As planning is applied to larger and richer domains the e ort involved in constructing domain descriptions increases and becomes a signi cant burden on the human application designer. If general planners are to be applied successfully to large and complex domains it is necessary to provide the domain designer with some assistance in building correctly encoded domains. One way of doing this is t...

The International Planning Competition is organized in the context of the International Conference on Automated Planning and Scheduling (ICAPS) and it is considered a reference source for the planning and scheduling community. The competition is typically organized every two years and deals with relevant issues for the community such as the definition of evaluation standards, the publication of...

Planning for multi-agent systems such as task assignment for teams of limited-fuel unmanned aerial vehicles (UAVs) is challenging due to uncertainties in the assumed models and the very large size of the planning space. Researchers have developed fast cooperative planners based on simple models (e.g., linear and deterministic dynamics), yet inaccuracies in assumed models will impact the resulti...

In most of the developing countries, the metropolitan organizations do not assess the safety consequences of alternative transportation systems and one of the reasons is the lack of suitable methodology. The goal of this paper is to develop practical tools for assessing safety consequences of arterial roads in the context of long-term urban transportation plans in Dhaka city, the capital of Ban...

A framework is proposed for the investigation of planning systems that must deal with bounded uncertainty. A deenition of this new class of contingency planners is given. A general, complete contingency planning algorithm is described. The algorithm is suitable to many incomplete information games as well as planning situations where the initial state is only partially known. A rich domain is i...

In recent work we showed that models constructed from planner performance data over a large suite of benchmark problems are surprisingly accurate; 91-99% accuracy for success and 3-496 seconds RMSE for runtime. In this paper, we examine the underlying causes of these accurate models. We deconstruct the learned models to assess how the features, the planners, the search space topology and the am...