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[2404.09877] Synergising Human-like Responses and Machine Intelligence for Planning in Disaster Response
Summary
This paper presents a novel cognitive architecture that combines human-like responses with machine intelligence for effective disaster response planning.
Why It Matters
As disaster response scenarios become increasingly complex, integrating human-like decision-making with machine intelligence can enhance the effectiveness of autonomous agents. This research addresses critical challenges in real-time planning and decision-making, potentially improving outcomes in emergency situations.
Key Takeaways
- Proposes an attention-based cognitive architecture inspired by Dual Process Theory.
- Integrates rapid human-like responses with machine intelligence for real-time decision-making.
- Demonstrates effectiveness in trajectory planning within dynamic environments.
- Optimizes mission objectives by dynamically assessing system performance.
- Addresses limitations of traditional AI approaches in unpredictable disaster scenarios.
Computer Science > Artificial Intelligence arXiv:2404.09877 (cs) [Submitted on 15 Apr 2024 (v1), last revised 21 Feb 2026 (this version, v3)] Title:Synergising Human-like Responses and Machine Intelligence for Planning in Disaster Response Authors:Savvas Papaioannou, Panayiotis Kolios, Christos G. Panayiotou, Marios M. Polycarpou View a PDF of the paper titled Synergising Human-like Responses and Machine Intelligence for Planning in Disaster Response, by Savvas Papaioannou and 3 other authors View PDF HTML (experimental) Abstract: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 environm...