Dynamic Hypergames for Synthesis of Deceptive Strategies with Temporal Logic Objectives

TL;DR

This paper introduces a hypergame-based approach for synthesizing deceptive strategies in adversarial environments with temporal logic objectives, enabling an agent to influence and manipulate an adversary's perception to achieve its goals.

Contribution

It develops a novel hypergame model capturing asymmetric information and proposes a synthesis method for deceptive strategies that maximize task satisfaction probability.

Findings

Deceptive strategies improve success rates in adversarial planning.

The approach effectively influences adversary perception and behavior.

Robot motion planning examples validate the method's correctness.

Abstract

In this paper, we study the use of deception for strategic planning in adversarial environments. We model the interaction between the agent (player 1) and the adversary (player 2) as a two-player concurrent game in which the adversary has incomplete information about the agent's task specification in temporal logic. During the online interaction, the adversary can infer the agent's intention from observations and adapt its strategy so as to prevent the agent from satisfying the task. To plan against such an adaptive opponent, the agent must leverage its knowledge about the adversary's incomplete information to influence the behavior of the opponent, and thereby being deceptive. To synthesize a deceptive strategy, we introduce a class of hypergame models that capture the interaction between the agent and its adversary given asymmetric, incomplete information. A hypergame is a hierarchy of games, perceived differently by the agent and its adversary. We develop the solution concept of this class of hypergames and show that the subjectively rationalizable strategy for the agent is deceptive and maximizes the probability of satisfying the task in temporal logic. This deceptive strategy is obtained by modeling the opponent evolving perception of the interaction and integrating the opponent model into proactive planning. Following the deceptive strategy, the agent chooses actions to influence the game history as well as to manipulate the adversary's perception so that it takes actions that benefit the goal of the agent. We demonstrate the correctness of our deceptive planning algorithm using robot motion planning examples with temporal logic objectives and design a detection mechanism to notify the agent of potential errors in modeling of the adversary's behavior.