Using Restart Heuristics to Improve Agent Performance in Angry Birds

Tommy Liu*, Jochen Renz, Peng Zhang, Matthew Stephenson

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingAcademicpeer-review

1 Citation (Web of Science)


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.
Original languageEnglish
Title of host publicationIEEE Conference on Games
Number of pages8
ISBN (Print)9781728118840
Publication statusPublished - 2019
EventIEEE Conference on Games (IEEE COG) - London, United Kingdom
Duration: 20 Aug 201923 Aug 2019

Publication series

SeriesIEEE Conference on Computational Intelligence and Games


ConferenceIEEE Conference on Games (IEEE COG)
Country/TerritoryUnited Kingdom
Internet address


  • Angry Birds
  • Heuristics
  • Qualitative Spatial
  • Reasoning
  • Restarts
  • Video Games

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