# Synergising Human-like Responses and Machine Intelligence for Planning in Disaster Response

arXiv: https://arxiv.org/abs/2404.09877 · HTML: https://mlsanity.com/paper/2404.09877/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 8 references that are in the map.
- First posted on arXiv 15 Apr 2024.

## Builds on (most cited first, 4 of 4)

- [Thinking Fast and Slow with Deep Learning and Tree Search](https://mlsanity.com/paper/1705.08439/) (2017)
- [SwiftSage: A Generative Agent with Fast and Slow Thinking for Complex Interactive Tasks](https://mlsanity.com/paper/2305.17390/) (2023)
- [Consciousness in Artificial Intelligence: Insights from the Science of Consciousness](https://mlsanity.com/paper/2308.08708/) (2023)
- [Thinking Fast and Slow in AI](https://mlsanity.com/paper/2010.06002/) (2020)

## Built on by (most cited first, 3 of 3)

- [VLM-RRT: Vision Language Model Guided RRT Search for Autonomous UAV Navigation](https://mlsanity.com/paper/2505.23267/) (2025)
- [Human-Robot Collaborative Minimum Time Search through Sub-priors in Ant Colony Optimization](https://mlsanity.com/paper/2410.00517/) (2024)
- [Human-AI Use Patterns for Decision-Making in Disaster Scenarios: A Systematic Review](https://mlsanity.com/paper/2509.12034/) (2025)

## Abstract (arXiv)

In the rapidly changing environments of disaster response, planning and decision-making for autonomous agents involve complex and interdependent choices. Although recent advancements have improved traditional artificial intelligence (AI) approaches, they often struggle in such settings, particularly when applied to agents operating outside their well-defined training parameters. To address these challenges, we propose an attention-based cognitive architecture inspired by Dual Process Theory (DPT). This framework integrates, in an online fashion, rapid yet heuristic (human-like) responses (System 1) with the slow but optimized planning capabilities of machine intelligence (System 2). We illustrate how a supervisory controller can dynamically determine in real-time the engagement of either system to optimize mission objectives by assessing their performance across a number of distinct attributes. Evaluated for trajectory planning in dynamic environments, our framework demonstrates that this synergistic integration effectively manages complex tasks by optimizing multiple mission objectives.