involved in the utilization of classification schemes. However, the invention of new or revised schemes demands completely different types of inference. Two patterns of inference comprise this advanced activity - "induction" (included in all standard accounts of inference) and "abduction" (first described by Peirce, 1960, 1966; see also Burks, 1946; Fann, 1970; and Frankfurt, 1958) - as discussed at length below.
The systems approach leads to two important conclusions about machine intelligence (MI). First, Ml involves the ability to utilize existing knowledge structures and to invent new ones. Second, although the utilization and invention of classification schemes require the formation of hypotheses, the inference for formulating hypotheses which apply existing classification schemes are logically distinct from the inferences used in formulating hypotheses which invent new or revised classification schemes (see fig. 3.6). TYPE OF INFERENCE
Figure 3.6. - Systems graph for machine intelligence.
CAPABILITY
TASKS
These conclusions have implications for machine intelligence systems designed for autonomous deep space exploration. If classification schemes applicable to the Earth were complete and correct for all extraterrestrial bodies, then an autonomous system utilizing these schemes via analytic inferences alone could successfully complete the knowing process. However, it is probably true that at least some of the available classification schemes are either incomplete or incorrect in the extraterrestrial context and, in any case, the most prudent design philosophy for a space exploration system would be to assume that gaps do exist. Under the assumption that novelty will be encountered in space, an autonomous exploratory system may successfully complete the knowing process only if it can utilize prefor- mulated classification schemes and also invent new or revised ones, that is, only if it can make inferences of the inductive and abductive types in addition to inferences of the analytic type. 3.3.3 Patterns of Inference for Hypothesis Formation
Analytic, inductive, and abductive inferences will now be characterized in terms of the information inputs and outputs of each. An existence argument for abductive inference, which also establishes its centrality to scientific investigation, is offered, and the process involved in abduction is characterized in some detail. Finally, the requisite state of development for each of the three basic inferential types is contrasted with AI state of the art in the context of autonomous scientific investigation, the ultimate goal.
Analytic inferences are logical patterns by which existing scientific classification schemes (principles, laws, theories, and concepts) are applied to information about the events and processes of the world for the purpose of producing identifications and descriptions of these events and processes as well as predictions and explanations about them (Alexander, 1963; ?????, 1960; Hempel, 1965, 1966; P
