Cooperative Systems for Perceptual Tasks in a Remote Sensing Environment

To design and implement knowledge-based systems for perceptual tasks, such as interpreting remotely-sensed data, we must first evaluate the appropriateness of current expert system methodology for these tasks. That evaluation leads to four conclusions which form the basis for the theoretical and practical work described in this paper. The first conclusion is that we should build 'cooperative systems' that advise and cooperate with a human interpreter rather than 'expert systems' that replace her. The second conclusion is that cooperative systems should place the user and the system in symmetrical roles where each can query the other for facts, rules, explanations and interpretations. The third conclusion is that most current expert system technology is ad hoc. Formal methods based on logic lead to more powerful, and better understood systems that are just as efficient when implemented using modern Prolog technology. The fourth conclusion is that, although the first three conclusions can be, arguably, accepted for high-level rule-based symbol-manipulation tasks, there are difficulties in accepting them for perceptual tasks that rely on visual expertise. In the rest of the paper work on overcoming those difficulties in the remote sensing environment is described. In particular, the issues of representing and reasoning about image formation, map-based constraints, shape descriptions and the semantics of depiction are discussed with references to theories and prototype systems that address them.