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- Probabilistic Robust Multi-Agent Path Finding
In this paper, we therefore study the problem of finding a p-robust solution to a given MAPF problem, which is a solution that succeeds with probability at least p, even though unexpected delays may occur We propose two meth-ods for verifying that given solutions are p-robust
- Continuum damage mechanics based probabilistic fatigue life prediction . . .
In this paper, a new two-scale probabilistic life prediction model based on CDM is proposed to predict probabilistic fatigue life of complex metallic structure
- A Strategic Search Algorithm in Multi-agent and Multiple Target . . .
To solve this multi-agent pathfinding problem for multiple pursuing agents and targets, a new search algorithm is introduced that has an assignment strategy and generates a path, if it exists
- Metallic Metal–Organic Frameworks Predicted by the Combination of . . .
Data-driven probabilistic machine learning in sustainable smart energy smart energy systems: Key developments, challenges, and future research opportunities in the context of smart grid paradigm
- Reinforcement Learning for Zone Based Multiagent Pathfinding under . . .
ICAPS 2020 - The 30th International Conference on Automated Planning and Scheduling
- Multi-Agent Path Finding Among Dynamic Uncontrollable Agents with . . .
We present a novel variant of Enhanced Conflict-Based Search (ECBS), for both one-shot and lifelong MAPF in dynamic en-vironments with uncontrollable agents
- Heterogeneous Multi-Robot Path Planning Based on Probabilistic Motion . . .
Every robot has its own probability distribution of motion time between any two neighbor locations We develop a conflict-detection scheme for this model and propose using the conflict-based search algorithm via probability calculation to find the optimal path that minimizes the entire motion time
- Fatigue life prediction of metallic materials considering mean stress . . .
The purpose of this research work is to develop a new methodology to generate a constant life diagram (CLD) for metallic materials, based on assumptions of Haigh diagram and artificial neural networks, using the probabilistic Stüssi fatigue S-N fields
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