Deceptive Path Planning: A Bayesian Game Approach

TL;DR

This paper models deceptive path planning in adversarial scenarios as a Bayesian game, using PBNE to derive strategies where an attacker balances deception and efficiency, and a defender infers intent to allocate resources.

Contribution

It introduces a Bayesian game framework with PBNE for deceptive path planning, providing a computational approach for strategic motion in adversarial settings.

Findings

PBNE-based strategies outperform existing methods

Attacker employs stochastic path mixing for deception

Defender effectively infers attacker intent using Bayesian reasoning

Abstract

This paper investigates how an autonomous agent can transmit information through its motion in an adversarial setting. We consider scenarios where an agent must reach its goal while deceiving an intelligent observer about its destination. We model this interaction as a dynamic Bayesian game between a mobile Attacker with a privately known goal and a Defender who infers the Attacker's intent to allocate defensive resources effectively. We use Perfect Bayesian Nash Equilibrium (PBNE) as our solution concept and propose a computationally efficient approach to find it. In the resulting equilibrium, the Defender employs a simple Markovian strategy, while the Attacker strategically balances deception and goal efficiency by stochastically mixing shortest and non-shortest paths to manipulate the Defender's beliefs. Numerical experiments demonstrate the advantages of our PBNE-based strategies over existing methods based on one-sided optimization.