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Automated dilemmas generation in simulations

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Abstract

Our ultimate purpose is to train individuals, in virtual environments, to handle critical situations. One of these critical situations is dilemmas. They refer to situations that lead to negative consequences whichever is the choice made by the protagonist. In critical contexts, it is crucial to know how to handle this kind of situations to prevent disastrous consequences from happening. Thus, people need to be exposed to various training situations in which they put in play and develop the appropriate skills. However, in complex domains, it is difficult—sometimes impossible—to write all the possible training scenarios. To address this problem, an automated generation approach is considered. In this article, we present KOBA, a scenario engine that automatically generates dilemma situations without having to write them beforehand. This engine uses knowledge models to extract the necessary properties for dilemmas to emerge. In this article, we present this approach and expose a proof of concept of the generation process.

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Notes

  1. 1.

    The constructivism is a learning theory that suggests that people construct their own understanding and knowledge, through experience and reflecting on those experiences (Piaget 1948; Vygotsky 1978).

    Situated learning is a theory that suggests that learning is a function of the activity, context and culture (Lave and Wenger 1991).

  2. 2.

    https://www.vive.com/fr/.

  3. 3.

    The inference process is out of the scope of this paper.

  4. 4.

    According to Goel (1995) “Nomological constraints are constraints dictated by natural law”.

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Correspondence to Azzeddine Benabbou.

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Benabbou, A., Lourdeaux, D. & Lenne, D. Automated dilemmas generation in simulations. Cogn Tech Work (2020). https://doi.org/10.1007/s10111-019-00621-z

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Keywords

  • Scenario generation
  • Virtual environment
  • Knowledge models
  • Dilemmas