# Deceptive Planning for Resource Allocation

arXiv: https://arxiv.org/abs/2206.01306 · HTML: https://mlsanity.com/paper/2206.01306/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 39 references that are in the map.
- First posted on arXiv 2 Jun 2022.

## Builds on (most cited first, 11 of 11)

- [Convex optimization](https://mlsanity.com/paper/2106.01946/) (2021)
- [CVXPY: A Python-Embedded Modeling Language for Convex Optimization](https://mlsanity.com/paper/1603.00943/) (2016)
- [Enabling Robots to Communicate their Objectives](https://mlsanity.com/paper/1702.03465/) (2017)
- [Entropy Maximization for Markov Decision Processes Under Temporal Logic Constraints](https://mlsanity.com/paper/1807.03223/) (2018)
- [Deception in Optimal Control](https://mlsanity.com/paper/1805.03090/) (2018)
- [Deceptive Decision-Making Under Uncertainty](https://mlsanity.com/paper/2109.06740/) (2021)
- [Deception in Supervisory Control](https://mlsanity.com/paper/1902.00590/) (2019)
- [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)
- [Minimizing the Information Leakage Regarding High-Level Task Specifications](https://mlsanity.com/paper/1911.09889/) (2019)

## Built on by (most cited first, 2 of 2)

- [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)

## Abstract (arXiv)

We consider a team of autonomous agents that navigate in an adversarial environment and aim to achieve a task by allocating their resources over a set of target locations. An adversary in the environment observes the autonomous team's behavior to infer their objective and responds against the team. In this setting, we propose strategies for controlling the density of the autonomous team so that they can deceive the adversary regarding their objective while achieving the desired final resource allocation. We first develop a prediction algorithm based on the principle of maximum entropy to express the team's behavior expected by the adversary. Then, by measuring the deceptiveness via Kullback-Leibler divergence, we devise convex optimization-based planning algorithms that deceive the adversary by either exaggerating the behavior towards a decoy allocation strategy or creating ambiguity regarding the final allocation strategy. A user study with 320 participants demonstrates that the proposed algorithms are effective for deception and reveal the inherent biases of participants towards proximate goals.