Call for Papers


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

Paper Format and Submission

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

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 submission.

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

Papers should be submitted via EasyChair at

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