论文标题
旨在忘记对集成机器人体系结构的表达产生
Toward Forgetting-Sensitive Referring Expression Generationfor Integrated Robot Architectures
论文作者
论文摘要
为了进行类似人类的对话，机器人需要能够描述其环境中对象，位置和人的能力，这是一种称为“参考表达产生”的功能。正如说话者反复指的是类似对象时，它们倾向于从以前的描述中重新使用属性，部分原因是为了帮助听众，部分原因是这些属性在工作记忆中（WM）中的认知能力可获得。由于工作记忆的不同理论“忘记”一定会导致认知可用性的差异，因此我们假设它们将同样导致产生不同的参考表达式。为了设计有效的智能代理，有必要确定不同的遗忘模型如何在产生类似人类的自然参考表达式方面有效。在这项工作中，我们计算了在机器人认知体系结构中忘记工作记忆的两个候选模型，并演示了它们如何导致基于认知的参考表达式中基于认知的可用性差异。
To engage in human-like dialogue, robots require the ability to describe the objects, locations, and people in their environment, a capability known as "Referring Expression Generation." As speakers repeatedly refer to similar objects, they tend to re-use properties from previous descriptions, in part to help the listener, and in part due to cognitive availability of those properties in working memory (WM). Because different theories of working memory "forgetting" necessarily lead to differences in cognitive availability, we hypothesize that they will similarly result in generation of different referring expressions. To design effective intelligent agents, it is thus necessary to determine how different models of forgetting may be differentially effective at producing natural human-like referring expressions. In this work, we computationalize two candidate models of working memory forgetting within a robot cognitive architecture, and demonstrate how they lead to cognitive availability-based differences in generated referring expressions.
