Saved in:
| Main Authors: | , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2510.04050 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866911192141791232 |
|---|---|
| author | Samanta, Sukanya Reddy, Manohar |
| author_facet | Samanta, Sukanya Reddy, Manohar |
| contents | The interdiction of escaping adversaries in urban networks is a critical security challenge. State-of-the-art game-theoretic models, such as the Escape Interdiction Game (EIG), provide comprehensive frameworks but assume a highly dynamic interaction and entail significant computational complexity, which can be prohibitive for real-time applications. This paper investigates a crucial sub-problem: an evader's optimal pathfinding calculus when faced with a static or pre-determined defender deployment. We propose the Dynamic Programming for Evader Route Optimization (DPERO) algorithm, which models the environment as a graph with probabilistic risks at various nodes. By transforming the multiplicative survival objective into an additive cost function using logarithms, we frame the task as a shortest path problem solvable with value iteration. This approach allows for the efficient computation of a path that optimally balances safety and distance. Experimental results on simulated grid networks demonstrate that DPERO identifies routes with significantly higher survival probabilities compared to naive shortest-path baselines, validating its efficacy as a practical tool for vulnerability analysis and strategic planning. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_04050 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Dynamic Programming Approach to Evader Pathfinding in Static Pursuit Scenarios Samanta, Sukanya Reddy, Manohar Data Structures and Algorithms The interdiction of escaping adversaries in urban networks is a critical security challenge. State-of-the-art game-theoretic models, such as the Escape Interdiction Game (EIG), provide comprehensive frameworks but assume a highly dynamic interaction and entail significant computational complexity, which can be prohibitive for real-time applications. This paper investigates a crucial sub-problem: an evader's optimal pathfinding calculus when faced with a static or pre-determined defender deployment. We propose the Dynamic Programming for Evader Route Optimization (DPERO) algorithm, which models the environment as a graph with probabilistic risks at various nodes. By transforming the multiplicative survival objective into an additive cost function using logarithms, we frame the task as a shortest path problem solvable with value iteration. This approach allows for the efficient computation of a path that optimally balances safety and distance. Experimental results on simulated grid networks demonstrate that DPERO identifies routes with significantly higher survival probabilities compared to naive shortest-path baselines, validating its efficacy as a practical tool for vulnerability analysis and strategic planning. |
| title | A Dynamic Programming Approach to Evader Pathfinding in Static Pursuit Scenarios |
| topic | Data Structures and Algorithms |
| url | https://arxiv.org/abs/2510.04050 |