{"title":"树的分支跟踪——解决部分有序HTN规划问题的命题编码","authors":"G. Behnke, D. Höller, Susanne Biundo-Stephan","doi":"10.1109/ICTAI.2018.00022","DOIUrl":null,"url":null,"abstract":"Planning via SAT has proven to be an efficient and versatile planning technique. Its declarative nature allows for an easy integration of additional constraints and can harness the progress made in the SAT community without the need to adapt the planner. However, there has been only little attention to SAT planning for hierarchical domains. To ease encoding, existing approaches for HTN planning require additional assumptions, like non-recursiveness or totally-ordered methods. Both limit the expressiveness of HTN planning severely. We propose the first propositional encodings which are able to solve general, i.e., partially-ordered, HTN planning problems, based on a previous encoding for totally-ordered problems. The empirical evaluation of our encoding shows that it outperforms existing HTN planners significantly.","PeriodicalId":254686,"journal":{"name":"2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"21","resultStr":"{\"title\":\"Tracking Branches in Trees - A Propositional Encoding for Solving Partially-Ordered HTN Planning Problems\",\"authors\":\"G. Behnke, D. Höller, Susanne Biundo-Stephan\",\"doi\":\"10.1109/ICTAI.2018.00022\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Planning via SAT has proven to be an efficient and versatile planning technique. Its declarative nature allows for an easy integration of additional constraints and can harness the progress made in the SAT community without the need to adapt the planner. However, there has been only little attention to SAT planning for hierarchical domains. To ease encoding, existing approaches for HTN planning require additional assumptions, like non-recursiveness or totally-ordered methods. Both limit the expressiveness of HTN planning severely. We propose the first propositional encodings which are able to solve general, i.e., partially-ordered, HTN planning problems, based on a previous encoding for totally-ordered problems. The empirical evaluation of our encoding shows that it outperforms existing HTN planners significantly.\",\"PeriodicalId\":254686,\"journal\":{\"name\":\"2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI)\",\"volume\":\"33 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"21\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICTAI.2018.00022\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICTAI.2018.00022","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Tracking Branches in Trees - A Propositional Encoding for Solving Partially-Ordered HTN Planning Problems
Planning via SAT has proven to be an efficient and versatile planning technique. Its declarative nature allows for an easy integration of additional constraints and can harness the progress made in the SAT community without the need to adapt the planner. However, there has been only little attention to SAT planning for hierarchical domains. To ease encoding, existing approaches for HTN planning require additional assumptions, like non-recursiveness or totally-ordered methods. Both limit the expressiveness of HTN planning severely. We propose the first propositional encodings which are able to solve general, i.e., partially-ordered, HTN planning problems, based on a previous encoding for totally-ordered problems. The empirical evaluation of our encoding shows that it outperforms existing HTN planners significantly.