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Future planetary exploration of the outer satellites of the Solar System will require higher levels of onboard
automation, including autonomous determination of sites where the probability of significant scientific
findings is highest. Generally, the level of needed automation is heavily influenced by the distance between
Earth and the robotic explorer(s) (e.g. spacecraft(s), rover(s), and balloon(s)). Therefore, planning missions
to the outer satellites mandates the analysis, design and integration within the mission architecture of semi- and/or completely autonomous intelligence systems. Such systems should (1) include software packages
that enable fully automated and comprehensive identification, characterization, and quantification of
feature information within an operational region with subsequent target prioritization and selection for
close-up reexamination; and (2) integrate existing information with acquired, "in transit" spatial and
temporal sensor data to automatically perform intelligent planetary reconnaissance, which includes
identification of sites with the highest potential to yield significant geological and astrobiological
information. In this paper we review and compare some of the available Artificial Intelligence (AI)
schemes and their adaptation to the problem of designing expert systems for onboard-based, autonomous
science to be performed in the course of outer satellites exploration. More specifically, the fuzzy-logic
framework proposed is analyzed in some details to show the effectiveness of such a scheme when applied
to the problem of designing expert systems capable of identifying and further exploring regions on Titan
and/or Enceladus that have the highest potential to yield evidence for past or present life. Based on
available information (e.g., Cassini data), the current knowledge and understanding of Titan and Enceladus
environments is evaluated to define a path for the design of a fuzzy-based system capable of reasoning over
collected data and capable of providing the inference required to autonomously optimize future outer
satellites explorations.
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