actions.

  • Decrease the learning over time as performance reaches satisfactory levels.


Symptom: Learning does not reach the perfect result.

Example: The average error of a neural network used for target selection is high.

Diagnostic: The design does not assist the adaptation; the system relies on optimality.


  • Design the system such that suboptimality is not a problem.

  • Provide hints to the learning by example (supervision) or guidance (feedback).

  • Model the problem better so it's easier to find the best solution (for instance, expert features).


Symptom: The behaviors are not realistic enough during the adaptation or at the end of the learning.

Example: Learning to aim causes the animat to spin around in circles for a few seconds.

Diagnostic: There is too much to learn; the policy is not designed for realism; the actions are inappropriate.


  • Learn as much as possible offline.

  • Select a better policy that rewards safe exploration and exploitation.

  • Design the actions at a higher level to reduce the unrealistic combinations.

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