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Experimental Iterated Competition with Artificially Intelligent Go Agents

David Liepmann Professor Cass, Advising. Experimental Iterated Competition with Artificially Intelligent Go Agents. The Game and GNU Go. 19x19 board Two players Uniform pieces, at intersections Goals: Territory and Capture Complexity through simplicity Next big AI challenge.

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Experimental Iterated Competition with Artificially Intelligent Go Agents

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  1. David Liepmann Professor Cass, Advising Experimental Iterated Competition with Artificially Intelligent Go Agents

  2. The Game and GNU Go • 19x19 board • Two players • Uniform pieces, at intersections • Goals: Territory and Capture • Complexity through simplicity • Next big AI challenge • GNU Go: open source, highly ranked Go AI • 4-phase move decision: • Understand • Candidate moves • Territory evaluation • Strategic evaluation • No fullboard lookahead

  3. Project Structure • Play original and modified against each other • 4 versions: original, 3 modified • 1200 games, 100 each of: • O vs. 1, O vs. 2, O vs. 3 and • 1 vs. 2, 1 vs. 3, 2 vs. 3 and reverse of each • Merge and randomize game results into one list • Analyze list with ELO statistical method • Based on probability to win for that pair of ratings • Simple score method, used with many similar games • Simplification of performance to results, not moves • Only considers win/loss/draw, not point differential ELO System: Rn = Ro + C * (S - Se) whereas: Rn = new rating Ro = old rating S  = score Se = expected score C  = constant

  4. My Modification(s) • Shared modification: • Surroundedness of disconnected groups • Convex hull  “snugness” of fit • Ternary (int) or continuous (float) • Directly affects: escape routes, board comprehension, life-death evaluation • Individuated tweaks • Threshold values for special-case changes to surround variable • Example: opponent groups in the expanded convex hull affect surround_status; if it is overvalued, surround_status needs reduction • Example: special position situations • 1 used ¾, 2 used 2/3, 3 used ½.

  5. Results • Overall: POOR • Guesses: • First-move advantage intensification? • Failure at unknown special case? • ELO analysis useful • Tweaking aspect of project de-emphasized

  6. More Results

  7. Even More Results

  8. All is Not Lost • Learned UNIX, Perl, experimental methods, analytical methods, difficulties in contributing to existing large-scale software projects... • Further work: • Locating specific problem case may yield results • Finely-grained variables may still be viable • Broader knowledge of go is vital • Traditional experimental methods Q.E.D.

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