构建开放世界推理环境Mars，测试智能体从环境中归纳规则的能力。
Mars: Situated Inductive Reasoning in an Open-World Environment
- 设计反常识游戏机制，逼迫智能体主动交互并归纳新规则。
- 各类强化学习与大模型方法在该环境中表现均不佳，验证任务难度。
- 提出'反思归纳'策略，显著提升推理性能，适合研究自适应AI者参考。
大规模语言模型在知识密集型任务中表现优异，但多依赖预存知识。从特定环境主动推导通用知识并进行推理——即‘情境归纳推理’，对机器智能至关重要且极具挑战。本文设计Mars，一个专为情境归纳推理构建的交互式开放世界环境。通过修改地形、生存设定与任务依赖关系，引入反常识机制，同时遵循一定原则。在Mars中，智能体需主动与环境互动，从中提炼有用规则，并在具体情境下做出决策。我们在多种基于强化学习与大模型的方法上进行实验，发现它们均在此基准测试中表现不佳。此外，我们探索‘从反思中归纳’策略，即引导智能体基于历史轨迹进行归纳推理，结果表现更优，凸显归纳能力的重要性。Mars旨在推动情境归纳推理的发展，为下一代具备自适应与情境敏感性的智能系统奠定基础。
原文摘要 · Abstract (English)
Large Language Models (LLMs) trained on massive corpora have shown remarkable success in knowledge-intensive tasks. Yet, most of them rely on pre-stored knowledge. Inducing new general knowledge from a specific environment and performing reasoning with the acquired knowledge -- \textit{situated inductive reasoning}, is crucial and challenging for machine intelligence. In this paper, we design Mars, an interactive environment devised for situated inductive reasoning. It introduces counter-commonsense game mechanisms by modifying terrain, survival setting and task dependency while adhering to certain principles. In Mars, agents need to actively interact with their surroundings, derive useful rules and perform decision-making tasks in specific contexts. We conduct experiments on various RL-based and LLM-based methods, finding that they all struggle on this challenging situated inductive reasoning benchmark. Furthermore, we explore \textit{Induction from Reflection}, where we instruct agents to perform inductive reasoning from history trajectory. The superior performance underscores the importance of inductive reasoning in Mars. Through Mars, we aim to galvanize advancements in situated inductive reasoning and set the stage for developing the next generation of AI systems that can reason in an adaptive and context-sensitive way.
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