It depends on your definition of artificial intelligence.
Is a Kalman filter AI? By most definitions, no. Yet a Kalman filter's job is to generate an ever improving estimate of state. It "learns" by comparing what it think it knows (imperfectly) versus the imperfect results from sensors. Some Kalman filters are extremely sophisticated. They handle sensors that report at different rates, they estimate biases in the sensors as well as where the vehicle is, and they help detect sensor failures. The mathematical hearts of a Kalman filter and a Bayesian belief network (which is AI) are one and the same. The key reason a Kalman filter isn't deemed to be AI is that it wasn't developed by AI researchers.
Is a star tracker AI? A modern star tracker looks for multiple bright stars at once and matches the detected stars against a database of stars. It is doing simple pattern matching. There's not much room in a star tracker's microprocessor for any advanced AI techniques. There's not much room for what most would call AI, period.
Is the Autonomous Landing Hazard Avoidance Technology (ALHAT) being developed by NASA AI? This is a lot closer to what many would call AI. However, is edge detection AI? Is detecting circles and ellipses (aka craters) AI? Does it matter whether the algorithm was developed by an AI practitioner or a signal processing expert? Does matching the detected craters against a database AI? Does it matter that the point of all this is to feed the information to a Kalman filter?
That's the long to midrange part of ALHAT. As the vehicle get's close to the landing site, the technology has to switch from "where am I" mode to "where can I land" mode. It must autonomously find and guide the vehicle to a good landing site. A good landing site is relatively flat and free of obstructions. Does it matter if the technique used to detect a good landing site uses what most would call AI to categorize obstructions as rocks, versus purely mathematical techniques such as wavelet or Fourier transformations?
It shouldn't matter whether the techniques came out of the signal processing community or the AI community, but it does. (Better: AI communities; there's a noted lack of communications amongst different branches of AI, and even a lot of bickering of what AI is.) That I could rewrite a Kalman filter as a Bayesian network and suddenly it's AI? That doesn't make sense.
Another issue that gets in the way of "true" AI from being used in space operations is the languages preferred by AI researchers. LISP won't fly, nor will Prolog (Buran excluded, and it didn't fly). The last thing one wants to happen is to have the computer go into garbage collection mode just as the vehicle is about to land. The algorithms have to be translated to the stodgy languages used by flight software developers, and then toned down further because dynamic memory allocation is taboo in flight software.
One last thing that gets in the way of employing AI in space exploration: A state of the art flight computer is a state of the practice desktop computer from the previous millennium. We won't be able to say Siri, open the pod bay doors on a flight computer anytime soon. Your smart phone has a much faster CPU and a whole lot more memory than does the flight computer on a spacecraft.