# Combining Fast and Slow Thinking for Human-like and Efficient Navigation in Constrained Environments

arXiv: https://arxiv.org/abs/2201.07050 · HTML: https://mlsanity.com/paper/2201.07050/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Placed in Test-time compute and efficient thinking, one of 17 directions in LLM reasoning.
- Builds on 10 of its 42 references that are in the map.
- First posted on arXiv 18 Jan 2022.

## Placed in

- LLM reasoning / [Test-time compute and efficient thinking](https://mlsanity.com/lr/test-time-compute-and-efficient-thinking/)

## Builds on (most cited first, 10 of 10)

- [Thinking Fast and Slow with Deep Learning and Tree Search](https://mlsanity.com/paper/1705.08439/) (2017)
- [The Next Decade in AI: Four Steps Towards Robust Artificial Intelligence](https://mlsanity.com/paper/2002.06177/) (2020)
- [Automated Algorithm Selection: Survey and Perspectives](https://mlsanity.com/paper/1811.11597/) (2018)
- [The Consciousness Prior](https://mlsanity.com/paper/1709.08568/) (2017)
- [Thinking Fast and Slow in AI](https://mlsanity.com/paper/2010.06002/) (2020)
- [Thinking Fast and Slow: Optimization Decomposition Across Timescales](https://mlsanity.com/paper/1704.07785/) (2017)
- [Building Ethically Bounded AI](https://mlsanity.com/paper/1812.03980/) (2018)
- [Deep Reasoning Networks: Thinking Fast and Slow](https://mlsanity.com/paper/1906.00855/) (2019)
- [Thinking, Fast and Slow: Combining Vector Spaces and Knowledge Graphs](https://mlsanity.com/paper/1708.03310/) (2017)
- [Interleaving Fast and Slow Decision Making](https://mlsanity.com/paper/2010.16244/) (2020)

## Built on by (most cited first, 5 of 5)

- [ChatABL: Abductive Learning via Natural Language Interaction with ChatGPT](https://mlsanity.com/paper/2304.11107/) (2023)
- [Improving deep learning with prior knowledge and cognitive models: A survey on enhancing explainability, adversarial robustness and zero-shot learning](https://mlsanity.com/paper/2403.07078/) (2024)
- [From Heuristic to Analytic: Cognitively Motivated Strategies for Coherent Physical Commonsense Reasoning](https://mlsanity.com/paper/2310.18364/) (2023)
- [Achieving Trustworthy Real-Time Decision Support Systems with Low-Latency Interpretable AI Models](https://mlsanity.com/paper/2506.20018/) (2025)
- [Synthesizing Evolving Symbolic Representations for Autonomous Systems](https://mlsanity.com/paper/2409.11756/) (2024)

## Abstract (arXiv)

Current AI systems lack several important human capabilities, such as adaptability, generalizability, self-control, consistency, common sense, and causal reasoning. We believe that existing cognitive theories of human decision making, such as the thinking fast and slow theory, can provide insights on how to advance AI systems towards some of these capabilities. In this paper, we propose a general architecture that is based on fast/slow solvers and a metacognitive component. We then present experimental results on the behavior of an instance of this architecture, for AI systems that make decisions about navigating in a constrained environment. We show how combining the fast and slow decision modalities allows the system to evolve over time and gradually pass from slow to fast thinking with enough experience, and that this greatly helps in decision quality, resource consumption, and efficiency.