领域实例化：通过鲁棒规划解决缺失的领域知识
Domain Concretization from Examples: Addressing Missing Domain Knowledge via Robust Planning
摘要：
对于现实世界中的机器人规划和决策，完全领域知识的假设是不必要的。它可能是由于设计缺陷或从域分支或资格产生的。在这种情况下，现有的规划和学习算法可能会产生非常不希望的行为。这个问题比部分可观测性更具挑战性，因为智能体不知道某些知识，而不是部分可观测：已知的未知数和未知的未知数之间的差异。在这项工作中，我们制定它作为域具体化的问题，域抽象的逆问题。基于设计者提供的不完整的领域模型和来自人类用户的教师痕迹，我们的算法在最小模型假设下搜索候选模型集。然后，它生成一个强大的计划，在候选模型集下具有最大的成功概率。除了在模型空间中的标准搜索公式，我们提出了一个基于样本的搜索方法，也是它的在线版本，以提高搜索时间。我们测试了我们的方法IPC域和模拟机器人领域的不完整性，从完整的模型中删除域功能。实验结果表明，该规划算法在不影响成本的情况下，提高了规划成功率。
英文摘要：
The assumption of complete domain knowledge is not warranted for robot planning and decision-making in the real world. It could be due to design flaws or arise from domain ramifications or qualifications. In such cases, existing planning and learning algorithms could produce highly undesirable behaviors. This problem is more challenging than partial observability in the sense that the agent is unaware of certain knowledge, in contrast to it being partially observable: the difference between known unknowns and unknown unknowns. In this work, we formulate it as the problem of Domain Concretization, an inverse problem to domain abstraction. Based on an incomplete domain model provided by the designer and teacher traces from human users, our algorithm searches for a candidate model set under a minimalistic model assumption. It then generates a robust plan with the maximum probability of success under the set of candidate models. In addition to a standard search formulation in the model-space, we propose a sample-based search method and also an online version of it to improve search time. We tested our approach on IPC domains and a simulated robotics domain where incompleteness was introduced by removing domain features from the complete model. Results show that our planning algorithm increases the plan success rate without impacting the cost much.
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