This content is associated with a 2014 GDC AI Summit lecture on algorithms for
dynamic behavior, including Regret Matching, UCB1 and MCTS. For more information
see the overview page.
The program on this page demos Regret Matching over actions using the game
Rock Paper Scissors. The AI can choose between rock, paper, and scissors.
Note that if you play with a strategy that is predictable, such as the
sequence of rock, paper, and scissors repeatedly, the AI won't notice this,
as it only reasons about single actions.
By looking at the internal AI debug information, you can play to maximize
your chance of winning with any single action. However, the more you win,
the greater the regret on any given action. This leads the AI to play closer
and closer to random -- the strategy that can't be beaten.
Suggested tests: (1) Try to play to win; you shouldn't win much in the long term
(2) Play a fixed pattern (R-P-S); the AI won't take advantage of your play, since it
doesn't reason about temporal patterns.