Planning with Domain Rules Based on State-Independent Activation Sets

机译：基于国家独立的激活集规划域规则

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In AI planning community, planning domains with derived predicates are very challenging to many planning system. Derived predicate is a new application of domain rules and domain knowledge acquisition. In this paper, we propose an approach to planning with derived predicates: defining activation sets of a derived predicate which are unrelated to any specific state and computing them in the preprocess phase through the instantiation rule-graph; replacing a derived predicate with one of its activation sets in relax-plan to extract action sequences. And we also implement the proposed approach in a new planner, called FF-DP, which shows good performance in our experiments.

机译：在AI规划社区中，具有导出谓词的规划域对许多规划系统非常具有挑战性。派生谓词是域规则和域知识获取的新应用。在本文中，我们提出了一种规划与导出的谓词规划的方法：定义与任何特定状态无关的导出谓词的激活集，并通过实例范围图形计算它们的预处理阶段;用其在放松计划中的一个激活集中替换派生谓词以提取动作序列。我们还在一个名为FF-DP的新计划员中实施了拟议的方法，在我们的实验中表现出良好的表现。

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《Pacific Rim Knowledge Acquisition Workshop》|2006年||共8页
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Zhi-hua Jiang; Yun-fei Jiang;
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正文语种
中图分类人工智能理论;
关键词
deterministic planning; domain rules; activation sets;

机译：确定性规划;域规则;激活集;

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2. Rough set-based rule generation and Apriori-based rule generation from table data sets II: SQL-based environment for rule generation and decision support [J] . Hiroshi Sakai, Zhiwen Jian CAAI Transactions on Intelligence Technology . 2019,第4期

机译：从表数据集II：规则生成和决策支持的基于SQL的环境基于粗糙的规则生成和基于APRiori的规则生成
3. Rough set-based rule generation and Apriori-based rule generation from table data sets: a survey and a combination [J] . Hiroshi Sakai, Michinori Nakata CAAI Transactions on Intelligence Technology . 2019,第4期

机译：表数据集的基于粗糙集的规则生成和基于APRiori的规则生成：调查和组合
4. Planning with Domain Rules Based on State-Independent Activation Sets [C] . Zhi-hua Jiang, Yun-fei Jiang Advances in Knowledge Acquisition and Management; Lecture Notes in Artificial Intelligence; 4303 . 2006

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7. A Knob-Socket Based Rule Set for Designing Peptide Binding to PDZ Domains [O] . Shivarni Patel, Hyun Joo, Jerry Tsai 2018

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Planning with Domain Rules Based on State-Independent Activation Sets

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