Planning: The town planners perspective

Reinforcement Planning: Planners as Policies

Introduction. State-of-the-art robotic systems [1, 2, 3] increasingly rely on search-based planning or optimal control methods to guide decision making. Similar observations can be made about computer game engines. Such methods are nearly always extremely crude approximations to the reality encountered by the robot: they consider a simplified model of the robot (as a point, or a “flying brick”)...

Intelligent Planning Meets Intelligent Planners

Deterministic planning in the fifth international planning competition: PDDL3 and experimental evaluation of the planners

The international planning competition (IPC) is an important driver for planning research. The general goals of the IPC include pushing the state of the art in planning technology by posing new scientific challenges, encouraging direct comparison of planning systems and techniques, developing and improving a common planning domain definition language, and designing new planning domains and prob...

Unrefinement Planning: Extending Refinement Planners with Plan Repair Capabilities

Kambhampati’s refinement planning framework provides a unifying view on planning algorithms. It shows how a planning problem can be solved by successively applying a refinement operator, which adds more and more detail to the plan. In this paper, we show how this framework can be extended to include plan repair capabilities. This extension relies on the inclusion of unrefinement operators, that...

New Insights into Harappan Town-Planning

Dholavira’s elaborate town-planning rests on the conscious use of specific proportions for its successive enclosures. Those proportions combined with the city’s dimensions allow us to calculate precisely the unit of length used for the fortifications, to relate it to the Lothal ivory scale, and to work out potential subunits. Both proportions and units receive overwhelming confirmation from str...