On Almost-Sure Intention Deception Planning that Exploits Imperfect Observers

TL;DR

This paper develops algorithms for intention deception in stochastic systems modeled as MDPs, enabling an attacker to almost surely reach targets while hiding intentions from defenders with limited observations.

Contribution

It introduces qualitative deception planning algorithms for both action-visible and action-invisible defenders, ensuring almost-sure attack success and deception.

Findings

Algorithms are correct and complete.

Strategies achieve almost-sure attack success.

Effective deception against partial observation defenders.

Abstract

Intention deception involves computing a strategy which deceives the opponent into a wrong belief about the agent's intention or objective. This paper studies a class of probabilistic planning problems with intention deception and investigates how a defender's limited sensing modality can be exploited by an attacker to achieve its attack objective almost surely (with probability one) while hiding its intention. In particular, we model the attack planning in a stochastic system modeled as a Markov decision process (MDP). The attacker is to reach some target states while avoiding unsafe states in the system and knows that his behavior is monitored by a defender with partial observations. Given partial state observations for the defender, we develop qualitative intention deception planning algorithms that construct attack strategies to play against an action-visible defender and an action-invisible defender, respectively. The synthesized attack strategy not only ensures the attack objective is satisfied almost surely but also deceives the defender into believing that the observed behavior is generated by a normal/legitimate user and thus failing to detect the presence of an attack. We show the proposed algorithms are correct and complete and illustrate the deceptive planning methods with examples.