coordination through anticipation, or prediction of the most appropriate action patterns followed by implementation of such action before a large discrepancy occurs. Complementary to the above capability is the capacity for automated construction of unprogrammed goal states as the result of environmental feedback. These latter two technology drivers fall under the general heading of automated learning and are not part of current research interests in the AI community at large.
Another broad technology requirement within the category of mission integrity is manipulation. A fully autonomous system should be capable of self-maintenance and repair, as well as sample collection for data analysis and utilization in decisionmaking processes. The former task presupposes some initial ability for self-diagnosis, while both tasks require a variety of effector capabilities for dealing with a wide range of situational demands. Here, advances in robotics with respect to hand-eye coordination
ONBOARD PROCESSING (ORBITER) MULTISPECTRAL SENSING IMAGING PERCEPTION PATTERN RECOGNITION CONTROL OF DISTRIBUTED SYSTEMS PLANNING (PROCEDURAL SEQUENCING) OUTLINING AND IMPLEMENTATION AND SUBGOALS LEARNING REASONING DECISION MAKING ? ? ? ? ? - EXPERT SYSTEMS (e.g.. GIVEN CERTAIN ATMOSPHERIC CONDITIONS WHAT ARE THE IMPLICATIONS FOR EQUIPMENT DEPLOYMENT) REQUIREMENTS: (7) LOGIC FUNCTIONS SELF CONSTRUCTION OF KNOWLEDGE BASE THROUGH EXPERIENCE (SELF-LEARNING EXPERT SYSTEMS) REQUIREMENTS: REPRESENTATION OF GOAL STATE REPRESENTATION OF PRESENT STATE CAPACITY FOR NOTING DISCREPANCY ACTUATORS FOR MODIFICATION ANTICIPATION: PREDICTION BASED ON EXPERIENCE ? CAPACITY FOR CONSTRUCTING UNPROGRAMMED GOAL STATES MANIPULATIONS: Figure 3.4. - Mission integrity.
