• LSTM: This approach is a Long Short Term Memory Recurrent Neural Network (LSTMRNN or just LSTM) based on Summerville and Mateas (2016), recreated in Tensorflow from the information given in the paper and training **data** supplied by the authors. It takes as input a game level represented as a sequence and outputs the next tile type. We modified this approach to a bidirectional Figure 2: Examples of six final levels from our study, each pair of levels from a specific co-creative agent: Markov Chain (top), Bayes Net (middle), and LSTM (bottom). These levels were selected at random from the set of final levels, split by co-creative agent.