# A Unified Framework for Planning in Adversarial and Cooperative Environments

arXiv: https://arxiv.org/abs/1802.06137 · HTML: https://mlsanity.com/paper/1802.06137/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Feb 2018.

## Built on by (most cited first, 15 of 15)

- [Deceptive Reinforcement Learning for Privacy-Preserving Planning](https://mlsanity.com/paper/2102.03022/) (2021)
- [Human-AI Symbiosis: A Survey of Current Approaches](https://mlsanity.com/paper/2103.09990/) (2021)
- [Deceptive Decision-Making Under Uncertainty](https://mlsanity.com/paper/2109.06740/) (2021)
- [A Unifying Bayesian Formulation of Measures of Interpretability in Human-AI](https://mlsanity.com/paper/2104.10743/) (2021)
- [A Mental-Model Centric Landscape of Human-AI Symbiosis](https://mlsanity.com/paper/2202.09447/) (2022)
- [Generating Active Explicable Plans in Human-Robot Teaming](https://mlsanity.com/paper/2109.08834/) (2021)
- [Planning for Proactive Assistance in Environments with Partial Observability](https://mlsanity.com/paper/2105.00525/) (2021)
- [Anticipatory Counterplanning](https://mlsanity.com/paper/2203.16171/) (2022)
- [Planning for Attacker Entrapment in Adversarial Settings](https://mlsanity.com/paper/2303.00822/) (2023)
- [On the Transit Obfuscation Problem](https://mlsanity.com/paper/2402.07420/) (2024)
- [Generalising Planning Environment Redesign](https://mlsanity.com/paper/2402.07799/) (2024)
- [On Almost-Sure Intention Deception Planning that Exploits Imperfect Observers](https://mlsanity.com/paper/2209.00573/) (2022)
- ["Teammates, Am I Clear?": Analysing Legible Behaviours in Teams](https://mlsanity.com/paper/2507.21631/) (2025)
- [Inducing Comparability of Factorised Probability Distributions](https://mlsanity.com/paper/2607.20502/) (2026)
- [Probabilistic Plan Legibility with Off-the-shelf Planners](https://mlsanity.com/paper/2610.00065/) (2026)

## Abstract (arXiv)

Users of AI systems may rely upon them to produce plans for achieving desired objectives. Such AI systems should be able to compute obfuscated plans whose execution in adversarial situations protects privacy, as well as legible plans which are easy for team members to understand in cooperative situations. We develop a unified framework that addresses these dual problems by computing plans with a desired level of comprehensibility from the point of view of a partially informed observer. For adversarial settings, our approach produces obfuscated plans with observations that are consistent with at least k goals from a set of decoy goals. By slightly varying our framework, we present an approach for goal legibility in cooperative settings which produces plans that achieve a goal while being consistent with at most j goals from a set of confounding goals. In addition, we show how the observability of the observer can be controlled to either obfuscate or clarify the next actions in a plan when the goal is known to the observer. We present theoretical results on the complexity analysis of our problems. We demonstrate the execution of obfuscated and legible plans in a cooking domain using a physical robot Fetch. We also provide an empirical evaluation to show the feasibility and usefulness of our approaches using IPC domains.