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 choose another target. An intriguing alternative would be a system capable of redesigning or adapting its equipment to accommodate the relevant alien environmental conditions.

Encounter. The processing of image data is probably one of the most computationally demanding tasks performed during planetary exploration missions. In the Encounter phase, when the spacecraft controller must make a quick go/no-go decision on the question of orbital insertion, the data processing challenge includes speed as well as volume. The problems of distance and communications delays, coupled with the necessity of making rapid local decisions, virtually demand that image analysis during Encounter be accomplished by fully autonomous onboard processing systems.

One possibility is an imaging system capable of describing a planetary body much as an astronaut would. For example: "The surface is bluish with some brownish areas near the equator. There appear to be thin wispy clouds covering a 100 X 200 km area centered about 75? N and 30? W." The observation of "bluish" and "brownish" indicates the processor's ability to match raw data inputs to color concepts understood by humans. The identification of "wispy clouds" suggests the capability of matching data in a sequential region of the image to the known concept of "wispy." The ability to match regions, spectral data, and other features in an image to stored concepts in memory requires a reasonably high level of machine intelligence.

Another part of the description of the image observed by spacecraft sensors locates the "wispy" area at a given latitude and longitude. To do this, the processor must be able to establish the geometrical shape of the body encountered and to apply a coordinate system to it. Once this coordinate system is computed it forms the cartographic grid to which all surface features are mapped. While this is a well-understood mathematical procedure, the "number crunching" load is significant and must be executed very rapidly during the Encounter phase.

Orbit. When preliminary analysis suggests a reasonably benign environment warranting further investigation, orbit is established to conduct a more detailed study. The establishment and maintenance of orbital position, like most of the functions already mentioned, should be a fully autonomous process with characteristics similar to the autonomous interplanetary flight navigation system. Onboard automated decisionmakers determine an optimal orbit using information gathered during preliminary analyses, and orbital insertion is achieved.

Multisensor analysis is implemented concurrently with the establishment of orbital position, permitting a more comprehensive investigation of planetary characteristics than during Encounter. During Orbit phase a variety of sensors and sophisticated image processing techniques are employed to examine atmospheric and surface conditions. Analyses should be conducted both in the context of (1) pragmatic decisionmaking, including assessments of atmospheric pressure, density, and identifications of surface conditions to be utilized in judging which equipment to deploy, and of (2) scientific investigation, such as information acquisition for hypothesis generation.

For the Titan mission an advanced expert system may be used to form judgments about appropriate exploratory equipment for specific environmental conditions. For instance, when deploying probes or landers smart sensors might first assimilate data regarding atmospheric density and pressure. The advanced expert system could then make probability judgments as to how fast probes should fall and how much retrorocket energy is required for landing. Additional assessments could be made of surface conditions, such as whether the surface is composed of a solid, liquid, or gaseous base. This information supports subsequent decisions about necessary configurational requirements of landing craft (e.g., should it be a wheeled, walking, hovering, or floating vehicle?). The above machine intelligence applications could probably be developed on a relatively short- term basis, utilizing minimal extensions of state-of-the-art AI techniques.

In the deployment of such exploratory mechanisms as atmospheric and surface probes, balloons, and landers, intelligent coordination of autonomous orbit maintenance and control is crucial. Since deployment of onboard equipment alters the total mass and mass distribution of the orbiter, some simultaneous revision of the altitude control function, ideally based on "anticipatory information," is required. That is, the spacecraft must anticipate changes in its state prior to component deployment and be prepared to adapt to concomitant variations in its physical state (a specific example of the type of feedback system required to maintain mission integrity).

A much more serious problem for development in the area of machine intelligence is the scientific analysis of data and the autonomous formulation of hypotheses and theories. Current expert systems technology cannot generate and test unique hypotheses that have not been preprogrammed by a human operator. This limitation restricts an exploratory device based on state-of-the-art AI to data analysis, categorization, and classification in terms of existing structures of thought or taxonomies of knowledge. However, in alien environments, particularly those accessible in an interstellar mission, pre-formed scientific notions may not reasonably be applicable; on the contrary, they may serve only to distort higher-order understanding of incoming data. Thus, a major technology driver is the development of an advanced machine intelligence system capable of reorganizing rejected hypotheses, integrating that information with data acquired through sensory apparatus, generating new hypotheses which coordinate all existing