Robot navigating in a complex maze using
large maps (pt 1)
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shows a robot navigating a complex maze using large
maps. There are no sound during parts of the video
because I wanted to show the viewers what the robot
is thinking while playing the game. The flashing
text and freeze frames are the internal thoughts of
the robot and not instruction text for the viewers.
These internal thoughts describe the details of how
the robot produce intelligence.
doesn't use: planning programs/heuristic searches (used by MIT and Stanford
University), Bayesian's probability theories for decision making, Bayesian's
equation for induction and deduction, semantic networks for natural language
understanding, predicate calculus, common sense systems, first-order logic,
rule-based systems, genetic programming, or MACHINE LEARNING.
like Metroid or Castlevania or Zelda, the player has to navigate in a
massive environment. Maps are provided to the player so that he doesn't get
lost. In this video, i'm trying to demonstrate how the robot thinks while
navigating in a large environment using maps.
can see, the robot is analyzing the map, making personal observations,
debating where to go, making decisions on how to do searches and so forth.
At the same time, he is mentally remembering general or detailed parts of
the map to do his navigation.
Navigation in an unknown environment is just one small task the robot has to
do. A real robot with human level intelligence can do multiple tasks
simultaneously. He can:
navigate in an unknown environment
follow rules of the game.
logic to determine next goals.
common sense to solve problems.
generate ideas to solve problems.
predict the future for selective objects.
generate strategies to defeat multiple enemies.
manage multiple tasks at the same time.
optimal routes to travel so that repeated travel is brought to a minimal.
scene, the player was stuck and didn't know what to do. He needed to raise
the elevator in order to go to the top level. The robot tried 3 ideas
before solving the problem. This ability to generate ideas to solve
problems is very important to human intelligence. It shows the robot can
adapt and change so that a problem can be solved.