# Value of Information-based Deceptive Path Planning Under Adversarial Interventions

arXiv: https://arxiv.org/abs/2503.24284 · HTML: https://mlsanity.com/paper/2503.24284/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 35 references that are in the map.
- First posted on arXiv 31 Mar 2025.

## Builds on (most cited first, 13 of 13)

- [Deceptive Reinforcement Learning for Privacy-Preserving Planning](https://mlsanity.com/paper/2102.03022/) (2021)
- [Deception in Optimal Control](https://mlsanity.com/paper/1805.03090/) (2018)
- [Deceptive Decision-Making Under Uncertainty](https://mlsanity.com/paper/2109.06740/) (2021)
- [Deceptive Reinforcement Learning in Model-Free Domains](https://mlsanity.com/paper/2303.10838/) (2023)
- [Deception in Supervisory Control](https://mlsanity.com/paper/1902.00590/) (2019)
- [Identity Concealment Games: How I Learned to Stop Revealing and Love the Coincidences](https://mlsanity.com/paper/2105.05377/) (2021)
- [Deceptive Planning for Resource Allocation](https://mlsanity.com/paper/2206.01306/) (2022)
- [Quantitative Planning with Action Deception in Concurrent Stochastic Games](https://mlsanity.com/paper/2301.01349/) (2023)
- [Integrated Resource Allocation and Strategy Synthesis in Safety Games on Graphs with Deception](https://mlsanity.com/paper/2407.14436/) (2024)
- [Planning for Attacker Entrapment in Adversarial Settings](https://mlsanity.com/paper/2303.00822/) (2023)
- [Deceptive Path Planning via Reinforcement Learning with Graph Neural Networks](https://mlsanity.com/paper/2402.06552/) (2024)
- [On the Transit Obfuscation Problem](https://mlsanity.com/paper/2402.07420/) (2024)
- [Deceptive Sequential Decision-Making via Regularized Policy Optimization](https://mlsanity.com/paper/2501.18803/) (2025)

## Abstract (arXiv)

Existing methods for deceptive path planning (DPP) address the problem of designing paths that conceal their true goal from a passive, external observer. Such methods do not apply to problems where the observer has the ability to perform adversarial interventions to impede the path planning agent. In this paper, we propose a novel Markov decision process (MDP)-based model for the DPP problem under adversarial interventions and develop new value of information (VoI) objectives to guide the design of DPP policies. Using the VoI objectives we propose, path planning agents deceive the adversarial observer into choosing suboptimal interventions by selecting trajectories that are of low informational value to the observer. Leveraging connections to the linear programming theory for MDPs, we derive computationally efficient solution methods for synthesizing policies for performing DPP under adversarial interventions. In our experiments, we illustrate the effectiveness of the proposed solution method in achieving deceptiveness under adversarial interventions and demonstrate the superior performance of our approach to both existing DPP methods and conservative path planning approaches on illustrative gridworld problems.