mlsanityData to 14 Oct 2026

Strategic Decisions Survey, Taxonomy, and Future Directions from Artificial Intelligence Perspective

arXiv 2210.12373 ↗First posted 22 Oct 2022cs.AIcited by 3

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Abstract (arXiv)

Strategic Decision-Making is always challenging because it is inherently uncertain, ambiguous, risky, and complex. It is the art of possibility. We develop a systematic taxonomy of decision-making frames that consists of 6 bases, 18 categorical, and 54 frames. We aim to lay out the computational foundation that is possible to capture a comprehensive landscape view of a strategic problem. Compared with traditional models, it covers irrational, non-rational and rational frames c dealing with certainty, uncertainty, complexity, ambiguity, chaos, and ignorance.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 3 of its 137 references that are in the map. First posted on arXiv 22 Oct 2022.

Builds on

3 of 137 references.

References · 3

  1. 2017Multimodal Machine Learning: A Survey and Taxonomycited by 1,013
  2. 2018Object Detection with Deep Learning: A Reviewcited by 528
  3. 2017Belief State Planning for Autonomously Navigating Urban Intersectionscited by 11

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 2; 2024: 0; 2025: 0; 2026: 1.

3 papers.