Othello Sean Farrell June 29, 2015

Othello Two-player game played on 8x8 board All pieces have one white side and one black side Initial board setup is shown right, with valid moves marked as dots

The Evolution of Strong Othello Programs Michael Buro, 2003 First computer Othello tournaments in 1979 In 1980, first time a World-champion lost a game of skill against a computer 6-0 defeat of then human World-champion in 1997

Evaluation Function Evolution Mini-max search used to estimate the chance of winning for the player with current move Construct function using features of board state that correlate with winning Important features  Disc stability – stable discs cannot be flipped  Disc mobility – move options  Disc parity – last move opportunities for every empty board region

IAGO (1982) Classic, hand-crafted evaluation function Features were chosen based on analysis of Othello  Edge Stability & Internal Stability  Current Mobility & Potential Mobility Evaluation parameters were done manually

BILL (1990) Partly pattern based, feature weights are learned  Edge Stability  Current Mobility & Potential Mobility  Sequence Penalty Evaluation speed increased by using pre- computed tables In comparison, BILL wins all games against IAGO, with 20% the thinking time

Logistello-1(1994) Pattern values learned independently from sample data Uses logistic regression to combine features  Current Mobility & Potential Mobility  Patterns

Logistello-1(1994) Evaluation function is entirely table based All evaluation parameters are learned from sample positions Logistello uses selective search heuristic called ProbCut Dominated computer Othello until 1996

Logistello-2(1997) Joint learning of pattern values Assigning pattern values independently neglected pattern correlations Can assign arbitrary values to patterns whose meaning is not bound by limited human understanding of the problem Strength increase from 1994 version is ten- fold

Other Important Improvements Opening books  Saves time  Avoids falling into known strategic traps  Less chance of losing two games in the same way End game search  Allows for optimal play near end

Takeshi Murakami vs. Logistello Michael Buro, 1997 Both humans and computers have improved playing Othello Against imperfect human players it pays off to complicate endgame positions, not so with strong Othello programs

Human vs. Computer Takeshi MurakamiMichael Buro and is program Logistello

Discovering Complex Othello Strategies… D. E. Moriarty & Risto Miikkulainen, 1997 Found that experts search selective paths through pattern recognition Most programs used deeper searches to be effective Wanted a “human-like” approach

Neural Networks Networks learned Othello without previous knowledge No hand-coded rules or heuristics Strategies evolved through play No search mechanism Goal was to discover strategies

Implementation Neural networks relied on pattern recognition Used marker-based scheme with genetic algorithms Network architecture and weights evolve Population of 50 networks Initially evolved against random move maker

Results Positional strategy against random mover after 100 generations

Results Mobility strategy against searcher after 2000 generations

Articles M. Buro, The Evolution of Strong Othello Programs, 2003 M. Buro, Takeshi Murakami vs. Logistello, 1997 D. E. Moriarty & R. Miikkulainen, Discovering Complex Othello Strategies Through Evolutionary Neural Networks, 1995

Websites Michael Buro Neural Networks Research Group Free online Othello game

Questions ??