Seth Rivard, Charles Town's city planner, said the panel has gathered citizen input and assessed the needs and opportunities for land use and development, parks and recreation facilities, downtown revitalization, as well as roads and "mobility" transportation infrastructure for the city and, when it affected the city, the surrounding county. He said the committee is preparing to release a written report to the Planning Commission and the public sometime in July and August. Public hearings will be held to obtain residents' views about the ideas and suggestions in the report.
We describe HTN-MAKER, an algorithm for learning hierarchical planning knowledge in the form of decomposition methods for Hierarchical Task Networks (HTNs). HTN-MAKER takes as input the initial states from a set of classical planning problems in a planning domain and solutions to those problems, as well as a set of semantically-annotated tasks to be accomplished. The algorithm analyzes this semantic information in order to determine which portions of the input plans accomplish a particular task and constructs HTN methods based on those analyses. Our theoretical results show that HTN-MAKER is sound and complete. We also present a formalism for a class of planning problems that are more expressive than classical planning. These planning problems can be represented as HTN planning problems. We show that the methods learned by HTN-MAKER enable an HTN planner to solve those problems. Our experiments confirm the theoretical results and demonstrate convergence in three well-known planning domains toward a set of HTN methods that can be used to solve nearly any problem expressible as a classical planning problem in that domain, relative to a set of goals.
Casting planning as propositional satisfiability has been recently shown to be a very promising technique of plan synthesis. Some challenges, one of which is the development of hybrid propositional encodings (that combine the important notions from the existing encodings) have also been posed to the community . The existing encodings  are either entirely based only on the plan space planning (also known as "causal" or "least commitment" or "partial order" planning) or only on the state space planning. To answer this challenge, we have developed several hybrid encodings. A key difference between state space planning and plan space planning is that state of the world is represented at each time step during the state space planning process and it is never available during the partial order planning process.
With the advent of compositional programming models in computer science, applying planning technologies to automatically build workflows for solving large and complex problems in such a paradigm becomes not only technically appealing but also feasible approach. The application areas that will benefit from automatic composition include, among others, Web services, Grid computing and stream processing systems. Although the classical planning formalism is expressive enough to describe planning problems that arise in a large variety of different applications, it can pose significant limitations on planner performance in compositional applications, in particular, in stream processing systems. In this paper we extend the classical planning formalism by introducing new language constructs that support the structure of stream processing domains. Exposing this structure to the planner can result in dramatic performance improvements: our experiments show exponential planning time reduction in comparison to most recent metric planners.