DeepAI AI Chat
Log In Sign Up

Adversarial Plannning

by   Valentin Vie, et al.
Department of Defense
Penn State University

Planning algorithms are used in computational systems to direct autonomous behavior. In a canonical application, for example, planning for autonomous vehicles is used to automate the static or continuous planning towards performance, resource management, or functional goals (e.g., arriving at the destination, managing fuel fuel consumption). Existing planning algorithms assume non-adversarial settings; a least-cost plan is developed based on available environmental information (i.e., the input instance). Yet, it is unclear how such algorithms will perform in the face of adversaries attempting to thwart the planner. In this paper, we explore the security of planning algorithms used in cyber- and cyber-physical systems. We present two adversarial planning algorithms-one static and one adaptive-that perturb input planning instances to maximize cost (often substantially so). We evaluate the performance of the algorithms against two dominant planning algorithms used in commercial applications (D* Lite and Fast Downward) and show both are vulnerable to extremely limited adversarial action. Here, experiments show that an adversary is able to increase plan costs in 66.9 only removing a single action from the actions space (D* Lite) and render 70 of instances from an international planning competition unsolvable by removing only three actions (Fast Forward). Finally, we show that finding an optimal perturbation in any search-based planning system is NP-hard.


page 8

page 15


Marvin: A Heuristic Search Planner with Online Macro-Action Learning

This paper describes Marvin, a planner that competed in the Fourth Inter...

Secure Minimum Time Planning Under Environmental Uncertainty: an Extended Treatment

Cyber Physical Systems (CPS) are becoming ubiquitous and affect the phys...

Soft Goals Can Be Compiled Away

Soft goals extend the classical model of planning with a simple model of...

Une approche totalement instanciée pour la planification HTN

Many planning techniques have been developed to allow autonomous systems...

Machine Learning Meets Quantitative Planning: Enabling Self-Adaptation in Autonomous Robots

Modern cyber-physical systems (e.g., robotics systems) are typically com...

Recognising Affordances in Predicted Futures to Plan with Consideration of Non-canonical Affordance Effects

We propose a novel system for action sequence planning based on a combin...