1. 1 Phase II - Checkers Operator: Eric Bengfort Temporal Status: End of Week Five Location: Phase Two Presentation Systems Check: Checkers Checksum Passed Status: Systems Functional

2. 2 Previously on Eric’s PowerPoint… Neural Network – Learns an Arbitrary Function. And now the conclusion: Today’s Episode… Checkers!

3. 3 The Goal Use my Neural Network to teach a computer how to play checkers intelligently against a human. The Why Something tangible / Hands on User can care about the algorithm, or not Somewhere between Tic Tac Toe and Chess I don’t like Chess

4. 4 The How y = (x1 + x2) / 100 That was then… and this is now ??? *No clearly defined inputs. *What kind of function anyway!?

5. 5 The Master Plan On computer’s turn: Generate Possible Moves (just through the rules of the game) Resolve each Possible Move, giving us a resulting board Somehow determine how favorable each resulting board is Have computer make the move which resulted in the most favorable board, thus making the best possible move. Neural Network to the Rescue! Create a Neural Network which takes a “board” (as input) and returns a value (as output) of how favorable said board is to the computer player.

6. 6 AI Brain Mach One My Proposed Inputs Number of Black Pieces Number of Black Kings Number of Jumps Black can make Number of Black Edge Hogs Number of Black Pieces in the Fray Number of White Pieces Number of White Kings Number of Jumps White can make Number of White Edge Hogs Number of White Pieces in the Fray Training Data Sample

7. 7 But where does Training Data come from!? *Probability Theory *Adaptation of tried method

8. 8 Randomized Self Play My Revision Half a million games per board! 7 to 10 minutes to make just one Training Data Instance! Hijacked Computers Used my Phase I to generate a Neural Network and saved it!

9. 9 Let’s Fire up the Demo Development Features! Features! Features! Findings Decisions Results