Robot navigating in a complex maze using
large maps (pt 2)
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This video 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.
My robot 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.
In games 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.
As you 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 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.
1. navigate in an
3. follow rules of
4. do recursive
5. use logic to
determine next goals.
6. use common sense
to solve problems.
7. generate ideas to
8. predict the future
for selective objects.
strategies to defeat multiple enemies.
10. manage multiple
tasks at the same time.
11. find optimal
routes to travel so that repeated travel is brought to a minimal.
In one 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.