Search and Rescue in the Face of Uncertain Threats
Not on the map. Why?
Abstract (arXiv)
We consider a search problem in which one or more targets must be rescued by a search party, or Searcher. The targets may be survivors of some natural disaster, or prisoners held by an adversary. The targets are hidden among a finite set of locations, but when a location is searched, there is a known probability that the search will come to an end, perhaps because the Searcher becomes trapped herself, or is captured by the adversary. If this happens before all the targets have been recovered, then the rescue attempt is deemed a failure. The objective is to find the search that maximizes the probability of recovering all the targets. We present and solve a game theoretic model for this problem, by placing it in a more general framework that encompasses another game previously introduced by the author. We also consider an extension to the game in which the targets are hidden on the vertices of a graph. In the case that there is only one target, we give a solution of the game played on a tree.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). First posted on arXiv 13 Feb 2019.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2024: 1; 2025: 2.
3 papers.
- 2024COMPrompter: reconceptualized segment anything model with multiprompt network for camouflaged object detectioncited by 6
- 2025CamoSAM2: SAM2-oriented Prompt Auto-Refinement for Video Camouflaged Object Detectioncited by 1
- 2025Phantom-Insight: Adaptive Multi-cue Fusion for Video Camouflaged Object Detection with Multimodal LLMcited by 0
- 2025Phantom-Insight: Adaptive Multi-cue Fusion for Video Camouflaged Object Detection with Multimodal LLM8 Sep 2025
- 2025CamoSAM2: SAM2-oriented Prompt Auto-Refinement for Video Camouflaged Object Detection1 Apr 2025
- 2024COMPrompter: reconceptualized segment anything model with multiprompt network for camouflaged object detection28 Nov 2024