only the large probe takes more detailed data regarding composition, cloud structure, and planetary heat balance. (The large probe considered for the Titan Demonstration is roughly the same size and complexity as the device proposed for the SOP1 mission.) Both types of probes also may serve as limited-purpose surface stations.
Powered air vehicles. Many options exist for intensive atmospheric investigation using still more sophisticated vehicles. A superpressure or passive hot-air (Montgolfier) balloon can be designed to float along an isobar for extended periods of time, providing a continuous record of wind speeds and other atmospheric data. Tethered balloons or kites could be used to sample the aerial environment surrounding a surface station. Powered air vehicles such as airplanes, helicopters, and dirigibles can study still larger regions of the atmosphere.
Of the options considered, the powered air vehicle ? especially one having an inexhaustible energy supply for long-term operation appears preferable. Such craft could be used to support extended surface operations, to conduct remote-sensing observations near the base, and even to help collect samples to be returned to the base site for detailed analysis. Regardless of whether the vehicle is an airplane or dirigible, it is highly unlikely that much previous experience will have been acquired with such systems in planetary missions. While the aerodynamic properties of fliers may match those of some Earth-based machines, control and propulsion requirements are likely to differ significantly. Control problems perhaps may be solved using a combination of smart sensors and an advanced machine intelligence capability, together with a satisfactory energy source such as a 10 kW nuclear-power generator to drive an efficient propeller. Titan's atmosphere possibly could be utilized for the production of propellants or buoyant gas.
Packaging the entire system and deploying it at Titan is an additional concern.
Surface science network. A scientific network should be established consisting of at least three permanent sites on the Titanian surface. The network collects seismographic and meteorological data needed to infer subsurface structure and global atmospheric circulation patterns. There are several ways to establish a network, such as (1) using long-range rovers to deploy stationary science packages, (2) deploying surface penetrators dropped from the main orbiter, and (3) extending the lifetime of the atmospheric probes (also dispatched from the main orbiter).
The network concept emphasizes long-term observation as much as 5 years or more on Titan's surface. Assuming network stations communicate directly to the main orbiting spacecraft, data must be stored for about a week following collection before uplinking. Each station must be able to function in an extremely cold thermal environment (about 100 K) with internal parts maintained at reasonable operating temperatures not below 220 K. Stations must be well-coupled to the planetary surface for seismometric purposes but must not thaw crustal ices. One solution is the radiation of excess heat up into the atmosphere.
All of the above components are relatively simple systems, mostly achievable using current or foreseeable aeronautical technology. 3.2.4 Machine Intelligence and Automation Requirements
In outlining the operational mission stages for a Titan demonstration and for the exploration of deep space, a number of automation technology drivers were identified in each of two general categories of system functions:
(1) Mission integrity, including self-maintenance, survival of the craft, and optimal sequencing of scientific study tasks.
(2) Scientific investigation, including data processing and the methodical formation of hypotheses and theories. Both categories impose considerable strain on current AI technology for development in several overlapping areas of machine intelligence. These requirements represent research needs in domains of present concern in the AI community, as well as new research directions which have not yet been taken.
Success in mission integrity (fig. 3.4) requires the application of sophisticated new machine intelligence techniques in computer perception and pattern recognition for imaging and low-level classification of data. This also presupposes the utilization of a variety of remote- and near-sensing equipment. Onboard processing of collected data serves to coordinate the distributed systems and planning activity in terms of reasoning, action synthesis, and manipulation. More capable remote sensing is the key to efficient exploration, making more selective and efficient use of highly complex equipment for atmospheric and planetary surface monitoring.
With respect to reasoning, automated decisionmaking emerges as an important research area. Within this field, development might depart from current expert systems with advancements coming in the form of interacting simulation models of the processes which structure given domains and hypothesis formulating logics. New research directions lie in the areas of alternative computer logics, self-constructing knowledge bases, and self-learning systems.
A need has been identified regarding action synthesis, or procedural sequencing, for representing the relationship between predefined goal states and the current state, and for reducing the discrepancy between the two through automated implementation of subgoals and tasks. Such a system implies the utilization of a sequential informational feedback loop. A more difficult problem is simultaneous
