时间逻辑目标下欺骗策略合成的动态超对策
Dynamic Hypergames for Synthesis of Deceptive Strategies with Temporal Logic Objectives
摘要：
在本文中，我们研究了在对抗性环境中使用欺骗进行战略规划。我们的代理（玩家1）和对手（玩家2）之间的互动模型作为一个两个玩家并发游戏中，对手有不完整的信息代理的任务规范的时间逻辑。在在线交互过程中，对手可以从观察中推断出Agent的意图，并调整其策略，以阻止Agent完成任务。为了计划对付这样一个适应性强的对手，智能体必须利用它对对手的不完整信息的了解来影响对手的行为，从而具有欺骗性。为了合成一个欺骗性的策略，我们引入了一类超博弈模型，捕捉代理和它的对手之间的相互作用不对称，不完整的信息。超博弈是一个层次的游戏，感知不同的代理和对手。我们开发了这类超博弈的解决方案的概念，并表明，主观合理化的策略，代理是欺骗性的，并最大限度地提高概率，满足任务的时间逻辑。这种欺骗性的策略是通过模拟对手不断变化的相互作用的感知，并将对手模型集成到主动规划中来获得的。在欺骗性策略之后，代理选择影响游戏历史的行动，以及操纵对手的感知，以便采取有利于代理目标的行动。我们证明了我们的欺骗性规划算法的正确性，使用机器人运动规划的例子与时间逻辑目标和设计一个检测机制，通知代理的潜在错误建模的对手的行为。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
