Team, Visitors, External Collaborators
Overall Objectives
Research Program
Application Domains
Highlights of the Year
New Software and Platforms
New Results
Bilateral Contracts and Grants with Industry
Partnerships and Cooperations
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Section: Research Program

Decision support

Patterns have proved to be quite useful for decision-aid. Predictive sequential patterns, to give an example, have a direct application in diagnosis. Itemsets and contrast patterns can be used for interpretable machine learning (ML). In regards to diagnosis, LACODAM inherits, from the former DREAM team, a strong background in decision support systems with internationally recognized expertise in this field. This subfield of AI (Artificial Intelligence) is concerned with determining whether a system is operating normally or not, and the cause of faulty behaviors. The studied system can be an agro- or eco-system, a software system (e.g., a ML classifier), a living being, etc. In relation to interpretable machine learning (ML), this subfield is concerned with the conception of models whose answers are understandable by users. This can be achieved by inducing inherently white-box models from data such as rule-based classifiers/regressors, or by mining rules and explanations from black-box models. The latter setting is quite common due to the high accuracy of black-box models compared to natively interpretable models. Pattern mining is a powerful tool to mine explanations from black-box systems. Those explanations can be used to diagnose biases in systems, either to debug and improve the model, or to generate trust in the verdicts of intelligent software agents.

The increasing volumes of data coming from a range of different systems (ex: sensor data from agro-environmental systems, log data from software systems and ML models, biological data coming from health monitoring systems) can help human and software agents make better decisions. Hence, LACODAM builds upon the idea that decision support systems (an interest bequeathed from DREAM) should take advantage of the available data. This third and last research axis is thus a meeting point for all members of the team, as it requires the integration of AI techniques for traditional decision support systems with results from data mining techniques.

Three main research sub-axes are investigated in LACODAM: