and force/proprioceptive feedback systems emerge as significant.
The technology drivers identified for the scientific investigation category of mission functions (fig. 3.5) overlap to some degree those outlined for mission integrity. Automated intelligent planning is perceived as a general requirement in terms of defining scientific goals (both preprogrammed and self-generated) and for the definition of appropriate subgoals. Advanced decisionmaking also is an essential prerequisite for implementing scientific research and for conducting experiments. Decisions such as whether or not an experiment should be carried out, or where and when it should be conducted, probably could be accomplished (as with mission integrity) through extensions of current expert systems technology.
Reduction of collected sensory data to informational categories is yet another significant technology driver. A number of requirements emerge, starting with the ability to describe data at the simplest perceptual level. A higher- order task is the addition of data descriptions to a knowledge base for purposes of classification. This classification may be accomplished in terms of given categories of knowledge requiring some low-level hypothesis generation and testing. More advanced is the necessary capability for reorganizing old categories into new schemes or structures as a consequence of active information acquisition. Underlying this form of classificatory activity is again the self- learning process of hypothesis formation and testing. Each of the aforementioned tasks require varying levels of research and development to transform them into fully realized capabilities.
Finally, a requirement exists within the area of communication - transmitting acquired information back to human users. Here the emphasis is on automated selection processes in which an advanced decisionmaking system determines what information and which hypotheses are appropriate and sufficiently interesting to report. The obvious need to communicate with human beings in this case underscores the need for further developments in the field of natural language interfaces.
A scenario illustrating the great complexity of data processing and high-level hypothesis formation capability required for scientific investigation by an autonomous exploration system is presented in appendix 3C.
3.3 Machine Intelligence in Space Exploration Missions
The advanced machine intelligence requirements for general-purpose space exploration systems can be summarized largely in terms of two tasks: (1) Learn new environments, and (2) formulate new hypotheses about them.
D
c> 0 PLANNING ACTION SYNTHESIS
- PROGRAMMED ? ? ?
? ? GOALS -SELF-DIRECTED DECISION MAKING
-WHETHER OR NOT TO CONDUCT EXPERIMENTS -WHERE EXPERIMENTS SHOULD BE CARRIED OUT - USE OF APPROPRIATE SENSORS AND EXPERIMENTAL APPARATUS
DATA PROCESSING -REDUCTION OF SENSORY DATA INTO INFORMATION CATEGORIES REQUIREMENTS: DATA
DESCRIPTION ADDING NEW DESCRIPTIONS TO KNOWLEDGE BASE CLASSIFICATION IN TERMS OF GIVEN CATEGORIES OF KNOWLEDGE HYPOTHESIS GENERATION AND TESTING REORGANIZATION OF OLD
CATEGORIES INTO NEW ONES WHEN THE OLD ARE NO LONGER SUFFICIENT COMMUNICATION OF RESULTS - INFORMATION REDUCTION
- REPORT OF INTERESTING FINDINGS
Figure 3.5. - Scientific investigation.
