Whats very interesting is if we could use LLMs to generate GOFAI methods. Its often not at all obvious how to do so. Than being said its still hard to express goals in terms of natural language and resources to LLMs. I;ve been trying different things and none seems to work for me to say hey this is a step improvement. Its also hard to come up with a dataset for these use cases.
FLE agents technically can implement their own Python libraries to leverage GOFAI to do the heavy lifting. None has actually attempted this yet though. It would be interesting to see if this can be achieved just by modifying the manual given to the agents to bias in favour of this approach.
That does sound interesting. I might attempt it. Thanks for this benchmark, I totally could use it for my PhD (I started with GOFAI, but have hit a dead end. My advisor is suggesting pivoting into using LLMs to call my GoFAI framework.
Feel free to create an issue in the repo - am totally happy to help however I am able! I think that the only change you'll have to make is to expose your GoFAI framework in the 'Namespace' object which the agents have access to (for them to call it directly). Alternatively you could design a new tool which takes in game objects and generates a solution / typed object output.
