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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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  • In other news... (Score:4, Insightful)

    by 14erCleaner (745600) <FourteenerCleaner@yahoo.com> on Friday July 19, 2013 @03:16PM (#44331719) Homepage Journal
    John Henry won the battle, but lost the war. How is being outcalculated by a computer news? Just because it's a hard problem?
    • 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.

      • by CastrTroy (595695)
        Yeah, but there's only so many bodies out there in the solar system. Probably under 200 planets/moons/rocks out there that can be used for this purpose. It probably wouldn't take much to throw a super-computer at the problem and just crunch through all the possibilities.
        • by Rich0 (548339)

          Yeah, but there's only so many bodies out there in the solar system. Probably under 200 planets/moons/rocks out there that can be used for this purpose. It probably wouldn't take much to throw a super-computer at the problem and just crunch through all the possibilities.

          Naively I'd be inclined to agree, but there are a lot of combinations. Cassini encountered Venus twice, the Earth once, and Jupiter once on the way to Saturn. Jupiter is a pretty natural target to hit once at the end of the trip since it is so large and far out, and you really don't want to hit it more than once unless you want to have your grandkids crunching your data when the mission is over. However, for the inner planets there are a lot of possibilities. Again, you don't have to hit them at your pe

        • Even accounting only 4 possible bodies (as in the Jupiter system) if 140 fly-bys are to be designed (as was the case for this particular mission) and at each fly-by one has to choose to "perform experiment" in of 1 out of 32 areas of the moon/planet, you have (4*32)^140 = 10^(295) possibile routes that all need to be optimized in low-thrust. It does not seem likely a computer will ever be built that can do that.
  • I'm surprised that humans can even do such problems. Numerical optimization by hand sucks. There are some strategy issues (should we slingshot around a planet?), but there aren't usually a vast number of options like that. So you crunch on all the plausible options. I wouldn't expect that this is a problem dominated by local minima.

    • by Ost99 (101831)

      Eh.
      It's not beating optimization by hand, it's beating computerized optimization of all plausible options worked on by experts in the field.

  • by slew (2918) on Friday July 19, 2013 @03:38PM (#44331941)

    The competition was not that AI was in competition with humans to develop spacecraft trajectories, it was that humans were in competition with other humans to quickly develop frameworks create the best mission design in a complicated search space that had multiple local optima and unusual constraint functions (preventing the use of "canned" solvers).

    One of the critera used to select the problem was...

    Problem is easy enough to tackle in a 3-4 week timeframe for experienced mission designers or mathematicians, including exploration of new algorithms.

    Of course many of the teams in the competition probably used AI-like frameworks to find the actual trajectories so it's unsurprising an AI technique won. Although perhaps some teams tried other non-AI-like searching techniques (like pseudo-objective functions), I'm pretty sure none of the teams chose to use human pondering to come up with mission designs.

  • It's an interesting parallel problem. I wonder if GPU processing will be the best architecture for it. (and keeping in form, I didn't read the article yet)
  • by Tablizer (95088)

    it's not like it's rocket science.....oh, wait

  • Oh boy. Imagine: computers are more precise with complex mathematics than humans. Whoop-dee-doo! Call me when they can answer the really tough questions, such as, "Does this dress make me look fat?"

    • Call me when they can answer the really tough questions, such as, "Does this dress make me look fat?"

      Interesting game. The only winning move is not to play.

  • That's funny, because that's amazingly close to the last thing I had my genetic algorithms trying to solve.

    I wanted them to use assembly instructions to move dudes around a grid with a sword and shank each other. But I was having a hard time getting them to do anything more advanced than "move forward, if I hit anything, turn right". Or "move east while spinning around". Hell, since it was competitive co-evolution, they couldn't even stay on local maxima for more than a few thousand generations. That was

  • All we have to do is discover the Spice planet.

  • I strongly suspect that it will not be AI that solves the n-body problem in a meaningful way. Because artificial "intelligence" is a misnomer commonly applied to non-creative, non-elegant, glorified calculators that not only can't think outside the box, but are still capable of being derailed by a single misplaced decimal point. So while some of us love our fuzzy giant thinkertoys, they are still struggling to work up to gnat level in the intelligence department.

  • Genetic algorithms is just another numerical optimization method programed by human programmers. The computer did nothing by itself. I am sure that the same results (perhaps a little better but at the cost of more time according to literature) can be obtained by simulated annealing. Definitely not AI.
    AI is when the computer learns from previous experience. GA do nothing like that.

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