E.g...
  • Core temperature is too high
    • Required Action = lower temperature
    • What happened previously that led to a lower temperature?
      • Open a window (but only if outside temperature is lower)
      • Stop processing unnecessary actions to let the hardware cool down
      • etc
This needs some recording of what the robot does and the outcome.
  • e.g. when i opened the window the core temperature went - Sometime other conditions need to be assessed.  Opening the window when the temperature outside is lower causes core temperature to go down, however opening the window when the temperature outside is higher would cause the core temperature to go up
  • Often many things will be happening at once so it may not be clear what directly causes the temperature to go down
    • If the core temperature always goes down when I open the window, then it's a high probability that it will work again
    • If the core temperature only went down once when I opened the window (out of 100 times opened) then it's unlikely that opening the window again will cause the core temperature to go down.
Unstructured Big Data principals might be needed here.  I need to investigate how to best model these types of information / relationships and then query them. This is about tracking "Cause and effect".  E.g. Performing intense activity "moving at full speed" causes battery levels to drop.  AI needs to pattern match on the data to evaluate the cause and effect. Was it the intense activity or something else? AI can use a history of these cause and effects to predict future effects based on chosen activities. Also, changes to the "System" need to be monitored, such as installing a bigger battery means that the system can perform that intense activity for longer before the battery runs down.