‘An autonomous agent is a system that interacts with the environment. It has the capability to perceive its surroundings and take actions over time to achieve its own goals (Wang et al., 2023). The development of powerful autonomous agents has long been a goal of industry and academia. However, previous investigations in this domain often leaned towards deploying models in isolated environments with limited knowledge. These approaches markedly diverge from human learning processes, thereby making it challenging for agents to emulate human-like decision-making. Recently, the acquisition of extensive web knowledge has enabled MLLMs to demonstrate impressive capabilities, approaching the level of human intelligence. This progress has captured the attention of the autonomous agent community, leading researchers to embark on extensive explorations. Gradually, a relatively unified framework has been constructed and applied across various domains.
General agent architecture. The MLLM-powered autonomous agent typically consists of a profiling module, a memory module, a planning module, and an action module (Wang et al., 2023). The profiling module aims to assign distinct roles to different agents through prompts, providing relevant demographic and personality information (Wang et al., 2023; Park et al., 2023). These prompts can be crafted through manual methods, LLM generation methods, or dataset alignment methods (Argyle et al., 2023; Hong et al., 2023; Wang et al., 2023). The memory module is a crucial component in the agent’s structure, preserving information gathered from the surroundings in diverse formats like natural language, embedded vectors, databases, and lists (Zhong et al., 2023; Hu et al., 2023; Zhu et al., 2023). Leveraging accumulated experiences, it can aid future decision-making, enabling the agent to evolve and exhibit consistent, rational, and effective behavior. When faced with intricate tasks, humans often decompose them and dynamically adjust them based on the environment. The planning module is crafted to emulate this human ability, employing techniques such as chain-of-thoughts, tree-of-thoughts, and graph-of-thoughts for task decomposition (Yao et al., 2023; Wang et al., 2023; Besta et al., 2023). The action module is in charge of turning the agent’s decisions into actual results or actions. To achieve this, it is essential to clearly define the goals of the actions, identify feasible action sets (e.g., external APIs), and comprehend the impact of actions (Shen et al., 2023).
Autonomous agent application. In social sciences, autonomous agents have catalyzed advancements in disciplines such as law, politics, and economics, yielding many achievements (Cui et al., 2023; Horton, 2023). One typical example is ChatLaw, a legal agent based on LLMs, proficient in providing effective legal consultation services through database support and keyword searches (Cui et al., 2023). In natural sciences, researchers actively explore the integration of autonomous agents with chemistry, giving birth to the ChemCrow model (Bran et al., 2023), which assists researchers in conducting chemical experiments by accessing external tools. In engineering, specifically civil engineering, certain scholars have introduced an interactive framework in which human architects work together with agents to build 3D structures(Mehta et al., 2023). Furthermore, scholars have leveraged the LangChain framework to develop an agent named GeoGPT (Zhang et al., 2023) which autonomously undertakes tasks related to geographical data collection, processing, and analysis. 这些成功的案例启示我们集成autonomous agents进自动地图设计算法，提升其的用户友好性。
’帮我用英文把上面的文字衔接地更加通顺些，不要用太华丽的辞藻