arXiv cs.AI· Solim LeGris, Philipp Mondorf, Jinran Jin, Akshay K. Jagadish, Brenden M. Lake, Todd M. Gureckis·· 11 小时前AI 评分38
研究提出 BCSS：自动合成人类与机器推理的可解释符号模型
Synthesizing symbolic models of human and machine reasoning at scale
AI 导读
研究者提出 Behavior-Conditioned Symbolic Synthesis（BCSS），可自动搜索能可验证复现行为的可解释确定性符号程序，并用它建模人类与机器在数百个高难度推理问题上的表现。分析发现，人和机器都倾向过度简化、采用只部分解释观察结果的规则，并错误推断问题特征间的依赖关系；这些共同特征受可用资源调节，推理投入程度可预测机器简化和解释观察结果的程度。
正文 · 原文
Abstract:Symbolic models offer a powerful account of cognition, but their scope has historically been limited by the need for hand-engineering and computational tractability. We show how advances in artificial intelligence (AI) can scale symbolic modeling and help overcome these engineering limitations. We introduce Behavior-Conditioned Symbolic Synthesis (BCSS)---an approach for automated search of interpretable, deterministic symbolic programs that verifiably reproduce behavior---and apply it to model both human and machine behavior across hundreds of challenging reasoning problems. Analyzing these programs reveals that both people and machines tend to oversimplify, adopt rules that are partial explanations for their observations, and misinfer dependencies between problem features. These shared signatures are modulated by available resources, with reasoning effort predicting the extent to which machines simplify and explain their observations. Together, these results point to underlying mechanisms reminiscent of satisficing in both people and machines, and illustrate how BCSS offers a general approach for interpreting and comparing rule-based reasoning of humans and black-box AI systems.
| Subjects: | Artificial Intelligence (cs.AI); Machine Learning (cs.LG) |
| Cite as: | arXiv:2611.07088 [cs.AI] |
| (or arXiv:2611.07088v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2611.07088 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Solim LeGris [view email]
[v1]
Wed, 4 Nov 2026 19:51:02 UTC (3,269 KB)
来源：arXiv cs.AI · arxiv.org