the examples it was shown, Winston's software created structural descriptions of the essentials of the concept in the form of a semantic network.
The function of such a program may appropriately be defined as concept learning. However, the programming techniques appear more closely wedded to the notion of concepts as feature lists rather than as prototypical, analogical structures. This "feature view" has theoretical limits in the domains of human and artificial intelligence since a number of abstract categories can be identified in which constituent members have a few or no structural features in common but whose relationship is either more functional in nature or salient "in more broadly specifiable terms" (Boden, 1977; Rosch and Mervis, 1975). Salience for Winston's program relates only "to categorizations made by its human teacher for human purposes" (Boden, 1977). It is difficult to see how a program with a feature list assumption could move beyond predefined categories to handle the construction of new abstract concepts. This is a significant constraint on state-of-the-art AI technology in terms of future space missions requiring autonomous exploration in novel environments where "there is no guarantee that categorizations previously found useful would still be salient" (Boden, 1977).
Genetic epistemology. One final consideration with respect to intelligent activity comes from Jean Piaget's work on genetic epistemology. This topic is relevant to the issues addressed in this chapter because genetic epistemology offers one of the most comprehensive views of intelligence to be found in the literature today. Piaget's conceptions of the underlying processes of "natural" intelligence encompass the behavioral and cognitive activities of humans and animals. Moreover, the processes are sufficiently general possibly to be captured in a nonliving artifact which would then serve as an effective realization of non-natural intelligence (Piaget, 1970).
How can intelligence be characterized in terms of structures and processes so that it might be embodied in a computer system? One important assumption of Piaget's theory is that any account of the evolution of cognitive activity and intelligence must include the nonteleological aspects of adaptation and purpose. The process of equilibration, a regulative function which propels the subject toward more inclusive and stable interactions with its environment, is basic to the theory. The deterministic result of equilibrium is seen as a characteristic structuring of the relations between subject and environment (Piaget, 1963).
There are two processes that subjects must coordinate in order to achieve a state of equilibrium: Assimilation and accommodation. Assimilation, exhibited by all organisms, is the functional aspect of structure formation by which subjects, acting on their environment, modify it in terms of existing structures (Piaget, 1970). Each organism possesses a set of generalized behavior patterns, or action schemes, which support its repetitive modification of its environment for the purpose of producing an expanded set of interactions. Accommodation is the modification of the assimila- tory cycle itself as a result of the subject's interactions with its surroundings (Piaget, 1963). Accommodation involves the transformation of existing structures in response to continuous environmental stimulation. The result is the construction of new categories of experience which then become part of the organism's general behavioral repertoire.
For Piaget, these "schemes" are the basic units for structuring knowledge (Rosenberg, 1980), the means by which all overt behavioral and cognitive activity is organized. The notion of "scheme" defined by Piaget has certain similarities to Minsky's "frames" as the basic units of knowledge representation. Both notions imply a top-down processing schedule for intelligent activity. However, the two notions differ dramatically in terms of their dynamics. The frame permits a kind of assimilatory activity (organization of particulars within its structure) but the structure itself is relatively static there seems to be no possibility for reorganization of the structure (the frames) in response to experience. Alternatively, the scheme emphasizes both assimilative and accommodative processes. Accommodation in this case is the restructuring of available schemes into new higher-order schemes which subsume all previous particulars while simultaneously permitting the inclusion of new ones. Again the primary gap between the level of intelligence available with current AI approaches and that which characterizes more advanced intelligent activity appears in the domain of emergent change. Transforming present knowledge structures into new higher-order schemes is a prerequisite for fully intelligent activity, and this capability is absent from state-of-the-art AI techniques.
While the utilization of a genetic epistemological framework has not yet received much study by researchers in the AI field, it has attracted some recent attention in other quarters. For instance, Rosenberg (1980) suggests a number of ways to blend Piaget's theory and current AI methodology to their mutual benefit. Perhaps this represents the beginning of a recognition of the need for comprehensive formulations of natural intelligence to be incorporat ed into the development of a theory of intelligence in nonhuman artifacts.
3.4 Technology Drivers for Automated Space Exploration
The most important single technology driver for automated space-exploration missions of the future is advanced machine intelligence, especially a sophisticated MI system able to learn new environments and to generate scientific hypotheses using analytic, inductive, and abductive reasoning. Within the AI field the most powerful technology driver is the demonstrable need for an abductive inferential capability useful for inferring new successful knowledge structures
