Large language models (LLMs) provide capabilities far beyond sentence
completion, including question answering, summarization, and natural-language
inference. While many of these capabilities have potential application to
cognitive systems, our research is exploiting language models as a source of
task knowledge for cognitive agents, that is, agents realized via a cognitive
architecture. We identify challenges and opportunities for using language
models as an external knowledge source for cognitive systems and possible ways
to improve the effectiveness of knowledge extraction by integrating extraction
with cognitive architecture capabilities, highlighting with examples from our
recent work in this area.

利用大型语言模型作为认知系统的任务知识、认知代理和知识提取，以及通过整合提取与认知架构能力来提高知识提取效果的挑战和机会。

利用语言模型作为认知代理的知识来源

Exploiting Language Models as a Source of Knowledge for Cognitive Agents

Cognitive agents such as humans and robots perceive their environment through
an abundance of sensors producing streams of data that need to be processed to
generate intelligent behavior. A key question of cognition-enabled and
AI-driven robotics is how to organize and manage knowledge efficiently in a
cognitive robot control architecture. We argue, that memory is a central active
component of such architectures that mediates between semantic and sensorimotor
representations, orchestrates the flow of data streams and events between
different processes and provides the components of a cognitive architecture
with data-driven services for the abstraction of semantics from sensorimotor
data, the parametrization of symbolic plans for execution and prediction of
action effects.
Based on related work, and the experience gained in developing our ARMAR
humanoid robot systems, we identified conceptual and technical requirements of
a memory system as central component of cognitive robot control architecture
that facilitate the realization of high-level cognitive abilities such as
explaining, reasoning, prospection, simulation and augmentation. Conceptually,
a memory should be active, support multi-modal data representations, associate
knowledge, be introspective, and have an inherently episodic structure.
Technically, the memory should support a distributed design, be
access-efficient and capable of long-term data storage. We introduce the memory
system for our cognitive robot control architecture and its implementation in
the robot software framework ArmarX. We evaluate the efficiency of the memory
system with respect to transfer speeds, compression, reproduction and
prediction capabilities.

本论文针对认知机器人控制架构中的知识管理问题，提出了一种支持感知运动表示和语义表示之间中介和提供数据驱动服务的内在记忆结构，并且对基于该结构的 ArmarX 机器人软件框架进行了评估。

机器人认知架构的记忆系统及其在 ArmarX 中的实现

A Memory System of a Robot Cognitive Architecture and its Implementation in ArmarX

Evidence-based reasoning is at the core of many problem-solving and
decision-making tasks in a wide variety of domains. Generalizing from the
research and development of cognitive agents in several such domains, this
paper presents progress toward a computational theory for the development of
instructable cognitive agents for evidence-based reasoning tasks. The paper
also illustrates the application of this theory to the development of four
prototype cognitive agents in domains that are critical to the government and
the public sector. Two agents function as cognitive assistants, one in
intelligence analysis, and the other in science education. The other two agents
operate autonomously, one in cybersecurity and the other in intelligence,
surveillance, and reconnaissance. The paper concludes with the directions of
future research on the proposed computational theory.

本文介绍了关于证据推理认知代理的计算理论，并将其应用于政府和公共部门的关键领域。该理论通过开发四种不同类型的认知代理来验证其可行性，并探讨未来研究的方向。

面向可指导认知机器人的基于证据的推理的计算理论

Toward a Computational Theory of Evidence-Based Reasoning for  Instructable Cognitive Agents

Given a single image x from domain A and a set of images from domain B, our
task is to generate the analogous of x in B. We argue that this task could be a
key AI capability that underlines the ability of cognitive agents to act in the
world and present empirical evidence that the existing unsupervised domain
translation methods fail on this task. Our method follows a two step process.
First, a variational autoencoder for domain B is trained. Then, given the new
sample x, we create a variational autoencoder for domain A by adapting the
layers that are close to the image in order to directly fit x, and only
indirectly adapt the other layers. Our experiments indicate that the new method
does as well, when trained on one sample x, as the existing domain transfer
methods, when these enjoy a multitude of training samples from domain A. Our
code is made publicly available at
this https URL

本篇论文讨论了单张图片从 A 领域转换到 B 领域的问题，提出了一种使用变分自编码器的训练方式，即先训练 B 领域自编码器模型，再根据待转换图片 x 创建 A 领域自编码器模型进行训练，本研究表明，该方法在只有一张样本图片 x 的情况下也能达到与现有领域转换方法相同的表现。