[PlanetKR] CFP: ICAPS 2017 Workshop on Generalized Planning

Vaishak Belle vaishak at cs.toronto.edu
Mon Feb 27 23:15:45 EST 2017

Preliminary Call for Papers
*ICAPS 2017 Workshop on Generalized Planning
June 19th or 20th 2017, Pittsburgh, USA

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 plans.

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) and *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 surveys and cites a body of work.

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

Detailed instructions for submission will be available soon.

*Important Dates*

Paper Submission Deadline  March 15, 2017
Author Notification                 April 10, 2017
Workshop Date                     June 19|20, 2017

*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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