Summary of Mars: Situated Inductive Reasoning in An Open-world Environment, by Xiaojuan Tang et al.
Mars: Situated Inductive Reasoning in an Open-World Environment
by Xiaojuan Tang, Jiaqi Li, Yitao Liang, Song-chun Zhu, Muhan Zhang, Zilong Zheng
First submitted to arxiv on: 10 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a novel environment called Mars, designed to evaluate large language models’ ability to perform situated inductive reasoning. This task involves inducing new general knowledge from a specific environment and performing decision-making tasks in context. The authors design counter-commonsense game mechanisms that modify terrain, survival settings, and task dependencies, requiring agents to actively interact with their surroundings and derive useful rules. They test various reinforcement learning-based and language model-based methods on the Mars benchmark, finding that they all struggle with situated inductive reasoning. Furthermore, the paper explores induction from reflection, where agents are instructed to perform inductive reasoning from history trajectories. The results highlight the importance of inductive reasoning in Mars, aiming to galvanize advancements in situated inductive reasoning and develop the next generation of AI systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Mars is a new environment designed for large language models (LLMs) to learn situated inductive reasoning. This means learning from specific situations and making decisions based on that learning. The authors create games with rules that change depending on where you are and what you’re doing. They tested several ways LLMs learned, but they all struggled with this new kind of thinking. The paper also shows how agents can learn by looking back at their history. This is important for building AI systems that can reason in different situations. |
Keywords
* Artificial intelligence * Language model * Reinforcement learning
