The first major conquest of artificial intelligence was chess. The game has a dizzying number of possible combinations, but it was relatively tractable because it was structured by a set of clear rules. An algorithm could always have perfect knowledge of the state of the game and know every possible move that both it and its opponent could make. The state of the game could be evaluated just by looking at the board.
But many other games aren't that simple. If you take something like Pac-Man, then figuring out the ideal move would involve considering the shape of the maze, the location of the ghosts, the location of any additional areas to clear, the availability of power-ups, etc., and the best plan can end up in disaster if Blinky or Clyde makes an unexpected move. We've developed AIs that can tackle these games, too, but they have had to take a very different approach to the ones that conquered chess and Go.
At least until now. Today, however, Google's DeepMind division published a paper describing the structure of an AI that can tackle both chess and Atari classics.
