5月30日上午10：00，我院副院长戴望州邀请俄亥俄州立大学的谷雨博士作题为“Extrapolating A Agents to Open-World Taskswith Natural Language”的学术报告。
报告摘要：
Enabling machines to take actions and fulfill diverse human goals constitutes, arguably, the most longstanding objective of Al research. A core challenge for developing such versatile agents in open-worlctasks lies in effective goal transfer (i.e., extrapolating to diverse unseen goals). Agents based onreinforcement learning (RL) specify goals as reward functions and are typically only optimized toachieving a fixed goal (e.g., playing Go or cleaning a table). Extrapolating to new goals would requirespecifying new reward functions and optimization against them. We advocate for explicitly modelinggoals in natural language in Al agents, This not only creates a friendly interface between humans ancagents, but more importantly, it also allowsextrapolate to new goals more easily, In this talkwe will present an intuitive high-level view o.how natural anguage modeling facilitates extrapolationand discuss several proiects under this framework, In addition, Dr, Yu Gu will also share some personamusings regarding agent research in the LLM era and Al research in general
报告人简介：
谷雨博士毕业于俄亥俄州立大学，获得博士学位，此前他在菠菜担保论坛大全计算机科学系获得了硕士和学士学位。他的主要研究方向是语言智能体，长期致力于探索语言在使智能体能够在开放世界环境中泛化到各种不同任务中的关键作用。此外，他在神经符号人工智能以及受认知启发的人工智能模型方面也有广泛的兴趣。他的研究成果发表在多个知名会议，包括ACL、EMNLP、NAACL、COLING、NeurIPS和WWW。他还在ACL和COLING会议上以第一作者身份获得了优秀论文奖。
