# Deception in Supervisory Control

arXiv: https://arxiv.org/abs/1902.00590 · HTML: https://mlsanity.com/paper/1902.00590/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Feb 2019.

## Built on by (most cited first, 6 of 6)

- [Deceptive Planning for Resource Allocation](https://mlsanity.com/paper/2206.01306/) (2022)
- [Covert Planning against Imperfect Observers](https://mlsanity.com/paper/2310.16791/) (2023)
- [Deceptive Sequential Decision-Making via Regularized Policy Optimization](https://mlsanity.com/paper/2501.18803/) (2025)
- [On Almost-Sure Intention Deception Planning that Exploits Imperfect Observers](https://mlsanity.com/paper/2209.00573/) (2022)
- [Value of Information-based Deceptive Path Planning Under Adversarial Interventions](https://mlsanity.com/paper/2503.24284/) (2025)
- [Masking Radar Cognition under Adversarial Surveillance: A Distributional Privacy Framework](https://mlsanity.com/paper/2609.07428/) (2026)

## Abstract (arXiv)

The use of deceptive strategies is important for an agent that attempts not to reveal his intentions in an adversarial environment. We consider a setting in which a supervisor provides a reference policy and expects an agent to follow the reference policy and perform a task. The agent may instead follow a different, deceptive policy to achieve a different task. We model the environment and the behavior of the agent with a Markov decision process, represent the tasks of the agent and the supervisor with reachability specifications, and study the synthesis of optimal deceptive policies for such agents. We also study the synthesis of optimal reference policies that prevent deceptive strategies of the agent and achieve the supervisor's task with high probability. We show that the synthesis of optimal deceptive policies has a convex optimization problem formulation, while the synthesis of optimal reference policies requires solving a nonconvex optimization problem. We also show that the synthesis of optimal reference policies is NP-hard.