Playing Michael Jackson's Moonwalker using Human Level Artificial Intelligence)

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This video shows a robot playing Moonwalker.  There are no sound in 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.

Moonwalker is a game where the player has to navigate in an unknown environment to rescue children.  This robot is playing this level for the first time and has no idea what the level environment looks like.  He uses logic to plan out optimal routes to travel to do his searches.  The player (the robot) will try not to travel in the same areas twice.  The goal is to rescue the children in the fastest time possible.

Along the way, the player encounters enemies.  In order to avoid getting hit or mobbed by a gang, the robot has to kill enemies.  Avoiding enemies is a stupid move because they can always turn around and follow you.  By killing the enemy it eliminates the threat all together.  Also, the game is programmed so that enemies are consistently attacking the player.  In order to survive, the player has to kill enemies.  If he doesn't, the enemies will keep coming and gang up on the player, resulting in death.

In the video, the robot is actually doing multiple tasks at the same time:  killing enemies, rescuing children, and navigating in an unknown environment.  He is making decisions every second that will satisfy all 3 tasks.  Sometimes he might do 2 tasks or 1 task, and it really depends on the current situation.  Other times, he might abandon a committed action/s and generate a new action.  The robot will always pursue actions that will accomplish his goals.

In this gameplay, the environment is broken up into 4 levels.  The main level, level2, level3, and level4.  In the robot's mind he has to visualize all the levels and where these levels are in 3-d space.  In each level there are teleporting pods located in various places.  The robot has to understand where each teleporting pod takes him.  The robot does this by playing the game, roaming around, and testing where certain pods take him.  For a medium sized environment like this, it is easy for the robot to use his mind to map out the environment.  However, when playing a game like Metroid where the environment is massive, the robot needs a map to identify the environment.  Thus, this robot is able to play any navigation game, regardless of how complex the environment is.

In games like Metroid or Zelda, the environment is endless and maps are available to players.  In order to navigate in such a complex environment, the robot has to generalize information on a map, write mental notes on the map, zoom in on certain areas on the map, and use logic to identify places in the map.  The more recent versions of Zelda have multiple layers of a map.  For example, a map can have 2-3 different worlds.  In each world, people, places, and things are different.  The robot has to manage this complex environment in his head.  The same goes with 3-d complex environments.  The robot simply has to interpret the environment as 3-dimensional and it has an added up or down dimension.