Using Restart Heuristics to Improve Agent Performance in Angry Birds
  Over the past few years the Angry Birds AI competition has been held in an
attempt to develop intelligent agents that can successfully and efficiently
solve levels for the video game Angry Birds. Many different agents and
strategies have been developed to solve the complex and challenging physical
reasoning problems associated with such a game. However none of these agents
attempt one of the key strategies which humans employ to solve Angry Birds
levels, which is restarting levels. Restarting is important in Angry Birds
because sometimes the level is no longer solvable or some given shot made has
little to no benefit towards the ultimate goal of the game. This paper proposes
a framework and experimental evaluation for when to restart levels in Angry
Birds. We demonstrate that restarting is a viable strategy to improve agent
performance in many cases.
