# Deceptive Decision-Making Under Uncertainty

arXiv: https://arxiv.org/abs/2109.06740 · HTML: https://mlsanity.com/paper/2109.06740/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 44 references that are in the map.
- First posted on arXiv 14 Sep 2021.

## Builds on (most cited first, 6 of 6)

- [Reinforcement Learning with Deep Energy-Based Policies](https://mlsanity.com/paper/1702.08165/) (2017)
- [Active Goal Recognition](https://mlsanity.com/paper/1909.11173/) (2019)
- [A Unified Framework for Planning in Adversarial and Cooperative Environments](https://mlsanity.com/paper/1802.06137/) (2018)
- [Deception in Optimal Control](https://mlsanity.com/paper/1805.03090/) (2018)
- [Deceptive Labeling: Hypergames on Graphs for Stealthy Deception](https://mlsanity.com/paper/2004.05213/) (2020)
- [A Unifying Bayesian Formulation of Measures of Interpretability in Human-AI](https://mlsanity.com/paper/2104.10743/) (2021)

## Built on by (most cited first, 8 of 8)

- [Deceptive Reinforcement Learning in Model-Free Domains](https://mlsanity.com/paper/2303.10838/) (2023)
- [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 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)
- [Optimal sensor deception in stochastic environments with partial observability to mislead a robot to a decoy goal](https://mlsanity.com/paper/2503.05972/) (2025)
- [Value of Information-based Deceptive Path Planning Under Adversarial Interventions](https://mlsanity.com/paper/2503.24284/) (2025)
- [Repeated Deceptive Path Planning against Learnable Observer](https://mlsanity.com/paper/2605.07174/) (2026)

## Abstract (arXiv)

We study the design of autonomous agents that are capable of deceiving outside observers about their intentions while carrying out tasks in stochastic, complex environments. By modeling the agent's behavior as a Markov decision process, we consider a setting where the agent aims to reach one of multiple potential goals while deceiving outside observers about its true goal. We propose a novel approach to model observer predictions based on the principle of maximum entropy and to efficiently generate deceptive strategies via linear programming. The proposed approach enables the agent to exhibit a variety of tunable deceptive behaviors while ensuring the satisfaction of probabilistic constraints on the behavior. We evaluate the performance of the proposed approach via comparative user studies and present a case study on the streets of Manhattan, New York, using real travel time distributions.