Summary of Synergising Human-like Responses and Machine Intelligence For Planning in Disaster Response, by Savvas Papaioannou et al.
Synergising Human-like Responses and Machine Intelligence for Planning in Disaster Response
by Savvas Papaioannou, Panayiotis Kolios, Christos G. Panayiotou, Marios M. Polycarpou
First submitted to arxiv on: 15 Apr 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed attention-based cognitive architecture is an innovative approach to improve autonomous agents’ decision-making in rapidly changing disaster response environments. Inspired by Dual Process Theory (DPT), the framework integrates heuristic and optimized planning capabilities, enabling real-time assessment of performance across various attributes. This synergy optimizes mission objectives in complex tasks, as demonstrated through trajectory planning experiments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper proposes a new way for autonomous agents to make decisions during disaster responses. It’s like having two brains: one that makes quick decisions and another that plans carefully. The researchers developed a special system that can switch between these two approaches depending on the situation. They tested this system with planning trajectories in changing environments and found that it works well. |
Keywords
» Artificial intelligence » Attention
