Abstract In many planning and scheduling aerospace applications that are investigated in the Airbus AI research group, there is no access to a white box model of the transition logics between states. For instance, planning aircraft paths in airways requires to evaluate the fuel burn and flight time between two successive waypoints, which can be only evaluated on-the-fly by an aircraft performance simulator as a function of current aircraft mass, speed, altitude and atmospheric conditions. At a lower level, planning continuous trajectories between successive waypoints for a real aircraft calls for a simulation of the dynamics of the aircraft based on a complex interaction between many avionic systems. For satellite mission planning applications, the dynamics of satellites and of the observed phenomena are usually so complex that they can only be accessed via simulators using current satellite's orientation and orbit position, weather conditions and predictions, and observation requests' status. Even worse, those simulators are usually very time-consuming, sometimes requiring several seconds of computation for a single simulation step, i.e. for generating a single transition of the planning problem. In addition, the state and action spaces are so huge that they cannot be enumerated beforehand, so as the transitions of the planning system. Those challenges impose to rethink the assumptions behind many classical algorithms from the community. We argue that the complexity of our aerospace applications resides more in the lack of transparent transition models and high combinatorics rather than on the expressivity of the underlying problem class which is generally quite simple. We will show how our Airbus AI research team, in collaboration with academic planning research groups, adapted some path planning, reinforcement learning, width-based planning and meta-heuristic algorithms to the challenges of real aerospace sequential decision-making with simulators only.
Abstract Planning and Scheduling (P&S) find many applications in space and aeronautic domains, where autonomous solutions are growing quickly, as in ground transportation or in defense areas. Ranging from piloting assistance to autonomous unmanned systems, P&S has an in-depth impact on the system architecture which must face several challenges. The operational environment provides multiple sources of contingencies and uncertainties, due to the kinematic of surrounding objects, the lack of efficient situational awareness, the interactions with human beings and the potential hazards. In the assistance / autonomous system state of the art, the execution control must constantly adapt the plan to the reality, dealing with contingencies and uncertainty. In addition the lack of homogeneous dataset prevents from using approaches solely based on machine learning approach. However, aerospace systems must be fault-tolerant, and their design have to demonstrate correct execution with respect to various types of failures, placing in it a “safe” state (so called fail-safe mode), or able to continue the mission (so called fail-operational mode). In some cases it is possible to optimize fault recovery, such that the system becomes resilient. As a result of design process, some system constraints have to be applied to the embedded processing architecture, which must provide integrity and safety properties. The presentation reviews the impacts of fail-safe and fail-operational aerospace systems on planning models, solving algorithms and their integration environment.
Abstract Thales is a multinational company providing innovation and services in aerospace, space, ground transportation, digital identity & security and defense & security markets. In the past decade, its teams at its research center have used automated planning to solve decision problems, mainly for crisis management systems. Following its initial investment on automated planning solvers with INRIA and ONERA, which led to the win of the IPC 2011 temporal satisficing track, several use cases have been solved thanks to this technology. However, the use of PDDL solvers has been put on hold after this period, mainly because of scalability issues. This presentation will try to explain the how and why of this decision, hoping that the academic community will be able to solve the underlying problems.
Abstract At AntsRoute, we are providing an optimization software that allows to solve different optimization problems in logistics. Our software not only facilitates decision making for daily, weekly, and/or monthly planning of ordinary user or a professional customer in an optimal manner but also it visualizes the obtained plans via a very user friendly graphical interface. The optimization library is developed in a generic way combining algorithms of operation research and artificial intelligence that can address different kind of Vehicle Routing Problems (VRP): Classic VRP, multi-depot VRP, Pickup and Delivery Problem (PDP). For each type of problems, any set of constraints for time window, capacity, vehicle duration, skills, and driver availabilities can be activated. The software can also help users to decide which date and time window is the optimal choice among the existing availabilities upon the arrival of a new order. Our so far experience shows that clients are looking for a software that provides a solution with high quality in a short amount of time. However, existing optimization algorithms cannot necessarily answer to all customer demands in a short amount of time. Besides, they are looking for a software that provides flexibility in their plannings : they prefer make changes over provided plans in real time.
Abstract Classical scheduling problems (like job-shop or RCPSP) are among the most difficult problems studied in combinatorial optimization. Still, they are far from accounting for all the complexity of industrial scheduling applications. Since more than 20 years, our team at ILOG (now IBM) develops and integrates a large panel of techniques from AI (constraint programming, temporal reasoning, learning, ...) and OR (mathematical programming, graph algorithms, local search, ...) to solve our customers most complex scheduling problems. These works have lead to the design of CP Optimizer, a generic solver based on a very expressive (but still, quite concise) mathematical modeling language to formulate complex scheduling problems. The models are solved with an automatic search algorithm that is exact, efficient, robust and continuously improving. This talk gives a short overview of CP Optimizer.
Abstract Planning, the task of finding a procedural course of action for declaratively described systems, is one of the oldest and well studied tasks in the field of Artificial Intelligence. Over the years, planning techniques have been applied to many real life applications. A few examples include but not limited to robotics, manufacturing, cyber security, diagnosis and remediation, logistics, transportation, and decision making in space. These applications often go beyond the classical setting in planning, requesting numeric features, state constraints, soft goals, non-deterministic effects, hierarchical compositional structure, etc. Further, even in cases that are close to classical planning, there are often no symbolic models available and, as a result, such models need to be constructed automatically from the existing data. Both these aspects pose a significant knowledge engineering and extraction challenge. Some of it, however, can be compiled away, some can be mitigated. In this talk, I will show how do we tackle problems with seemingly beyond classical features, using classical tools. Such tools include planning task transformations and reformulations, as well as top-k/top-quality/diverse planners. Using classical planners allows us to easily benefit from the progress of the planning community, constantly producing better classical tools, and solve problems of a large size.