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.

翻译：我们预见到机器人能够引导知识表示，并利用这些表示对相关情境进行分类，并基于未来观测做出决策。特别是在辅助机器人领域，引导机制可由人类监督，人类不应重复多次训练阶段，且应能优化已教授的知识表示。我们考虑引导结构化表示以对某些可理解的类别进行分类的机器人。这种结构应能增量式引导，即在考虑新类别时，不使已识别的类别模型失效。为解决这一场景，我们提出了场景识别与标记（SIT）算法，该算法在精确的OWL-DL本体中引导结构化知识表示。随着时间的推移，SIT引导出一个表示场景、子场景及相似场景的图。随后，SIT可通过基于逻辑的推理，在已引导的图中对新场景进行分类。然而，SIT在处理传感器数据时存在问题，因其精确实现无法抵抗感知噪声。本文提出了SIT在模糊域中的重新表述，利用模糊DL本体克服鲁棒性问题。通过比较SIT的模糊实现与精确实现的性能，我们展示了模糊SIT具有鲁棒性，保留了其精确表述的特性，并增强了引导出的知识表示。相反，SIT的模糊实现导致的知识表示比精确域中引导出的可理解性更低。