کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
4942133 | 1436986 | 2017 | 21 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Model-lite planning: Case-based vs. model-based approaches
ترجمه فارسی عنوان
برنامه ریزی مدل: مبتنی بر مورد مبتنی بر مدل مبتنی بر رویکرد
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موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
چکیده انگلیسی
There is increasing awareness in the planning community that depending on complete models impedes the applicability of planning technology in many real world domains where the burden of specifying complete domain models is too high. In this paper, we consider the problem of generating robust and accurate plans, when the agent only has access to incomplete domain models, supplanted by a set of successful plan cases. We will develop two classes of approaches - one case-based and the other model-based. ML-CBP is a case-based approach that leverages the incomplete model and the plan cases to solve a new problem directly by affecting case-level transfer. RIM is a model-based approach that uses the incomplete model and the plan cases to first learn a more complete model. This model contains both primitive actions as well as macro-operators that are derived from the plan cases. The learned model is then used in conjunction with an off-the-shelf planner to solve new problems. We present a comprehensive evaluation of the two approaches, both to characterize their relative tradeoffs, and to quantify their advances over existing approaches.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Artificial Intelligence - Volume 246, May 2017, Pages 1-21
Journal: Artificial Intelligence - Volume 246, May 2017, Pages 1-21
نویسندگان
Hankz Hankui Zhuo, Subbarao Kambhampati,