mlsanityData to 7 Oct 2026

An AIC-based approach for articulating unpredictable problems in open complex environments

arXiv 2403.14697 ↗First posted 15 Mar 2024cs.CY cs.AI cs.SEcited by 0

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Abstract (arXiv)

This research paper presents an approach to enhancing the predictive capability of architects in the design and assurance of systems, focusing on systems operating in dynamic and unpredictable environments. By adopting a systems approach, we aim to improve architects' predictive capabilities in designing dependable systems (for example, ML-based systems). An aerospace case study is used to illustrate the approach. Multiple factors (challenges) influencing aircraft detection are identified, demonstrating the effectiveness of our approach in a complex operational setting. Our approach primarily aimed to enhance the architect's predictive capability.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 3 of its 11 references that are in the map. First posted on arXiv 15 Mar 2024.

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3 of 11 references.

References · 3

  1. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,302
  2. 2023Navigate through Enigmatic Labyrinth A Survey of Chain of Thought Reasoning: Advances, Frontiers and Futurecited by 184
  3. 2023AVOIDDS: Aircraft Vision-based Intruder Detection Dataset and Simulatorcited by 4

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