ARMS: an automatic knowledge engineering tool for learning action models for AI planning
ARMS: an automatic knowledge engineering tool for learning action models for AI planning
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DOI：
10.1017/s0269888907001087
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发表时间：
2007-06
期刊：
影响因子：
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通讯作者：
Kangheng Wu;Qiang Yang;Yunfei Jiang
中科院分区：
文献类型：
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作者：
Kangheng Wu;Qiang Yang;Yunfei Jiang
摘要我们提出了一个动作模型学习系统ARMS(Action-Relationship Modeling System)，用于从一组成功观察到的计划中自动发现动作模型。目前的人工智能(AI)规划者在许多现实世界和人工领域表现出令人印象深刻的表现，但它们都需要定义动作模型。ARMS旨在从观察到的示例计划中自动学习行动模型，其中每个示例计划都是一系列行动轨迹。然后，人类编辑可以使用这些动作模型来改进。人们的期望是，这个系统将减轻人类编辑从头开始设计动作模型的负担。在本文中，我们对ARM进行了详细的描述。为了学习行动模型，ARM收集关于示例计划中频繁行动集的统计分布的知识。然后，它建立一个加权命题可满足性(加权SAT)问题，并使用加权MAXSAT求解器求解该问题。此外，我们的经验证据表明，ARM确实可以有效地学习最终行动模型的良好近似。
Abstract We present an action model learning system known as ARMS (Action-Relation Modelling System) for automatically discovering action models from a set of successfully observed plans. Current artificial intelligence (AI) planners show impressive performance in many real world and artificial domains, but they all require the definition of an action model. ARMS is aimed at automatically learning action models from observed example plans, where each example plan is a sequence of action traces. These action models can then be used by the human editors to refine. The expectation is that this system will lessen the burden of the human editors in designing action models from scratch. In this paper, we describe the ARMS in detail. To learn action models, ARMS gathers knowledge on the statistical distribution of frequent sets of actions in the example plans. It then builds a weighted propositional satisfiability (weighted SAT) problem and solves it using a weighted MAXSAT solver. Furthermore, we show empirical evidence that ARMS can indeed learn a good approximation of the finally action models effectively.