Discovering Temporal Logic patterns as binary supervised learning

Making sense of data is a main challenge in creating human understandable descriptions of complex situations. When data refer to process executions, techniques exist that discover explicit descriptions in terms of formal models. Many research works envisage the discovery task as a one-class supervised learning job. Work on deviance mining highlighted nonetheless the need to characterise behaviours that exhibit certain characteristics and forbid others (e.g., the slower, less frequent), leading to the quest for a binary supervised learning task.

In this microproject we focus on the discovery of declarative process models, expressed through Linear Time Temporal Logic, as a binary supervised learning task, where the input log reports both positive and negative behaviours. We therefore investigate how valuable information can be extracted and formalised into a “optimal” model, according to user-preferences (e.g., model generality or simplicity). By iteratively including further examples, the user can also refine the discovered models

Output

Paper to be submitted to relevant journal

Machine learning tool

Artificial data set

Presentations

Project Partners:

• Fondazione Bruno Kessler (FBK), Chiara Ghidini
• Università di Bologna (UNIBO), Federico Chesani

Primary Contact: Chiara Ghidini, FBK

Main results of micro project:

The microproject has produced so far two main results:
– A two-step approach for the discovery of temporal-logic patterns as a binary supervised learning problem, that is starting from a set of "positive traces" (execution traces whose behaviour we want to observe in the discovered patterns), and a set of "negative" traces (execution traces whose behaviour we do not want to observe in the discovered patterns). In detail, in the first step, sets of patterns (possible models) that accept all positive traces and discard as much as possible of the negative ones are discovered. In the second step, the model(s) optimizing one criterion, as for instance the generality or the simplicity, are selected among the possible discovered models.
– Two synthetic labelled ("positive" and "negative") event log datasets used for the synthetic evaluation of the proposed approach.

Contribution to the objectives of HumaneAI-net WPs

The results of the microproject mainly contribute to WP1 (Human-in-the-Loop Machine Learning, Reasoning and Planning). Indeed, on the one hand, the micro-project aims at leveraging machine learning techniques (sub-symbolic learning) to provide LTL patterns (symbolic representation) of a set of "positive" traces, while excluding the "negative" ones (T1.1). On the other hand, the micro-project is a first step towards including the human in the loop of the discovery of LTL patterns representing all and only the cases the human wants to represent (T1.3). The user could indeed iteratively refine the discovered patterns so as to be sure to include all the cases she is interested to include, while excluding all those cases that she wants to exclude.

Tangible outputs

• Dataset: LoanApproval1 – Federico Chesani
  https://drive.google.com/drive/folders/15BwG4PJq8iIMh9Sr9dpMXAYBY-qp7QDE?usp=sharing
• Dataset: LoanApproval2 – Federico Chesani
  https://drive.google.com/drive/folders/1fcJ8itzdMbNOjEAeV6nUEeI5B6__aB_c?usp=sharing
• Program/code: Discovery Framework – Sergio Tessaris
  https://zenodo.org/record/5158528