Phil 6.4.20

GPT-2 Agents

  • Thinking about how to parse the data to build maps.
    • Clearly, there are black and white agents (the players). Option 1 would be to simply collect the from-to points at the player level
    • One step down would be the piece family, pawns, rooks, bishops. The king and queen would be single instances
    • The most granular would be to track the individual pieces.
    • The issue I’m struggling with is when pieces pass over squares rather than through them. There is no explicit d3 when white moves the pawn from d2 to d4. Trying to think of the best way to uncover the latent information.
    • I think a good way to procrastinate about this problem is to parse the games into a database of moves. The format is always “<player/color> moves <(color)piece> from <start> to <end>. There is additional information as well (game, players, move number), but that could be added later.
    • Done
    • table_moves
    • Sent a note to Thomas Wolf at Huggingface
    • I know what I’m going to do!
      • Create probes for each piece, like:
        • white moves pawn from e2 to
        • black moves pawn from e7 to
        • A slightly more sophisticated parser will need to work with “The game begins
      • I can take the results of multiple probes and store them in the table_moves, then run statistics by color, piece, etc
      • Then see if it’s possible to connect one piece to another piece using a “from/to chain” across multiple pieces. There will probably be some sort of distribution where the median(?) value should be a set of adjacent squares.
      • The connections can be tested by building adjacency matrices by piece and by move number range

GOES

  • Hey! The VPN is much more responsive today! Logged into Influx
  • Getting the right time for the query from here
    • start: 2020-04-06 15:30:00.000
    • end: 2020-04-06 18:00:00.000
    • Need to get the right mnemonics. Pinged Bruce
  • Starting some timing tests on my local influx copy
  • The VPN has stopped again
  • 2:00 NSOF Meeting – Nice demo by Jason
  • 3:30 AIMS IRAD
    • Status. John is going part time
    • Railed against the poor VPN access to the DevLab

ML Brownbag – Aaron did a nice job

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