Accomplishing recursive tasks using
Human-Level Artificial Intelligence
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This video shows a robot
solving a problem by doing recursive tasks. There are
no sound in this video because I wanted the viewers
to focus on the robot's thoughts while solving a
problem.
When the robot does a
task, such as play a videogame, there will be
recursive tasks the robot has to do. In this example,
the robot is trying to reach the top of a building to
rescue mario junior. In order to do that, from the
first floor, the robot has to get to the second floor
and then to the third floor to rescue mario jr. Each
floor the robot navigates is considered a sub-task of
the overall task. Within each sub-task are even more
tasks.
To make things even
more complex, the robot has no knowledge about the
objective of the level or the rules of the level. He
has to use common sense knowledge in order to
discover the strategies to pass the level. In the
video, I show that the robot is using some ideas that
leads to dead ends. After numerous trial and error,
the robot finds the linear steps to navigate from
floor to floor.
If you analyze this
video further, the robot is using a much more
sophisticated way of solving problems than recursive
tasks. Tasks are interconnected to each other,
previous tasks done are revisited, some deep
recursive tasks are forgotten, some tasks are bypass
because of better methods found, and other tasks are
ongoing, etc. The robot's conscious creates an
optimal computer program to manage multiple
simultaneous tasks. This computer program manipulates
task in the task container using different data
structures (not just recursive tasks). The more the
robot plays a level the faster the level can be
solved.
