Results 11 to 20 of about 73,109 (257)

Quality-Oriented Hybrid Path Planning Based on A* and Q-Learning for Unmanned Aerial Vehicle

open access: yesIEEE Access, 2022
Unmanned aerial vehicles (UAVs) are playing an increasingly important role in people’s daily lives due to their low cost of operation, low requirements for ground support, high maneuverability, high environmental adaptability, and high safety. Yet
Dongcheng Li   +4 more
doaj   +1 more source

The Application of “Big Data Analysis + Hierarchical Medical” Model in the Context of the COVID-19 [PDF]

open access: yesE3S Web of Conferences, 2021
The COVID-19 epidemic has swept the world, causing serious impact and influence on economic development and residents' life in countries all over the world.
Yang Xingyu   +5 more
doaj   +1 more source

A Hierarchical Goal-Biased Curriculum for Training Reinforcement Learning

open access: yesProceedings of the International Florida Artificial Intelligence Research Society Conference, 2022
Hierarchy and curricula are two techniques commonly used to improve training for Reinforcement Learning (RL) agents. Yet few works have examined how to leverage hierarchical planning to generate a curriculum for training RL Options.
Sunandita Patra   +6 more
doaj   +1 more source

Leveraging Demonstrations for Learning the Structure and Parameters of Hierarchical Task Networks

open access: yesProceedings 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
doaj   +1 more source

Dynamic Causal Modelling of Hierarchical Planning

open access: yesNeuroImage, 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
doaj   +1 more source

Hierarchical Planning With Annotated Skeleton Guidance

open access: yesIEEE 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
openaire   +2 more sources

Hierarchical Task Network Planning Based Attack Path Discovery [PDF]

open access: yesJisuanji 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
doaj   +1 more source

Hierarchical Width-Based Planning and Learning

open access: yesProceedings 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
openaire   +4 more sources

Hierarchical Planning in the IPC

open access: yesCoRR, 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
openaire   +2 more sources

Learning to Plan Hierarchically From Curriculum [PDF]

open access: yesIEEE 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
openaire   +2 more sources

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