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Space AI Math NASA Open Source Python

Evolution of AI Interplanetary Trajectories Reaches Human-Competitive Levels 52

Posted by Soulskill
from the skynet-practicing-for-conquest-of-the-solar-system dept.
New submitter LFSim writes "It's not the Turing test just yet, but in one more domain, AI is becoming increasingly competitive with humans. This time around, it's in interplanetary trajectory optimization. From the European Space Agency comes the news that researchers from its Advanced Concepts Team have recently won the Gold 'Humies' award for their use of Evolutionary Algorithms to design a spacecraft's trajectory for exploring the Galilean moons of Jupiter (Io, Europa, Ganymede and Callisto). The problem addressed in the awarded article (PDF) was put forward by NASA/JPL in the latest edition of the Global Trajectory Optimization Competition. The team from ESA was able to automatically evolve a solution that outperforms all the entries submitted to the competition by human experts from across the world. Interestingly, as noted in the presentation to the award's jury (PDF), the team conducted their work on top of open-source tools (PaGMO / PyGMO and PyKEP)."
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Evolution of AI Interplanetary Trajectories Reaches Human-Competitive Levels

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  • Re:In other news... (Score:5, Informative)

    by Rich0 (548339) on Friday July 19, 2013 @03:51PM (#44332105) Homepage

    John Henry won the battle, but lost the war. How is being outcalculated by a computer news? Just because it's a hard problem?

    It involves a certain amount of intuition. You'd always use a computer to optimize a trajectory, but picking the overall approach to be used requires some educated guessing. You can visit any other planets along the way, or their moons, and you can visit them at an optimal point along their orbit or somewhere that is non-optimal (from the standpoint of that particular encounter). You can launch today at one cost, or wait 20 years and maybe launch at a cheaper cost.

    So, the current approach is generally to have physicists come up with a couple of basic plans, then use computers to optimize each one, and then see which works best, or perhaps iterate.

    Looking at it another way, this is similar to any other problem where you're trying to find the lowest minima in a function that has many local ones. Finding the nearest minima is easy - finding the best is much harder.

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