Adaptive AI Engine for RTS Games

Discussing the theory and practice

Architecture of Learning from Human Demonstration – 2008

Posted by Ogail on April 6, 2010

  • Demonstrations Triplets Extractor:
    • Input: nothing.
    • Description:
      • An expert will play the game.
      • Each time stamp a triplet will be generated. This triplet is consisted of <TimeStamp, GameState,SetOfActions>.
      • At the end of each time stamp we check if every goal was satisfied in that time stamp or not.
      • This process ends when the game ends.
    • Output:
      • A log file that contains a set D of demonstration triplets generated within the game.
  • Raw Plan Extractor:
    • Input: Demonstration triplets.
    • Description:
      • Generating goal matrix, a 2D Matrix, where one of the dimensions is the set of goals, and the other dimension is the set of demonstration triplets.
      • Extracting the raw plans for each goal using the algorithm described in the paper.
      • Generate raw cases that consists of <Snippet,Episode<Goal,GameState,Outcome>>.
    • Output:
      • Raw cases.
  • Ready-To-Use Cases Generator:
    • Input:
      • Raw cases.
    • Description:
      • Generate dependency graph for each snippet according to the algorithm in the paper.
      • Remove irrelevant actions.
      • Check all the plans to see if any plan Pi is a sub-plan of another plan Pj and substitute the actions in Pj that compose plan Pi with the goal of plan Pi.
    • Output: Ready-To-Use Cases.

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