Humans have a rich representation of the entities in their environment. Entities are described by their attributes, and entities that share attributes are often semantically related. For example, if two books have "Natural Language Processing" as the value of their `title' attribute, we can expect that their `topic' attribute will also be equal, namely, "NLP". Humans tend to generalize such observations, and infer sufficient conditions under which the `topic' attribute of any entity is "NLP". If robots need to interact successfully with humans, they need to represent entities, attributes, and generalizations in a similar way. This ends in a contextualized cognitive agent that can adapt its understanding, where context provides sufficient conditions for a correct understanding. In this work, we address the problem of how to obtain these representations through human-robot interaction. We integrate visual perception and natural language input to incrementally build a semantic model of the world, and then use inductive reasoning to infer logical rules that capture generic semantic relations, true in this model. These relations can be used to enrich the human-robot interaction, to populate a knowledge base with inferred facts, or to remove uncertainty in the robot's sensory inputs.

中文翻译：

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走向人类机器人交互中的抽象关系学习

人类在其环境中具有丰富的实体表示。实体通过属性来描述，共享属性的实体通常在语义上相关。例如，如果两本书的“书名”属性的值为“自然语言处理”，我们可以预期它们的“主题”属性也将相等，即“ NLP”。人类倾向于对这些观察结果进行概括，并推断出任何实体的“主题”属性为“ NLP”的充分条件。如果机器人需要与人类成功交互，则它们需要以类似的方式表示实体，属性和概括。这以可以适应其理解的情境化认知主体结尾，其中情境为正确理解提供了充分条件。在这项工作中 我们解决了如何通过人机交互获得这些表示的问题。我们将视觉感知和自然语言输入进行集成，以逐步构建世界的语义模型，然后使用归纳推理来推断捕获通用语义关系的逻辑规则，在该模型中是正确的。这些关系可用于丰富人机交互，以推断的事实填充知识库，或消除机器人感官输入中的不确定性。