We study the computational complexity of adapting a reactive architecture to meet task constraints. This computational problem has application in a wide variety of fields, including cognitive and evolutionary robotics and cognitive neuroscience. We show that—even for a rather simple world and a simple task—adapting a reactive architecture to perform a given task in the given world is NP-hard. This result implies that adapting reactive architectures is computationally intractable regardless the nature of the adaptation process (e.g., engineering, development, evolution, learning, etc.) unless very special conditions apply. In order to find such special conditions for tractability, we have performed parameterized complexity analyses. One of our main findings is that architectures with limited sensory and perceptual abilities are efficiently adaptable.
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