Nir Lipovetzky

Width-based algorithms for classical planning: new results

We have recently shown that classical planning problems can be characterized in terms of a width measure that is bounded and small for most planning benchmark domains when goals are restricted to single atoms. Two simple algorithms have been devised for exploiting this structure: Iterated Width (IW) for achieving atomic goals, that runs in time exponential in the problem width by performing a sequence of pruned breadth first searches, and Serialized IW (SIW) that uses IW in a greedy search for achieving conjunctive goals one goal at a time. While SIW does not use heuristic estimators of any sort, it manages to solve more problems than a Greedy BFS using a heuristic like hadd. Yet, it does not approach the performance of more recent planners like LAMA. In this short paper, we introduce two simple extension to IW and SIW that narrow the performance gap with state-of-the-art planners. The first involves changing the greedy search for achieving the goals one at a time, by a depth-first search that is able to backtrack. The second involves computing a relaxed plan once before going to the next subgoal for making the pruning in the breadth-first procedure less agressive, while keeping IW exponential in the width parameter. The empirical results are interesting as they follow from ideas that are very different from those used in current planners.

Real--Time UAV Maneuvering via Automated Planning in Simulations

The automatic generation of realistic behaviour such as tactical intercepts for Unmanned Aerial Vehicles (UAV) in air combat is a challenging problem. State-of-the-art solutions propose hand–crafted algorithms and heuristics whose performance depends heavily on the initial conditions and specific aerodynamic characteristics of the UAVs involved. This demo shows the ability of domain–independent planners, embedded into simulators, to generate on–line, feed–forward, control signals that steer simulated aircraft as best suits the situation

Scheduling with Fixed Maintenance, Shared Resources and Nonlinear Feedrate Constraints: A Mine Planning Case Study

Given a short term mining plan, the task for an operational mine planner is to determine how the equipment in the mine should be used each day. That is, how crushers, loaders and trucks should be used to realise the short term plan. It is important to achieve both grade targets (by blending) and maximise the utilisation (i.e., throughput) of the mine. The resulting problem is a non-linear scheduling problem with maintenance constraints, blending and shared resources. In this paper, we decompose this problem into two parts: the blending, and the utilisation problems. We then focus our attention on the utilisation problem. We examine how to model and solve it using alternative approaches: specifically, constraint programming, MIQP and MINLP. We provide a repair heuristic based on an outer-approximation, and empirically demonstrate its effectiveness for solving the real-world instances of operational mine planning obtained from our industry partner.