A Unified Framework for Planning in Adversarial and Cooperative Environments

TL;DR

This paper introduces a unified framework for generating plans that balance obfuscation and legibility, enabling AI systems to protect privacy or enhance understanding depending on the context, with theoretical and empirical validation.

Contribution

It presents a novel unified approach for planning that handles both obfuscation in adversarial settings and legibility in cooperative environments, with complexity analysis and real-world experiments.

Findings

Successfully generated obfuscated plans that hide goals from observers.

Produced legible plans that clarify goals for team members.

Validated approach in robotic and IPC domains.

Abstract

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.