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Automated planning and

  • Engineering processses.

Developing techniques for space missions and scientific infrastructures

This group is actively involved in developing techniques to build efficient plans in the field of global search devoted to automated planning and scheduling for constraint-based problems and to define dispatcher algorithms based on astronomical heuristics.

Artificial Intelligence for space missions

Automated planning and scheduling is a very active field of research within the area of Artificial Intelligence, focusing on the optimization of sequences of actions that should be executed by intelligent agents. The automatic planning and scheduling of space missions, ground-based observatories and astronomical instruments has become of strategic importance for several reasons: to coordinate the operation of multiple instruments or observatories located at different points of the Earth; the dynamic and fast reaction to changes in the environment or to astronomical phenomena; maximising scientific return and minimising operating costs; etc. Automated planning and scheduling is a branch of Artificial Intelligence that responds to these challenges, increasing the autonomy and efficiency of these scientific infrastructures.

Automated planning and schudules.


The purpose of our research is two-fold: first, to propose, analyse and develop techniques aimed on building efficient plans (with a scope that can range from years to a day) fulfilling all the constraints of the problem that can be predicted; and second, to define and implement processes that adapt the ideal plans reacting dynamically to unexpected situations in a short period of time (in the order of a few seconds).

The first research line is framed in the field of global search devoted to automated planning and scheduling for constraint-based problems. Particularly, we mainly focus on scheduler systems based on Evolutionary Algorithms (e.g., Genetic Algorithms, Multiobjective Evolutionary Algorithms), which are Artificial Intelligence techniques that emulate natural evolution by means of combining potential solutions using selection, combination and mutation operators. The goal of Evolutionary Algorithms is to efficiently explore a large amount of potential solutions in order to find near-optimal solutions. A solution is considered near-optimal (or efficient) when it fulfils all problem constraints and it highly optimises the objectives defined by the problem (i.e., maximisation of the time observing objects, minimization of idle time, maximisation of completed proposals). In this sense, we pay special attention to Multiobjective Evolutionary Algorithms, which are recognized as one of the most valuable and promising approaches to addressing complex and diverse problems of multi-objective optimization.

The second research line is focused on defining dispatcher algorithms based on astronomical heuristics that repair the ideal plans avoiding intensive calculations in order to provide a response in a few seconds. This step requires a great understanding of the problem where the process is being applied in order to define rules that model the reaction that scientists/engineers/operators expect in particular situations, without leaving aside the optimization obtained by the scheduler system.

Senior institute members involved

Meet the senior researchers who participate in this research group.

  • Alberto García

  • Josep Colomé