Short presentation to explain my work as term paper for the AI class (2009)

1. Term paper presentation, Artificial Intelligence class, 2009
Development of an Artificial Player
for Connect-(4|n) game
Alessandro Manfredi
256479

2. The Connect-(4|n) game
• Implementation of game dynamics with
dynamic board size and required connections
for the game to be won.
• Different playing modes
• Human vs Human
• Human vs AI
• Simulations: AI vs AI
(Useful to obtain wide range of moves computed by
the algorithm as possible input for an automatic
learning tool, or just to cache precomputed moves)

3. Mini-Max Algorithm
• Implementations in PHP and Java
• Game dynamics are implemented as a PHP script
• An external Java jar is used to compute moves
• Chance to train or precompute moves outside
the actual game simulations (?)

4. connectMinimax usage
$ java -jar connectMinimax.jar
<rows> <columns> <connections> <depth-limit>
<heuristic-exp> <board-dump> <player>
<rows> # Board rows
<columns> # Board columns
<connections> # Required connections to win
<depth-limit> tree depth limit
<heuristic-exp> Heuristic exponent
<board-dump> Game state representation
<player> Player for which compute the move

5. Mini-Max for non-perfect decisions
• Need an heuristic
• Evaluation of game state or evaluation of possible
further moves?
• Incremental evaluation: estimate value of chances
opened by a move
• NB: a winning move has an infinite (positive)
evaluation
• NB: moves which bring to (even slightly) better
game states must have a relevant surplus of
evaluation

6. Moves’ Heuristic
• Consider the 4 dimensions
separately
• For each, consider any
window including the move
• For each window available to
be completed, sum player’s
pieces raised to the
parametric heuristic exponent
• The evaluation of a move is
the sum of all these
components

7. Improvements
• Raise tree depth
• Better results, longer time to wait for each
non-precomputed move :(
• Max depth can be increased as the game moves on
(less possibilities to compute)
• Time to wait almost not affected
• Alfa-Beta Pruning

8. Results and future dev.
• AI beats Human in 60-65% of cases.
• NB: Initial player is pseudo-random
• Time-to-wait is still too long: 17-19 sec.
• How about a machine learning tool trained with
the move computation component?
• A classifier ( Decision-Tree ?)
• Neural network ( Multilayer perceptron with
backpropagation ?)