Abstract
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.
Abstract (translated)
我们预计将出现能够引导知识表示的机器人，并将其用于分类相关情况并根据未来观察结果做出决策的机器人。特别是辅助机器人，引导机制可能由人类监督，他们不应该重复训练阶段多次，并且应该能够精炼所教授的表示。我们认为，引导结构化表示以分类一些可解释类别的机器人。这种结构应该通过逐步引导来进行，即在考虑新增类别时不会破坏已确定的类别模型。为解决这种情况，我们提出了Scene Identification and Tagging (SIT)算法，它在 crisp OWL-DL 上下文中引导结构化知识表示。随着时间的推移，SIT 通过基于逻辑推理绘制场景、子场景和类似场景的图。然后，SIT 通过逻辑推理对引导的图中的新场景进行分类。然而，SIT 在感官数据方面存在问题，因为其明确的实现对感知噪声不具有鲁棒性。本文在模糊领域对SIT进行了重新表述，利用模糊DL 上下文克服了鲁棒性问题。通过比较模糊和明确实现SIT的性能，我们证明了模糊SIT具有鲁棒性，保留了其明确的公式的性质，并增强了引导的表示。相反，模糊实现SIT导致生成的知识表示比在清晰领域引导的更不清晰。
URL
https://arxiv.org/abs/2404.11744
