M2-PALE extracts process models from multi-agent MCTS-Minimax execution traces using Alpha Miner, iDHM and Inductive Miner, then uses LLMs to generate causal explanations, shown in a small checkers setting.
arXiv preprint arXiv:1610.07989 (2016)
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abstract
This report presents a submission to the Process Discovery Contest. The contest is dedicated to the assessment of tools and techniques that discover business process models from event logs. The objective is to compare the efficiency of techniques to discover process models that provide a proper balance between "overfitting" and "underfitting". In the context of the Process Discovery Contest, process discovery is turned into a classification task with a training set and a test set; where a process model needs to decide whether traces are fitting or not. In this report, we first show how we use two discovery techniques, namely: Inductive Miner and Decomposition, to discover process models from the training set using ProM tool. Second, we show how we use replay results to 1) check the rediscoverability of models, and to 2) classify unseen traces (in test logs) as fitting or not. Then, we discuss the classification results of validation logs, the complexity of discovered models, and their impact on the selection of models for submission. The report ends with the pictures of the submitted process models.
years
2026 2verdicts
UNVERDICTED 2representative citing papers
PM4Py-UCM extends process mining to output hierarchical UCM models with configurable visualizations and round-trip export to jUCMNav using public and synthetic event logs.
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Towards Process Mining Use Case Map Models with PM4Py-UCM
PM4Py-UCM extends process mining to output hierarchical UCM models with configurable visualizations and round-trip export to jUCMNav using public and synthetic event logs.