- March 22, 2017 - Submissions Due
- April 20, 2017 - Notification
- June 1, 2017 - Camera-ready Due
- June 19, 2017 - Workshop Date
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 plan.
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
- 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 algorithms?
- What are the computational limits to the feasibility of these problems?
- Can restricted formulations of generalized planning that are practical and efficiently solvable be developedxs?
- How can abstraction techniques for understanding, analyzing and reasoning about programs be utilized for generalized planning?
- 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
Topics of interest include but not limited to the following:
- Camera ready papers due by June 1, 2017.
- List of accepted papers available online.
- Papers should be submitted via EasyChair.