Domain Concretization From Examples: Addressing Missing Domain Knowledge Via Robust Planning
Domain Concretization From Examples: Addressing Missing Domain Knowledge Via Robust Planning
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DOI：
10.1109/lra.2021.3137549
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发表时间：
2020-11
影响因子：
5.2
通讯作者：
Akshay Sharma;Piyush Rajesh Medikeri;Yu Zhang
中科院分区：
文献类型：
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作者：
Akshay Sharma;Piyush Rajesh Medikeri;Yu Zhang
对于真实的世界中的机器人规划和决策来说，完全领域知识的假设是没有根据的。领域知识的不完整性可能来自于设计缺陷，也可能来自于领域分支或资格。在这种情况下，传统的规划方法可能会产生非常不受欢迎的行为。在不完整的领域知识下解决规划问题是具有挑战性的，因为代理人不知道缺少什么信息。这是一种未知的未知数，它与部分可观测性（一种已知的未知数）有很大的不同。在这项工作中，我们假设缺失的信息被编码在一组示例或教师演示中。我们制定这些例子作为一个反问题域抽象的域具体化的问题。给定最初提供的领域模型，当模型不符合教师演示时，我们的方法会搜索候选模型集，该模型集在极简主义和确定性模型假设下细化初始模型。对于新问题，它生成一个鲁棒的计划，在候选模型集下具有最大的成功概率。连同一个标准的搜索公式在模型空间中，我们提出了一个基于知识的搜索方法，也是它的在线版本，以减少搜索时间。我们评估了我们的方法与几个国际规划竞赛（IPC）域和一个模拟的机器人领域的不完整性，从完整的模型中删除域功能。结果表明，我们的方法提高了规划的成功率，而不会显着影响计划成本。
The assumption of complete domain knowledge is unwarranted for robot planning and decision-making in the real world. Incompleteness in domain knowledge may come from design flaws or arise from domain ramifications or qualifications. In such cases, traditional planning methods can produce highly undesirable behaviors. Addressing the planning problem under incomplete domain knowledge is challenging since the agent has no clue about what information is missing. This is a type of unknown unknowns, which differs significantly from partial observability, a type of known unknowns. In this work, we assume that the missing information is encoded in a set of examples or teacher demonstrations. We formulate the problem of domain concretization with these examples as an inverse problem to domain abstraction. Given a domain model provided initially, when the model does not conform with the teacher demonstrations, our method searches for a candidate model set that refines the initial model under a minimalistic and deterministic model assumption. For new problems, it generates a robust plan with the maximum probability of success under the set of candidate models. Together with a standard search formulation in the model-space, we propose a heuristic-based search method and also an online version of it to reduce the search time. We evaluated our approach with several International Planning Competition (IPC) domains and a simulated robotics domain where incompleteness was introduced by removing domain features from the complete models. Results show that our methods increase the success rate of planning without significantly impacting the plan cost.