Large Language Models (LLMs) have proven to be useful tools in various domains outside of the field of their inception, which was natural language processing. In this study, we provide practical directions on how to use LLMs to generate 2D-game rooms for an under-development game, named Metavoidal. Our technique can harness the power of GPT-3 by Human-in-the-loop fine-tuning which allows our method to create 37% Playable-Novel levels from as scarce data as only 60 hand-designed rooms under a scenario of the non-trivial game, with respect to (Procedural Content Generation) PCG, that has a good amount of local and global constraints.

In this paper we present a lossless technique to compress pattern databases (PDBs) in the Pancake Sorting problems. This compression technique together with the choice of zero-cost operators in the construction of additive PDBs reduces the memory requirement for PDBs in these problems to a great extent, thus making otherwise intractable problems able to be efficiently handled. Also, using this method, we can construct some problem-size independent PDBs. This precludes the necessity of constructing new PDBs for new problems with different numbers of pancakes. In addition to our compression technique, by maximizing over the heuristic value of additive PDBs and the modified version of the gap heuristic, we have obtained powerful heuristics for the burnt pancake problem.