Large language models (LLMs) have become phenomenally surging, since 2018--two decades after introducing context-awareness into computing systems. Through taking into account the situations of ubiquitous devices, users and the societies, context-aware computing has enabled a wide spectrum of innovative applications, such as assisted living, location-based social network services and so on. To recognize contexts and make decisions for actions accordingly, various artificial intelligence technologies, such as Ontology and OWL, have been adopted as representations for context modeling and reasoning. Recently, with the rise of LLMs and their improved natural language understanding and reasoning capabilities, it has become feasible to model contexts using natural language and perform context reasoning by interacting with LLMs such as ChatGPT and GPT-4. In this tutorial, we demonstrate the use of texts, prompts, and autonomous agents (AutoAgents) that enable LLMs to perform context modeling and reasoning without requiring fine-tuning of the model. We organize and introduce works in the related field, and name this computing paradigm as the LLM-driven Context-aware Computing (LCaC). In the LCaC paradigm, users' requests, sensors reading data, and the command to actuators are supposed to be represented as texts. Given the text of users' request and sensor data, the AutoAgent models the context by prompting and sends to the LLM for context reasoning. LLM generates a plan of actions and responds to the AutoAgent, which later follows the action plan to foster context-awareness. To prove the concepts, we use two showcases--(1) operating a mobile z-arm in an apartment for assisted living, and (2) planning a trip and scheduling the itinerary in a context-aware and personalized manner.

翻译：大型语言模型（LLMs）自2018年以来（即在计算系统中引入上下文感知概念二十年之后）呈现出爆发式增长。通过考虑泛在设备、用户及社会情境，上下文感知计算已催生出广泛创新应用，如辅助生活、基于位置的社会网络服务等。为识别上下文并据此制定行动决策，本体论、OWL等多种人工智能技术被采用作为上下文建模与推理的表征方式。近年来，随着LLMs的兴起及其自然语言理解与推理能力的提升，使用自然语言进行上下文建模并通过与ChatGPT、GPT-4等LLMs交互进行上下文推理已成为可能。本教程演示了如何通过文本、提示词和自主代理（AutoAgents），使LLMs无需微调即可执行上下文建模与推理。我们系统梳理了相关领域的研究成果，并将这种计算范式命名为"LLM驱动的上下文感知计算（LCaC）"。在LCaC范式中，用户请求、传感器读数数据及执行器指令均以文本形式表示。自主代理根据用户请求文本与传感器数据，通过提示词构建上下文模型并提交至LLM进行推理。LLM生成行动方案并反馈至自主代理，后者遵循该方案实现上下文感知。为验证上述概念，我们展示了两个实例：（1）在公寓中操控移动式Z型机械臂实现辅助生活，（2）以情境感知和个性化方式规划旅行并编排行程。