[PlanetKR] CFP: ICAPS 2017 Workshop on Generalized Planning [extended deadline]

Vaishak Belle vaishak at cs.toronto.edu
Wed Mar 15 18:34:19 EST 2017

Call for Papers
* ICAPS 2017 Workshop on Generalized Planning*



Automated planning is a fundamental area of AI, concerned with computing
behaviors which when executed in an initial state realize the goals and
objectives of the agent. In the last 15 years, we have seen great advances
in the efficiency of automated planning techniques, as a consequence of a
variety of innovations, including advances in heuristic search for
classical planning, and the application of classical planning to
non-classical planning tasks. Nevertheless, industrial-level scalability
remains a fundamental challenge to the broad applicability of AI automated
planning techniques. This is especially notable when the space of objects
is (possibly) infinite or when there is inherent uncertainty about the
initial plan parameters.

This workshop aims to bring together researchers working on emerging
directions for addressing this challenge, including: (1) achieving
scalability through plans that include cyclic flow of control and solve
large classes of problems, (2) acquisition (through learning or search) of
domain control knowledge for reducing the cost of planning, or otherwise
structuring the space of solutions, (3) automated composition of
pre-existing control modules like software services, and (4) synthesis of
program-like structures from partial programs or goal-specifications.
Common to all of these approaches is the notion of generalized plans, or
plans that include rich control structures that resemble programs. In
addition, all of these approaches share the fundamental problem of
evaluating whether a given control structure will be helpful in developing
a scalable solution for a given class of problem instances. While these
approaches have achieved promising results, many fundamental challenges
remain regarding the synthesis, analysis and composition of such generalized

The focus of this workshop is on techniques for addressing these challenges
in particular, and more generally on scalable representation and reasoning
techniques for planning. An additional objective is to reevaluate some of
the most fundamental, traditionally accepted notions in planning about
plan structure
and representation of domain knowledge. Some of the questions motivating
this workshop are:

   - How can we effectively find, represent and utilize high-level
   knowledge about planning domains?
   - What separates planning problems from program synthesis??
   - How can we effectively embed complex control structures in planning
   - What are the computational limits to the feasibility of these
   - Can restricted formulations of generalized planning that are practical
   and efficiently solvable be developed??
   - How can abstraction techniques for understanding, analyzing and
   reasoning about programs be utilized for generalized planning??

In addition to these key questions, we would like to additionally emphasize
and encourage submissions on the following theme:

   - How can we learn generalized plans and partial policies from data?

We believe a deeper integration of machine learning approaches and
planning algorithms
presents an exciting and novel direction for formulating and solving
generalized planning.


Topics of interest to this workshop bring together research being conducted
in a range of areas, including classical planning, knowledge engineering,
partial policies and hierarchical reinforcement learning, plan verification,
and model checking. Potential topics include but are not limited to:

   - generating plans with loops
   - generating parametrized plans
   - program synthesis
   - instantiating parametrized plans
   - learning macro actions
   - learning domain control knowledge
   - planning with domain control knowledge (e.g., Golog, HTNs, control
   - planning with partial policies
   - generating robust or partial schedules
   - work-flows as plans

*Paper Format and Submission*

We invite technical papers (up to 8 pages), position papers (up to 2

We invite submissions describing either work in progress or mature work
that has already been published at other research venues and would be of
interest to researchers working on generalized planning. Submission of
previously published work in whole or in part may be in the form of a
resubmission of a previous paper, or in the form of a position paper that
overviews and cites a body of work.

Submissions of papers being reviewed at other venues are welcome since this
is a non archival venue and we will not require a transfer of copyright. If
such papers are currently under blind review, *please anonymize the

All papers should be typeset in the AAAI style, described at

Detailed instructions for submission will be available soon at the workshop
website <http://homepages.inf.ed.ac.uk/vbelle/workshops/genplan17/>.

*Important Dates*

Paper Submission Deadline  March 22, 2017 (extended from March 15)
Author Notification                 April 10, 2017
Workshop Date                     June 19|20, 2017

*Program Committee*

Jorge Baier, Pontificia Universidad Catolica de Chile, Chile
Blai Bonet, Universidad Simon Bolivar, Venezuela
Alberto Camacho, University of Toronto, Canada
Giuseppe De Giacomo, Sapienza Universita' di Roma, Italy
Hector Geffner, Universitat Pompeu Fabra, Spain
Leon Illanes, University of Toronto, Canada
Anders Jonsson, Universitat Pompeu Fabra, Spain
Roni Khardon, Tufts University, USA
Kristian Kersting, Technical University of Dortmund, Germany
Ugur Kuter, SIFT, USA
Gerhard Lakemeyer, Aachen University of Technology, Germany
Christian Muise, MIT, USA
Karen Myers, SRI, USA
Miguel Ramirez, Universitat Pompeu Fabra, Spain
Patricia Riddle, University of Auckland, New Zealand
Scott Sanner, University of Toronto, Canada
Sebastian Sardina, RMIT, Australia
Shirin Sohrabi, IBM, USA
Paolo Traverso, FBK ICT IRST, Italy
Shlomo Zilberstein, University of Massachusetts, USA

*Organizing Committee*

Vaishak Belle, University of Edinburgh, United Kingdom
Sheila McIlraith, University of Toronto, Canada
Ron Petrick, Heriot-Watt University, United Kingdom
Siddharth Srivastava, United Technologies Research Center, USA

Workshop related queries can be addressed to a common alias: genplan17 \at\
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