Overview
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
are:
We believe a deeper integration of machine learning approaches and planning algorithms presents an exciting and novel direction for formulating and solving generalized planning. |
Announcements
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    19th International Conference on     Automated Planning and Scheduling |