# Optimal sensor deception in stochastic environments with partial observability to mislead a robot to a decoy goal

arXiv: https://arxiv.org/abs/2503.05972 · HTML: https://mlsanity.com/paper/2503.05972/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 21 references that are in the map.
- First posted on arXiv 7 Mar 2025.

## Builds on (most cited first, 10 of 10)

- [Stochastic Finite State Control of POMDPs with LTL Specifications](https://mlsanity.com/paper/2001.07679/) (2020)
- [Signaling Friends and Head-Faking Enemies Simultaneously: Balancing Goal Obfuscation and Goal Legibility](https://mlsanity.com/paper/1905.10672/) (2019)
- [Deceptive Decision-Making Under Uncertainty](https://mlsanity.com/paper/2109.06740/) (2021)
- [Deceptive Labeling: Hypergames on Graphs for Stealthy Deception](https://mlsanity.com/paper/2004.05213/) (2020)
- [Decoy Allocation Games on Graphs with Temporal Logic Objectives](https://mlsanity.com/paper/2010.01208/) (2020)
- [Dynamic Hypergames for Synthesis of Deceptive Strategies with Temporal Logic Objectives](https://mlsanity.com/paper/2007.15726/) (2020)
- [Supervisor Synthesis to Thwart Cyber Attack with Bounded Sensor Reading Alterations](https://mlsanity.com/paper/1608.04103/) (2016)
- [Deceptive Planning for Resource Allocation](https://mlsanity.com/paper/2206.01306/) (2022)
- [Covert Planning against Imperfect Observers](https://mlsanity.com/paper/2310.16791/) (2023)
- [A Decentralized Shotgun Approach for Team Deception](https://mlsanity.com/paper/2406.17160/) (2024)

## Abstract (arXiv)

Deception is a common strategy adapted by autonomous systems in adversarial settings. Existing deception methods primarily focus on increasing opacity or misdirecting agents away from their goal or itinerary. In this work, we propose a deception problem aiming to mislead the robot towards a decoy goal through altering sensor events under a constrained budget of alteration. The environment along with the robot's interaction with it is modeled as a Partially Observable Markov Decision Process (POMDP), and the robot's action selection is governed by a Finite State Controller (FSC). Given a constrained budget for sensor event modifications, the objective is to compute a sensor alteration that maximizes the probability of the robot reaching a decoy goal. We establish the computational hardness of the problem by a reduction from the 0/1 Knapsack problem and propose a Mixed Integer Linear Programming (MILP) formulation to compute optimal deception strategies. We show the efficacy of our MILP formulation via a sequence of experiments.