We foresee robots that bootstrap knowledge representations and use them for
classifying relevant situations and making decisions based on future
observations. Particularly for assistive robots, the bootstrapping mechanism
might be supervised by humans who should not repeat a training phase several
times and should be able to refine the taught representation. We consider
robots that bootstrap structured representations to classify some intelligible
categories. Such a structure should be incrementally bootstrapped, i.e.,
without invalidating the identified category models when a new additional
category is considered. To tackle this scenario, we presented the Scene
Identification and Tagging (SIT) algorithm, which bootstraps structured
knowledge representation in a crisp OWL-DL ontology. Over time, SIT bootstraps
a graph representing scenes, sub-scenes and similar scenes. Then, SIT can
classify new scenes within the bootstrapped graph through logic-based
reasoning. However, SIT has issues with sensory data because its crisp
implementation is not robust to perception noises. This paper presents a
reformulation of SIT within the fuzzy domain, which exploits a fuzzy DL
ontology to overcome the robustness issues. By comparing the performances of
fuzzy and crisp implementations of SIT, we show that fuzzy SIT is robust,
preserves the properties of its crisp formulation, and enhances the
bootstrapped representations. On the contrary, the fuzzy implementation of SIT
leads to less intelligible knowledge representations than the one bootstrapped
in the crisp domain.

本文介绍了一种在模糊领域中重塑 Scene Identification and Tagging（SIT）算法的方法，利用模糊 DL 本体论来克服其感知噪声问题，并通过与清晰实现的 SIT 的性能比较展示了模糊 SIT 的鲁棒性，保留其清晰表达方式，并增强了引导式表示，而模糊实现则导致知识表示的可理解性下降。