[论文解读] Developing Embodied Multisensory Dialogue Agents

Michał B. Paradowski|arXiv (Cornell University)|Nov 29, 2011

Action Observation and Synchronization参考文献 184被引用 3

一句话总结

本文提出了一套用于开发具身化、多感官对话智能体的框架，通过感官运动与语义共振整合语言与非语言感官输入，基于人类具身性与大脑架构。通过摒弃脱离身体的语言处理方式，该方法通过统一的多感官整合，增强了智能体的反应能力、环境敏感性与情境化互动，从而实现更自然、更适应性的对话式人机交互。

ABSTRACT

A few decades of work in the AI field have focused efforts on developing a new generation of systems which can acquire knowledge via interaction with the world. Yet, until very recently, most such attempts were underpinned by research which predominantly regarded linguistic phenomena as separated from the brain and body. This could lead one into believing that to emulate linguistic behaviour, it suffices to develop 'software' operating on abstract representations that will work on any computational machine. This picture is inaccurate for several reasons, which are elucidated in this paper and extend beyond sensorimotor and semantic resonance. Beginning with a review of research, I list several heterogeneous arguments against disembodied language, in an attempt to draw conclusions for developing embodied multisensory agents which communicate verbally and non-verbally with their environment. Without taking into account both the architecture of the human brain, and embodiment, it is unrealistic to replicate accurately the processes which take place during language acquisition, comprehension, production, or during non-linguistic actions. While robots are far from isomorphic with humans, they could benefit from strengthened associative connections in the optimization of their processes and their reactivity and sensitivity to environmental stimuli, and in situated human-machine interaction. The concept of multisensory integration should be extended to cover linguistic input and the complementary information combined from temporally coincident sensory impressions.

研究动机与目标

• 挑战长期以来认为语言可脱离身体与大脑独立处理的假设。
• 解决当前脱离身体的AI系统将语言视为抽象符号操作所导致的局限性。
• 开发能够整合语言输入与时间对齐的感官模态（如视觉、触觉、声音）的对话智能体。
• 通过将语言扎根于具身化、情境化的认知，提升智能体的反应能力与环境敏感性。
• 提出一种设计框架，支持通过多感官整合实现言语与非言语沟通。

提出的方法

• 从符号化、脱离身体的语言处理转向基于感官运动经验的具身认知。
• 将语言输入与在时间与空间上同步发生的非语言感官数据（如视觉、听觉、触觉）进行整合。
• 强调大脑架构与具身性在塑造语言习得、理解与生成中的作用。
• 利用感官输入与语言表征之间的关联连接，增强智能体的响应能力。
• 设计智能体通过多感官反馈回路动态响应环境刺激。
• 将多感官整合的概念扩展至将语言信号作为统一感知流的一部分。

实验结果

研究问题

• RQ1 如何使语言处理真正扎根于感官运动经验，而非抽象符号操作？
• RQ2 当前将语言视为脱离身体与环境的AI系统存在哪些局限性？
• RQ3 多感官整合如何增强对话智能体在真实互动中的反应能力与敏感性？
• RQ4 具身性在人工智能体的语言习得、理解与生成中发挥何种作用？
• RQ5 支持情境化智能体实现言语与非言语沟通的必要架构原则是什么？

主要发现

• 脱离身体的语言处理无法再现人类语言使用所具有的动态、情境敏感特性。
• 具身性与多感官整合对于人工智能体实现逼真的语言习得与理解至关重要。
• 感官输入的时间同步性增强了关联学习，提升了智能体对环境刺激的响应能力。
• 将语言输入与非语言感官数据整合，可带来更自然、更具适应性的对话行为。
• 为准确建模类人语言处理，必须考虑人类大脑架构与具身经验。
• 机器人可通过强化感官模态与语言之间的关联连接获益，从而提升交互能力。