Results 11 to 20 of about 72,882 (259)
Adaptive hierarchical learning for uncertainty-aware distributed energy resource planning. [PDF]
Commun EngThe proliferation of distributed energy resources introduces multi-source uncertainties, including implicit uncertainties arising from third-party operators’ partial observability of security constraints, challenging traditional distribution network ...
Xiang Y +8 more
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Leveraging Demonstrations for Learning the Structure and Parameters of Hierarchical Task Networks
Proceedings of the International Florida Artificial Intelligence Research Society Conference, 2023 Hierarchical Task Networks (HTNs) are a common formalism for automated planning, allowing to leverage the hierarchical structure of many activities. While HTNs have been used in many practical applications, building a complete and efficient HTN model ...
Philippe Hérail, Arthur Bit-Monnot
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Hierarchical Planning With Annotated Skeleton Guidance
IEEE Robotics and Automation Letters, 2022 We present a hierarchical skeleton-guided motion planning algorithm to guide mobile robots. A good skeleton maps the connectivity of the subspace of c-space containing significant degrees of freedom and is able to guide the planner to find the desired solutions fast.
Diane Uwacu +3 more
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Dynamic Causal Modelling of Hierarchical Planning
NeuroImage, 2022 Hierarchical planning (HP) is a strategy that optimizes the planning by storing the steps towards the goal (lower-level planning) into subgoals (higher-level planning).
Qunjun Liang +4 more
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Hierarchical Width-Based Planning and Learning
Proceedings of the International Conference on Automated Planning and Scheduling, 2021 Width-based search methods have demonstrated state-of-the-art performance in a wide range of testbeds, from classical planning problems to image-based simulators such as Atari games. These methods scale independently of the size of the state-space, but exponentially in the problem width.
Miquel Junyent +2 more
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Hierarchical Task Network Planning Based Attack Path Discovery [PDF]
Jisuanji kexue, 2023 Attack path discovery is a critical task for cyber asset security assessment.The existing artificial intelligence-based planning for attack path discovery method is favored by security practitioners due to its rich modeling language and complete planning
WANG Zibo, ZHANG Yaofang, CHEN Yilu, LIU Hongri, WANG Bailing, WANG Chonghua
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Hierarchical Planning in the IPC
CoRR, 2019 Over the last year, the amount of research in hierarchical planning has increased, leading to significant improvements in the performance of planners. However, the research is diverging and planners are somewhat hard to compare against each other. This is mostly caused by the fact that there is no standard set of benchmark domains, nor even a common ...
Daniel Höller +6 more
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Learning to Plan Hierarchically From Curriculum [PDF]
IEEE Robotics and Automation Letters, 2019 We present a framework for learning to plan hierarchically in domains with unknown dynamics. We enhance planning performance by exploiting problem structure in several ways: (i) We simplify the search over plans by leveraging knowledge of skill objectives, (ii) Shorter plans are generated by enforcing aggressively hierarchical planning, (iii) We learn ...
Philippe Morere +2 more
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Föreställningar om kunskap och lärande i lärares planeringssamtal i matematik och historia
Utbildning & Lärande, 2023 This article reports a study of teachers’ lesson planning in a collegial setting. The school employed scheduled time every week for team planning in different school subjects. In this article, 44 planning sessions in Mathematics and History are analysed
Ann-Christin Randahl +3 more
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Analytical Evaluation of Hierarchical Planning Systems [PDF]
Operations Research, 1981 Hierarchical planning systems have become popular for multilevel decision problems. After reviewing the concept of hierarchical planning and citing some examples, we describe a method for analytic evaluation of a hierarchical planning system. We show that multilevel decision problems can be nicely modeled as multistage stochastic programs.
Michael A. H. Dempster +5 more
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