During the summer of 1980 eighteen educators throughout the United States worked with fifteen programme engineers to examine the ways in which automation, including artificial intelligence (AI) and robotics might be used in space missions in the next ??-50 years. The study was supported by NASA because of increasing realisation that advanced automatic and robotic services will play a major role in future missions of space exploration and utilisation, and may provide enormously beneficial capabilities at affordable cost.

The 10-week, 10,000-man-hour study, hosted by the University of Santa Clara and jointly sponsored by the American Society for Engineering Education and the NASA/Ames Research Center, was given the task of selecting and defining a number of representative space missions which would require extensive application of machine intelligence and automation. This was to be followed by an assessment of existing and foreseeable technologies which must be available to accomplish the proposed missions. The workshop was divided into four teams for most of the. study: Terrestrial Applications, Space Exploration, Non-Terrestrial Utilization of Materials, and Replicating Systems Concepts Each team was to choose an exciting, challenging space venture that could be used to identify critical technology needs for future research and development.

The Terrestrial Applications team concluded that artificial intelligence techniques would be most useful in near-Earth missions which generate data at very high rates - as the Landsat terrestrial imaging system. As presently operated, such satellites record everything they "see," causing a deluge of data at the user end which must laboriously be sifted for desired information. The team used a new, highly versatile Intelligent Earth Sensing Information System (IESIS), able to perform substantial amounts of selection and interpretation of incoming sensor and provide more useful information tailored to the is of the individual user. A new philosophy of goal-oriented data collection, in which information is gathered to meet specific objectives, was devised by the team as the cornerstone of the proposed mission.

The IESIS would include the following major features:

1. An intelligent satellite network which gathers data in a goal-directed manner, based on specific requests for observation (such as a farmer requesting once-a-week surveillance is cornfield) and on prior knowledge contained in a detailed, self-correcting "World Model" (see below);
2. a user-oriented "natural language" interface which permits requests to be satisfied in plain English, without additional human intervention, using information retrieved from the system library or from direct observations made by a member satellite within the network;
3. a medium-level onboard decision-making capability that optimises sensor utilisation without compromising user requests; and
4. a array of stored information which provides a detailed set of all significant planetary features and resources, adjustable for seasonal and other identifiable variations and accessible through a comprehensive cross-referencing system.

The Space Exploration team early identified interstellar navigation by automatic probe as the ultimate goal in the decades ahead. There was much interest in the work completed by the Project Daedalus Study Group under the auspices of the BIS. The team consequently defined their overall study concept as an autonomous interstellar space exploration system incorporating advanced machine intelligence technology in order to telescope the customary three stages of investigation currently used by NASA reconnaissance, exploration, and intensive study - into a single integrated scientific phase of discovery.

As a preliminary shakedown voyage for the technology which might be employed to journey to other star systems, the team suggested a Titan Demonstration Mission around the year 2000 which would be capable of independent operation starting from launch in Low Earth Orbit; navigation and propulsion system control during interplanetary transfer to Saturn-, rendezvous with Titan (the largest Saturnian moon) and orbital insertion; automatic landing site decision making; deployment of various subsatellites, landers, and fliers on and about Titan; and subsequent monitoring and control of atmospheric and surface exploration. A major finding of the study team was that automated hypothesis formation is highly desirable for sophisticated interplanetary missions within the Solar System but is absolutely essential for interstellar exploration. Machine intelligences capable of unassisted scientific and operational hypothesis formation must be able to handle three distinct classes of inferential thinking:

1. Analytic inference (application of existing scientific classification schemes),
2. inductive inference (logical processes for generating universal statements about an entire domain based on quantitative or symbolic information from a restricted part of that domain), and
3. abductive inference (a method for evolving new information classification schemes using old theories, old schemes, old predictions, and novel contradictory data as inputs).

The team identified a number of new technologies that may ease the transition to manufacturing in space, including:

1. A ground-based electromagnetic catapult to fling large masses of raw materials into Low Earth Orbit at relatively little cost;
2. use of the electrophoretic materials separation principle to achieve rapid and efficient benefication of raw lunar soil;
3. a robot crawler able to cannibalise the Space Shuttle external tank (after delivery empty to LEO) as a convenient early source of cheap aluminium metal;
4. use of native basalts as construction materials for almost everything required in a lunar base (basalt can be cast into machine parts or in bulk as slabs for construction, drilled with holes, aluminised for mirrors or printed circuit boards; and even spun into asbestos-like threads for use as insulation, woven fabrics, and so on );
5. advanced casting and powder metallurgy technologies which may permit industrial feedstock, machine parts, or even large space structures to be entirely built up by "raster scanning" a collimated beam of atomised metallic particles across the workpiece and making use of the vacuum cold weld effect for binding; and
6. the creation and marketing of what the team called "teletourism," a way for people who remain on Earth to vicariously explore the caves and valleys of the Moon or play a few rounds of lunar golf.

The team developed two complementary "proof-of-concept" designs for a self-replicating lunar factory with closure. The first, consisting of factory units of fixed size. mines raw lunar soil, processes the material into useful elements and chemicals, then produces parts and assemble. them into duplicates of the original facility. When a "field of these individual units of the desired aggregate manufacturing output is attained, useful commercial production begins. The second design, consisting also of a single basic "seed" facility initially landed on the lunar surface, increases its output capability not by making more copies of itself but rather by growing to a larger size, spreading out in a growing circular pattern across a mare or lunar crater floor. Preliminary calculations indicate that the original seed in either design may have a mass at least as low as 100 tons and a replication time on the order of one year. The team, also considered possible types of logical organisation for such a factory, and proposed a very specific Earth-based demonstration of the concept.

Major new technology needs were identified during the summer study in the areas of autonomous World Model generation (including land, ocean and atmospheric modelling, data storage, automatic mapping, image processing, "smart sensors," complex systems management, plan formation and scheduling, information extraction and pattern recognition techniques), machine learning and hypothesis formation. natural language and other man-machine communication. space manufacturing automation, teleoperation/robotics. and computer science and technology.

Further information may be obtained directly from Dr. Timothy J. Healy, Co-Director of the Summer Study on Advanced Automation for Space Missions, Department Of Electrical Engineering and Computer Science, University Santa Clara, California 95053, USA.