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Special Track on Planning and Learning @ ICAPS 2020
https://icaps20.icaps-conference.org/calls/special-track-on-learning-and-planning/
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Call For Papers:
The success of deep learning methods has enabled significant progress in
some areas of AI, but has been mostly limited to reactive systems that
require large amounts of annotated data. There is an increasing interest
in planning from the machine learning community. However, an important
gap exists between both communities in terms of objectives, techniques,
or methodologies. For example, learning-based systems tend to scale
poorly on problems of combinatorial nature, in which planning-based
systems excel, and similarly, planning-based systems have very limited
generalization capabilities, which is a fundamental quality of
learning-based systems. The Planning and Learning track aims to bridge
this gap by providing an opportunity for both communities to engage
directly. In particular, we are interested in how the progress we have
seen from the deep learning side can be integrated into the progress we
have seen in planning.
This track has already become an important part of the main ICAPS
conference and in this fourth edition we welcome both theoretical and
empirical work addressing (but not limited to) the following topics:
* Planning and efficient exploration in reinforcement learning
* Planning and representation learning
* Planning and relational learning
* Learning to improve the effectiveness of planning & scheduling systems
* Planning & scheduling in learned domain models
* Learning effective heuristics and other forms of control knowledge
* Planning applied to automating machine learning systems
* Learning of cost functions used in planning
Important Dates:
November 15, 2019 Abstracts due (electronic submission)
November 20, 2019 Papers due (electronic submission, PDF)
January 6-8, 2020 Author feedback period
January 20, 2020 Author notification of acceptance or rejection
Submission details:
https://icaps20.icaps-conference.org/calls/call-for-papers/#author_guidelines
Best,
Vicenç Gómez, Gerhard Neumann, and Hankz Hankui Zhuo (track chairs)
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