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)

Abstract

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
PublisherIEEE
Number of pages8
ISBN (Print)9781728118840
DOIs
Publication statusPublished - 2019
EventIEEE Conference on Games (IEEE COG) - London, United Kingdom
Duration: 20 Aug 201923 Aug 2019
https://ieee-cog.org/2019/

Publication series

SeriesIEEE Conference on Computational Intelligence and Games
ISSN2325-4270

Conference

ConferenceIEEE Conference on Games (IEEE COG)
Country/TerritoryUnited Kingdom
CityLondon
Period20/08/1923/08/19
Internet address

Keywords

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

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