LangBirds: An Agent for Angry Birds using a Large Language Model

The game Angry Birds is a challenging problem for artificial intelligence in that it requires physical reasoning ability. Previous approaches require domain knowledge or playing data, or have limitations in generalization performance. Inspired by human approach to physics-based puzzle games, we devise a new Angry Birds agent that separates AI’s thought into two stages. To this end, our method, LangBirds, uses Large Language Models (LLMs) that are recently considered to show human-level performance. We compared LangBirds to several reinforcement learning agents as well as heuristic agents. The results on the Phy-Q benchmark, which is a testbed based on Angry Birds, showed that our approach outperforms baselines. Moreover, the proposed approach allows us to understand the decision-making process since it uses natural language. Qualitative assessments indicated that the rationale for the decisions was reasonable.