Shavlik and Narayan-Chen say their method was simple

Shavlik and Narayan-Chen say their method was simple. They took screenshots of the game periodically during play, taking note of where the objects on the screen were positioned when things occurred. Essentially, the screenshots were their data."We had only taken an intro class to AI, so the techniques are fairly simple," Narayan-Chen said. "You don't have a chance to go deep into complex algorithms. It's really simple but really effective Mobile Crane QY12 manufacturers and exporters. Many other competitors used really complex algorithms."They chose the shot angles based on what they saw on the screen around the pigs. Eventually, they started to label the shots as "good" or "bad."

A good shot was one from a game in which all the pigs were destroyed. A bad shot was one that didn't destroy a pig and resulted in a lost game.They encountered a few surprises along the way.Angry Bird aficionados might be interested to learn that that the outcome of each bird affects the outcome of the next.The team also learned that making the same shot twice under the same conditions will not result in the same outcome. There's an element of randomness to the game.One of the most important things for the team to achieve was to actually teach the AI player how to play the game — not just how to memorize and ace specific levels.

In other words, the player makes shot choices based on the surroundings of the pig, rather than the fact that it has played and defeated the level a certain way before. Mastering that served them well in the competition, where special levels were created to stump the competitors."That's the most important lesson in machine learning, to make sure you test on different data than you trained on buy Truck Crane QY16C from China," Shavlik said. "It's easy to memorize data, but it's hard to generalize and do well with future data."While the trio of scientists was measured and meticulous in its research, it was aggressive and unrelenting in the competitive arena.