AutoToM自动构建心理模型，让机器人更懂人的心思。
AutoToM: Scaling Model-based Mental Inference via Automated Agent Modeling
- 用自动化贝叶斯逆向规划构建可迭代优化的心理模型
- 在5个基准上超越现有方法，且推理更接近人类
- 适合需要理解他人意图的智能体决策场景
心智理论（ToM）是理解他人行为背后心理状态的关键能力，对构建社交智能体至关重要。当前方法要么依赖大语言模型提示，易出系统性错误；要么使用人工设计的刚性代理模型，虽稳健但泛化能力差。本文提出AutoToM，一种可扩展、鲁棒且可解释的自动化代理建模方法。给定一个ToM问题，AutoToM首先生成初始代理模型，再基于该模型进行自动化贝叶斯逆向规划，利用大语言模型作为后端支持。根据推理不确定性，它通过引入更多心理变量和/或增加上下文时间步，迭代优化模型。在五个不同基准上，AutoToM的表现优于现有ToM方法，甚至超过大型推理模型。此外，AutoToM能生成类人的置信度估计，并支持具身决策中的在线心理推断。
原文摘要 · Abstract (English)
Theory of Mind (ToM), the ability to understand people's minds based on their behavior, is key to developing socially intelligent agents. Current approaches to ToM reasoning either rely on prompting Large Language Models (LLMs), which are prone to systematic errors, or use handcrafted, rigid agent models for model-based inference, which are more robust but fail to generalize across domains. In this work, we introduce AutoToM, an automated agent modeling method for scalable, robust, and interpretable mental inference. Given a ToM problem, AutoToM first proposes an initial agent model and then performs automated Bayesian inverse planning based on this model, leveraging an LLM backend. Guided by inference uncertainty, it iteratively refines the model by introducing additional mental variables and/or incorporating more timesteps in the context. Across five diverse benchmarks, AutoToM outperforms existing ToM methods and even large reasoning models. Additionally, we show that AutoToM can produce human-like confidence estimates and enable online mental inference for embodied decision-making.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。