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

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: Individuated tweaks
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: ELO analysis useful
First-move advantage intensification?
Failure at unknown special case?
ELO analysis useful
Tweaking aspect of project de-emphasized 1 2 3
9940.9
9857.8
9925.9
rand1
9895.6
9847.0
rand2
9948.1
9879.1
9875.8
rand3
9970.3
9794.2
9958.0
rand4
9902.8
9881.5
9958.9
notrand
9852.1
9899.8
9964.9
ordered
sum

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.