Akbaba – An Agent for the Angry Birds AI Challenge Based on Search and Simulation

Akbaba – An Agent for the Angry Birds AI Challenge Based on Search and Simulation. Schiffer, S., Jourenko, M., & Lakemeyer, G. IEEE Transactions on Computational Intelligence and AI in Games, PP(99):1–12, Sep, 2015.
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We report on our entry for the AI Birds competition, where we designed, implemented and evaluated an agent for the physics puzzle computer game Angry Birds. Our agent uses search and simulation to find appropriate parameters for launching birds. While there are other methods that focus on qualitative reasoning about physical systems we try to combine simulation and adjustable abstractions to efficiently traverse the possibly infinite search space. The agent features a hierarchical search scheme where different levels of abstractions are used. At any level, it uses simulation to rate subspaces that should be further explored in more detail on the next levels. We evaluate single components of our agent and we also compare the overall performance of different versions of our agent. We show that our approach yields a competitive solution on the standard set of levels.

@Article{ Schiffer:Jourenko:Lakemeyer:TCIAIG2015SIPBSG:Akbaba,
  title      = {{Akbaba} -- An Agent for the {Angry Birds} {AI} {Challenge} Based on Search and Simulation},
  author     = {Schiffer, Stefan and Jourenko, Maxim and Lakemeyer, Gerhard},
  journal    = {IEEE Transactions on Computational Intelligence and AI in Games},
  year       = {2015},
  month      = {Sep},
  volume     = {PP},
  number     = {99},
  pages      = {1--12},
  keywords   = {Artificial intelligence; Angry Birds; Computational modeling; Physics Engines; Games;Search; Simulation},
  doi        = {10.1109/TCIAIG.2015.2478703},
  ISSN       = {1943-068X},
  abstract   = {We report on our entry for the AI Birds competition,
                where we designed, implemented and evaluated an agent
                for the physics puzzle computer game Angry Birds. Our
                agent uses search and simulation to find appropriate
                parameters for launching birds. While there are other
                methods that focus on qualitative reasoning about
                physical systems we try to combine simulation and
                adjustable abstractions to efficiently traverse the
                possibly infinite search space. The agent features a
                hierarchical search scheme where different levels of
                abstractions are used. At any level, it uses
                simulation to rate subspaces that should be further
                explored in more detail on the next levels. We
                evaluate single components of our agent and we also
                compare the overall performance of different versions
                of our agent. We show that our approach yields a
                competitive solution on the standard set of levels.},
}

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