Using restart heuristics to improve agent performance in Angry Birds

Tommy Liu, Jochen Renz, Peng Zhang, Matthew Stephenson

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

1 Citation (Scopus)


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 2019, CoG 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages8
ISBN (Electronic)9781728118840
ISBN (Print)9781728118857
Publication statusPublished - Aug 2019
Externally publishedYes
Event2019 IEEE Conference on Games, CoG 2019 - London, United Kingdom
Duration: 20 Aug 201923 Aug 2019

Publication series

NameIEEE Conference on Computatonal Intelligence and Games, CIG
ISSN (Print)2325-4270
ISSN (Electronic)2325-4289


Conference2019 IEEE Conference on Games, CoG 2019
Country/TerritoryUnited Kingdom


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


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