ICAPS 2015 - Workshop on Planning and Learning (PAL), ICAPS 2015
Topics/Call fo Papers
Learning and planning are two distinct capabilities required for an intelligent agent, but they are connected in many ways and it is often beneficial to consider them together.
Learning allows planning to be more easily applied to new domains by serving as a means for automatically acquiring knowledge required in planning. In traditional planning methods, knowledge about the models and the search guidance are often manually specified, and can be very difficult or time-consuming to obtain. Many recent works have addressed the knowledge acquisition bottleneck by learning models for planning and learning heuristics to guide planning.
The techniques in planning can be used for solving learning problems. For example, Bayesian reinforcement learning for MDPs can be formulated as a Partially Obserable Markov Decision Process (POMDP), and thus can be solved using a POMDP solution algorithm. The problem of active learning can be formulated as a planning problem too.
Some existing learning or planning algorithms, when viewed from the right perspective, also show interesting connections between learning and planning. For example, adaptive Monte Carlo planning can be seen as online learning of search guidance.
This workshop aims to provide a stimulating forum for researchers from both the learning community and the planning community to discuss recent advances, and potential developments on these exciting topics at the intersection of learning and planning. This workshop is the continuation of the lineage of four workshops on planning and learning in 2007, 2009, 2011, 2013.
Submissions are invited for topics on, but not limited to:
- Multi-agent planning and learning
- Robust planning in uncertain (learned) models
- Adaptive Monte Carlo planning
- Learning search heuristics for planner guidance
- Model-based reinforcement learning
- Bayesian reinforcement learning
- Model representations for learning and planning
- Theoretical aspects of planning and learning
- Learning and planning competition
- Applications of planning and learning
Important Dates:
- Submission Deadline: February 20, 2015.
- Notifications and Technical Program: March 20, 2015.
- Workshop Date: June 7th or 8th, 2015
- Submission Procedure
Paper should be submitted via the workshop EasyChair web site:
https://easychair.org/conferences/?conf=icapswpal2...
Paper submission is in PDF only. Please format submissions in AAAI style. Refer to the author instructions on the AAAI web site for detailed formatting instructions and LaTeX style files (http://www.aaai.org/Publications/Author/author.php). Final papers will be in the same format, keep them to at most 8+1 pages long (meaning 8 pages plus 1 extra page containing only references). Please note that all submitted papers will be peer-reviewed, and that low-quality or off-topic papers will not be accepted. Also note that all workshop participants must register for the main ICAPS 2015 conference and that at least one author of each accepted paper must attend the workshop.
Organizing Committee:
Alan Fern
School of Electrical Engineering and Computer Science
Oregon State University
Corvallis OR 97331 USA
Hanna Kurniawati
School of Information Technology and Electrical Engineering
The University of Queensland (St. Lucia Campus)
Brisbane, QLD, 4072, Australia
Scott Sanner
NICTA & ANU
Canberra, Australia
Nan Ye
Department of Computer Science
National University of Singapore
Singapore 117417
Learning allows planning to be more easily applied to new domains by serving as a means for automatically acquiring knowledge required in planning. In traditional planning methods, knowledge about the models and the search guidance are often manually specified, and can be very difficult or time-consuming to obtain. Many recent works have addressed the knowledge acquisition bottleneck by learning models for planning and learning heuristics to guide planning.
The techniques in planning can be used for solving learning problems. For example, Bayesian reinforcement learning for MDPs can be formulated as a Partially Obserable Markov Decision Process (POMDP), and thus can be solved using a POMDP solution algorithm. The problem of active learning can be formulated as a planning problem too.
Some existing learning or planning algorithms, when viewed from the right perspective, also show interesting connections between learning and planning. For example, adaptive Monte Carlo planning can be seen as online learning of search guidance.
This workshop aims to provide a stimulating forum for researchers from both the learning community and the planning community to discuss recent advances, and potential developments on these exciting topics at the intersection of learning and planning. This workshop is the continuation of the lineage of four workshops on planning and learning in 2007, 2009, 2011, 2013.
Submissions are invited for topics on, but not limited to:
- Multi-agent planning and learning
- Robust planning in uncertain (learned) models
- Adaptive Monte Carlo planning
- Learning search heuristics for planner guidance
- Model-based reinforcement learning
- Bayesian reinforcement learning
- Model representations for learning and planning
- Theoretical aspects of planning and learning
- Learning and planning competition
- Applications of planning and learning
Important Dates:
- Submission Deadline: February 20, 2015.
- Notifications and Technical Program: March 20, 2015.
- Workshop Date: June 7th or 8th, 2015
- Submission Procedure
Paper should be submitted via the workshop EasyChair web site:
https://easychair.org/conferences/?conf=icapswpal2...
Paper submission is in PDF only. Please format submissions in AAAI style. Refer to the author instructions on the AAAI web site for detailed formatting instructions and LaTeX style files (http://www.aaai.org/Publications/Author/author.php). Final papers will be in the same format, keep them to at most 8+1 pages long (meaning 8 pages plus 1 extra page containing only references). Please note that all submitted papers will be peer-reviewed, and that low-quality or off-topic papers will not be accepted. Also note that all workshop participants must register for the main ICAPS 2015 conference and that at least one author of each accepted paper must attend the workshop.
Organizing Committee:
Alan Fern
School of Electrical Engineering and Computer Science
Oregon State University
Corvallis OR 97331 USA
Hanna Kurniawati
School of Information Technology and Electrical Engineering
The University of Queensland (St. Lucia Campus)
Brisbane, QLD, 4072, Australia
Scott Sanner
NICTA & ANU
Canberra, Australia
Nan Ye
Department of Computer Science
National University of Singapore
Singapore 117417
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Last modified: 2015-01-31 17:51:01