I think going about it from a complex algorithm point of view is the wrong approach.
We should, instead, be concentrating our efforts on two things - sensing, and reacting. The predictability of the reaction doesn't matter; all that matters is that the machine reacts. Everything else will need to depend on evolutionary processes, which requires a third criterion - changing reaction based on prior data.
If the previous reaction did not lead to a negative result ("negative" meaning detrimental to one or more arbitrary values), then the reaction can continue to the same stimulus. If the previous reaction, however, elicited a strong positive result, then the reaction should be encouraged. Similarly, if it triggered a strong negative response, it should be avoided.
To a degree, you could do this without any kind of "operating system," just by using sensory data as inputs in a complex circuit.
At least, that's how I would approach it. I know nothing about A.I. research.
We should, instead, be concentrating our efforts on two things - sensing, and reacting. The predictability of the reaction doesn't matter; all that matters is that the machine reacts. Everything else will need to depend on evolutionary processes, which requires a third criterion - changing reaction based on prior data.
If the previous reaction did not lead to a negative result ("negative" meaning detrimental to one or more arbitrary values), then the reaction can continue to the same stimulus. If the previous reaction, however, elicited a strong positive result, then the reaction should be encouraged. Similarly, if it triggered a strong negative response, it should be avoided.
To a degree, you could do this without any kind of "operating system," just by using sensory data as inputs in a complex circuit.
At least, that's how I would approach it. I know nothing about A.I. research.