ABSTRACT

In dynamic environments, external events might occur and modify the environment without consent of intelligent agents. Plans of the agents might hence be disrupted and, worse, the agents might end up in dead-end states and no longer be able to achieve their goals. Hence, the agents should monitor the environment during plan execution and if they encounter a dangerous situation they should (reactively) act to escape from it.

In this paper, we introduce the notion of dangerous states that the agent might encounter during its plan execution in dynamic environments. We present a method for computing lower bound of dangerousness of a state after applying a sequence of actions. That method is leveraged in identifying situations in which the agent has to start acting to avoid danger. We present two types of such behaviour – purely reactive and proactive (eliminating the source of danger). The introduced concepts for planning with dangerous states are implemented and tested in two scenarios – a simple RPG-like game, called Dark Dungeon, and a platform game inspired by the Perestroika video game. The results show that reasoning with dangerous states achieves better success rate (reaching the goals) than naive planning or rule-based techniques.

摘要

在动态环境中，外部事件可能会在未经智能代理同意的情况下发生并修改环境。因此，代理的计划可能会被打乱，更糟糕的是，代理可能最终陷入死胡同，不再能够实现其目标。因此，代理应该在计划执行期间监控环境，如果他们遇到危险情况，他们应该（反应性地）采取行动逃离它。

在本文中，我们介绍了危险状态的概念代理在动态环境中执行计划期间可能遇到的问题。我们提出了一种在应用一系列动作后计算状态危险性下限的方法。该方法用于识别代理必须开始采取行动以避免危险的情况。我们提出了两种类型的此类行为——纯粹的反应性和主动性（消除危险源）。引入的危险状态规划概念在两个场景中实施和测试——一个简单的类似 RPG 的游戏，称为 Dark Dungeon，以及一个受 Perestroika 视频游戏启发的平台游戏。结果表明，与幼稚的计划或基于规则的技术相比，危险状态下的推理取得了更好的成功率（达到目标）。