Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Toward Co-creative Dungeon Generation via Transfer Learning   Co-creative Procedural Content Generation via Machine Learning (PCGML) refers
to systems where a PCGML agent and a human work together to produce output
content. One of the limitations of co-creative PCGML is that it requires
co-creative training data for a PCGML agent to learn to interact with humans.
However, acquiring this data is a difficult and time-consuming process. In this
work, we propose approximating human-AI interaction data and employing transfer
learning to adapt learned co-creative knowledge from one game to a different
game. We explore this approach for co-creative Zelda dungeon room generation.

7
The categories of this paper are: hep-th
8
The categories of this paper are: hep-ex
17
The categories of this paper are: q-fin
19
The categories of this paper are: eess
5
The categories of this paper are: gr-qc
25
The categories of this paper are: patt-sol
30
The categories of this paper are: q-alg
37
The categories of this paper are: supr-con
29
The categories of this paper are: funct-an
10
The categories of this paper are: q-bio
11
The categories of this paper are: hep-lat
34
The categories of this paper are: chem-ph
28
The categories of this paper are: alg-geom
23
The categories of this paper are: solv-int
24
The categories of this paper are: dg-ga
36
The categories of this paper are: cmp-lg