6.6.1. Process Hallucinations Neural networks, especially RNN, can be made to ”hallucinate”, i.e. to generate output on their own, for example in language modeling (Graves, 2013; Sutskever et al., 2011; Karpathy, 2016) or music creation (van den Oord et al., 2016; Huang and Wu, 2016; Choi et al., 2016). This is done by feeding the net output (prediction) immediately back as new input. Hallucinations can provide insight into what the RNN has learned about the training set. The ability of an RNN to re-produce, on its own, realistic and convincing process traces demonstrates that it has correctly learned the important features of the event log used as training sample and thereby validates the trained net and supports the usefulness of the RNN approach for process prediction. Hallucinations are initialized by providing a short seed sequence of input events. One may either accept the event with the highest probability as output, or one samples from events using the output probabilities. We explore the two methods for the full BPI 2012 and BPI 2013 datasets. For each output method and for each dataset, we train a net with 32 embedding dimensions and 5 unrolled steps for 100 epochs. After training is complete, we produce 20 hallucinations of sequence length 1000 from each trained net. An excerpt of the BPI2012 hallucinations is shown in Fig. 9. The complete set of generated hallucinations is available from the authors. In addition to visually inspecting the generated hallucinations and judging their realism, we mine a process model from the original event log using the Flexible Heuristics Miner (FHM) (Weijters and Ribeiro, 2011) with default parameters. We choose the FHM because it performs well on a wide variety of models (Claes and Poels, 2012). We replay both the original log and the generated hallucinations against this model and compute the replay fitness, which indicates how well the model can generate the observed behavior (van der Aalst et al., 2012). A replay 25