Town Planning Commission Member Lives Without Car

U.S. News

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.

HTN-MAKER: Learning HTNs with Minimal Additional Knowledge Engineering Required

AAAI Conferences

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.

Imperfect Match: PDDL 2.1 and Real Applications Artificial Intelligence

PDDL was originally conceived and constructed as a lingua franca for the International Planning Competition. PDDL2.1 embodies a set of extensions intended to support the expression of something closer to real planning problems. This objective has only been partially achieved, due in large part to a deliberate focus on not moving too far from classical planning models and solution methods.

SHOP2: An HTN Planning System Artificial Intelligence

The SHOP2 planning system received one of the awards for distinguished performance in the 2002 International Planning Competition. This paper describes the features of SHOP2 which enabled it to excel in the competition, especially those aspects of SHOP2 that deal with temporal and metric planning domains.