Real-Time Action Model Learning with Online Algorithm 3SG
Real-Time Action Model Learning with Online Algorithm 3SG
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DOI：
10.1080/08839514.2014.927692
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发表时间：
2014-08
影响因子：
2.8
通讯作者：
Michal Čertický
中科院分区：
文献类型：
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作者：
Michal Čertický
行动模型作为行动效果和前提条件的逻辑表示，是规划和智能行为的基本要求。手工编写这些模型，特别是在复杂的领域中，通常是一项耗时且容易出错的任务。另一种方法是让代理从自己的观察中学习动作模型。我们介绍了一种新的动作学习算法，称为3SG（同时规范，简化和泛化），分析和证明它的一些属性，并提出了第一个实验结果（使用真实世界的机器人的SyRoTek平台和模拟代理人在行动计算机游戏虚幻锦标赛2004）。与大多数可用的替代方案不同，3SG产生具有条件效应的概率动作模型，并处理动作失败，感觉噪声和不完整的观测。然而，主要的区别在于3SG是一种在线算法，这意味着它相当快（输入大小的多项式），但可能不太精确。
An action model, as a logic-based representation of action’s effects and preconditions, constitutes an essential requirement for planning and intelligent behavior. Writing these models by hand, especially in complex domains, is often a time-consuming and error-prone task. An alternative approach is to let the agents learn action models from their own observations. We introduce a novel action learning algorithm called 3SG (Simultaneous Specification, Simplification, and Generalization), analyze and prove some of its properties, and present the first experimental results (using real-world robots of the SyRoTek platform and simulated agents in action computer game Unreal Tournament 2004). Unlike the majority of available alternatives, 3SG produces probabilistic action models with conditional effects and deals with action failures, sensoric noise, and incomplete observations. The main difference, however, is that 3SG is an online algorithm, which means it is rather fast (polynomial in the size of the input) but potentially less precise.