# Machine Common Sense Concept Paper

arXiv: https://arxiv.org/abs/1810.07528 · HTML: https://mlsanity.com/paper/1810.07528/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Oct 2018.

## Built on by (most cited first, 17 of 17)

- [Neural Theory-of-Mind? On the Limits of Social Intelligence in Large LMs](https://mlsanity.com/paper/2210.13312/) (2022)
- [A Systematic Investigation of Commonsense Knowledge in Large Language Models](https://mlsanity.com/paper/2111.00607/) (2021)
- [NegotiationToM: A Benchmark for Stress-testing Machine Theory of Mind on Negotiation Surrounding](https://mlsanity.com/paper/2404.13627/) (2024)
- [JointLK: Joint Reasoning with Language Models and Knowledge Graphs for Commonsense Question Answering](https://mlsanity.com/paper/2112.02732/) (2021)
- [Towards A Holistic Landscape of Situated Theory of Mind in Large Language Models](https://mlsanity.com/paper/2310.19619/) (2023)
- [Theory of Mind abilities of Large Language Models in Human-Robot Interaction : An Illusion?](https://mlsanity.com/paper/2401.05302/) (2024)
- [KM-BART: Knowledge Enhanced Multimodal BART for Visual Commonsense Generation](https://mlsanity.com/paper/2101.00419/) (2021)
- [Social World Models](https://mlsanity.com/paper/2509.00559/) (2025)
- [SalKG: Learning From Knowledge Graph Explanations for Commonsense Reasoning](https://mlsanity.com/paper/2104.08793/) (2021)
- [The Box is in the Pen: Evaluating Commonsense Reasoning in Neural Machine Translation](https://mlsanity.com/paper/2503.03308/) (2025)
- [Decker: Double Check with Heterogeneous Knowledge for Commonsense Fact Verification](https://mlsanity.com/paper/2305.05921/) (2023)
- [Generalizable Neuro-symbolic Systems for Commonsense Question Answering](https://mlsanity.com/paper/2201.06230/) (2022)
- [Skill Check: Some Considerations on the Evaluation of Gamemastering Models for Role-playing Games](https://mlsanity.com/paper/2309.13702/) (2023)
- [Divergences between Language Models and Human Brains](https://mlsanity.com/paper/2311.09308/) (2023)
- [Zero-shot Commonsense Reasoning over Machine Imagination](https://mlsanity.com/paper/2410.09329/) (2024)
- [How to Design and Deliver Courses for Higher Education in the AI Era: Insights from Exam Data Analysis](https://mlsanity.com/paper/2308.02441/) (2023)
- [Enhancing Zero-shot Commonsense Reasoning by Integrating Visual Knowledge via Machine Imagination](https://mlsanity.com/paper/2603.05040/) (2026)

## Abstract (arXiv)

This paper summarizes some of the technical background, research ideas, and possible development strategies for achieving machine common sense. Machine common sense has long been a critical-but-missing component of Artificial Intelligence (AI). Recent advances in machine learning have resulted in new AI capabilities, but in all of these applications, machine reasoning is narrow and highly specialized. Developers must carefully train or program systems for every situation. General commonsense reasoning remains elusive. The absence of common sense prevents intelligent systems from understanding their world, behaving reasonably in unforeseen situations, communicating naturally with people, and learning from new experiences. Its absence is perhaps the most significant barrier between the narrowly focused AI applications we have today and the more general, human-like AI systems we would like to build in the future. Machine common sense remains a broad, potentially unbounded problem in AI. There are a wide range of strategies that could be employed to make progress on this difficult challenge. This paper discusses two diverse strategies for focusing development on two different machine commonsense services: (1) a service that learns from experience, like a child, to construct computational models that mimic the core domains of child cognition for objects (intuitive physics), agents (intentional actors), and places (spatial navigation); and (2) service that learns from reading the Web, like a research librarian, to construct a commonsense knowledge repository capable of answering natural language and image-based questions about commonsense phenomena.