18,611 research outputs found
Towards learning domain-independent planning heuristics
Automated planning remains one of the most general paradigms in Artificial
Intelligence, providing means of solving problems coming from a wide variety of
domains. One of the key factors restricting the applicability of planning is
its computational complexity resulting from exponentially large search spaces.
Heuristic approaches are necessary to solve all but the simplest problems. In
this work, we explore the possibility of obtaining domain-independent heuristic
functions using machine learning. This is a part of a wider research program
whose objective is to improve practical applicability of planning in systems
for which the planning domains evolve at run time. The challenge is therefore
the learning of (corrections of) domain-independent heuristics that can be
reused across different planning domains.Comment: Accepted for the IJCAI-17 Workshop on Architectures for Generality
and Autonom
Interpretable deep learning for guided structure-property explorations in photovoltaics
The performance of an organic photovoltaic device is intricately connected to
its active layer morphology. This connection between the active layer and
device performance is very expensive to evaluate, either experimentally or
computationally. Hence, designing morphologies to achieve higher performances
is non-trivial and often intractable. To solve this, we first introduce a deep
convolutional neural network (CNN) architecture that can serve as a fast and
robust surrogate for the complex structure-property map. Several tests were
performed to gain trust in this trained model. Then, we utilize this fast
framework to perform robust microstructural design to enhance device
performance.Comment: Workshop on Machine Learning for Molecules and Materials (MLMM),
Neural Information Processing Systems (NeurIPS) 2018, Montreal, Canad
The State-of-the-art of Coordinated Ramp Control with Mixed Traffic Conditions
Ramp metering, a traditional traffic control strategy for conventional
vehicles, has been widely deployed around the world since the 1960s. On the
other hand, the last decade has witnessed significant advances in connected and
automated vehicle (CAV) technology and its great potential for improving
safety, mobility and environmental sustainability. Therefore, a large amount of
research has been conducted on cooperative ramp merging for CAVs only. However,
it is expected that the phase of mixed traffic, namely the coexistence of both
human-driven vehicles and CAVs, would last for a long time. Since there is
little research on the system-wide ramp control with mixed traffic conditions,
the paper aims to close this gap by proposing an innovative system architecture
and reviewing the state-of-the-art studies on the key components of the
proposed system. These components include traffic state estimation, ramp
metering, driving behavior modeling, and coordination of CAVs. All reviewed
literature plot an extensive landscape for the proposed system-wide coordinated
ramp control with mixed traffic conditions.Comment: 8 pages, 1 figure, IEEE INTELLIGENT TRANSPORTATION SYSTEMS CONFERENCE
- ITSC 201
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