Media Innovation Circle #15
November 30 @2pm-3pm (Lisbon Time)
Narrative orchestration based virtual environments for training
Speaker: Domitile Lourdeaux, Alliance Sorbonne Université, Université de technologie de Compiègne – France
Moderation: Carlos Santos, University of Aveiro

Domitile Lourdeaux is Full Professor at Alliance Sorbonne Université, Université de technologie de Compiègne, CNRS Heudiasyc. Her research interests are Artificial Intelligence based Virtual Environments for training. She is vice-president of the French Association for Artificial Intelligence and coordinator of the Artificial Intelligence and Data Science specialty in Université de technologie de Compiègne. She was a member of the National University Committee (CNU) between 2013 and 2019. She coordinated several national projects: ANR VICTEAMS (training of teams to rescue the injured), ANR NIKITA (aeronautical training), ANR V3S (training on high-risk sites) and RIAM APLG (monitoring in virtual reality), She is scientific manager for the CNRS of the European H2020 INFINITY (coupling of virtual reality and artificial intelligence tools for the investigation of data in the fight against crime and terrorism).


Virtual environments allow trainees to experiment, practice and see the impact of their decisions. In crisis management training, in order to facilitate learning through personal experience, it is necessary to confront the trainees with a wide variety of situations adapted to their level. To support this type of learning, writing scenarios requires significant work.
One of the Interactive Narrative challenges is to be able to dynamically orchestrate these environments. In this presentation, we will present an orchestration system aiming at a set of objectives often considered as contradictory: freedom of action, the dynamic character and the effectiveness of the control of the scenario, the consistency of behaviors, the explicability of behaviors and the adaptability of the system. To maintain freedom of action and ensure the adaptability and explainability of behaviors, we dynamically and automatically generate training situations from knowledge models that underlie the simulation. We hypothesize that it is the generative power that will achieve the variability and resilience objectives. However, the more a system is generative, the more difficult it is to obtain scenarios faithful to the intention of the author (narrative paradox).
We will present a computational orchestration model reifying cognitive models, learning theories and narrative theories. Our model combines symbolic (knowledge models, planning) and numerical (probabilistic approaches, belief functions) artificial intelligence approaches.