Finally, it is instructive to contrast state-of-the-art AI treatments of analytic, inductive and abductive inference with the optimal treatment required to achieve working machine intelligence systems with highly advanced capabilities. (See also chapter 6.) First, with respect to analytic inference, current AI research is not addressing the central problem of supporting the detailed knowledge in the classification schemes with fundamental models. Second, although some preliminary work has been done in mechanizing inductive inference (Hajek and Havranek, 1978), this work also has not adequately addressed the basic problem of connecting fundamental models to the generalizing process. Third, only tentative steps have been taken in the development of mechanized abductive inference (Hayes- Roth, 1980), and even these efforts are not grounded on a mature theory of abduction for machine intelligence.
3.3.4 The Inference Needs of Autonomous Space Exploration Systems
For an autonomous space exploration system to undertake knowing and learning tasks, it must be capable of mechanically formulating hypotheses using all three of the distinct logical patterns of inference, as follows:
? Analytic inference - needed by the explorer system to process raw data and to identify, describe, predict, and explain events and processes in terms of existing knowledge structures.
? Inductive inference - necessary to formulate quantitative generalizations and to abstract the common features of events and processes, both of which amount to the invention of new knowledge structures.
? Abductive inference - needed by the system to formulate hypotheses about new scientific laws, theories, models, concepts, principles, and classifications. The formulation of this type of hypothesis is the key to the ability to invent a full range of novel knowledge structures required for successful and comprehensive scientific investigation.
Although the three patterns of inferences are distinct and independent, they can be ordered by difficulty and complexity. This ordering is the same as comparing their ability to support the invention of new knowledge structures. Analytic inference is at the low end. An automated system that performs only this type of inference could probably undertake reconnaissance missions successfully. Next is inductive inference. A machine system able to perform this type as well as analytic inference could successfully undertake missions combining reconnaissance and exploration, provided the planet explored is represented well enough by the fundamental models with which the system would be preprogrammed. But if the processes underlying the phenomena of the new world are not well- represented by the fundamental models, automated combined reconnaissance and exploration missions will require abductive inference. Abduction is at the top of both orderings. It is the most difficult as well as the heart of knowledge invention. An automated system capable of abductive reasoning could successfully undertake missions combining reconnaissance, exploration, and intensive study.
3.3.5 Cognitive Processes in Intelligent Activity
One significant technology driver in fully autonomous space exploration is the capacity for learning and the need for adaptive forms of machine intelligence in future space missions (fig. 3.10). However, a review of the literature (Arden, 1980; Boden, 1977; Raphael, 1976) and personal consultations with experts in the field of AI indicate that theoretical and technological research in this area has not seriously been pursued for many years.
For this reason it is useful to approach the goal of adaptive intelligence from the perspective of a related field of study in which it has already received considerable attention: Cognitive psychology. Clearly, descriptions of human thought processes leading to intelligent behavior cannot serve as a direct template for machine intelligence programming - it is a recognized philosophy of the AI community that software need not exactly mimic human processes to achieve an intelligent outcome. Rather, the objective is to describe some aspects of human cognition in hopes of bridging the gap between present limitations in the AI field and the level of machine intelligence likely to be needed in future space exploration missions.
Perception and pattern recognition. The most fundamental kinds of intelligence are perception and the related activity of pattern recognition. Each has been the subject of much study by cognitive and physiological psychologists. For example, evidence from Sperling (1960) suggests that perceptual input is held briefly in a sensory buffer register, thus, permitting the activation of control processes to encode the data in terms of meaningful categories. Stimuli presented to the human sensorium arrive in conscious awareness first as some perceptual-level description, then later with some useful label attached. Exactly how these processes work remains unknown, in part because perception occurs below the subject's level of awareness. Progress to date provides only partially integrated theories of perceptual data handling, yet these are sufficiently well- developed to deserve a brief review in the context of the present study.
A definition of perception at the descriptive level, popular in the psychological literature, holds that sensory processing is essentially inferential or interpretive, based on raw
