sensory cues available in the environment, and produces and subsequently tests interpretations about what the world looks like. The percept is the phenomenological result of the interpretation. In this view, perception is a subconscious, "hard-wired" constructive process involving the formation of a hypothesis, a test of that hypothesis, and a consequent decision as to whether the hypothesis accurately encompasses the sensory information. The literature of psychology contains much evidence to support such a description as a reasonable characterization of human perception (Neisser, 1967; Rock, 1975), and the AI community has accepted, in principle, a similar view (Arden, 1980). However, the techniques and operations typically employed to achieve computer pattern-sensing generally fail to properly incorporate the notion of perception and recognition as active constructive processes.
Cognitive psychological theory has largely emphasized two general approaches in characterizing pattern recognition schemes - template matching and feature extraction theory. Each has a different focus of attention with respect to the three major aspects of recognition called "description," "representation," and "matching" (of new images against stored representations).
Template matching theorists propose that a literal copy of perceived stimuli stored in memory is matched against new incoming stimuli. Although this view has been criticized as too simplistic and naive (Klatsky, 1975; Neisser, 1967), updated versions of the hypothesis still hold sway. For instance, one modification retains the notion that literal copies are stored in memory but suggests that new percepts are "normalized" before matching. In this view, some precomparison processing takes place in which edges are smoothed out, oriented in the appropriate plane, and centered with respect to the surrounding field. In addition, image context helps in the normalizing process by reducing the number of possible patterns the stimulus might match (Klatsky, 1975). In the field of AI technology, the Massively Parallel Processor or "MPP" (an imaging system currently under development at Goddard Space Flight Center) uses visual data-handling techniques with characteristics LEARNING ? CAPACITY TO FORM UNIVERSALS ASSOCIATED WITH INFORMATION PATTERNS PRESENT IN THE ENVIRONMENT ? SUBSUMES A CERTAIN LEVEL OF HYPOTHESIS FORMATION AND CONFIRMATION ? NEW UNIVERSALS MAY BE FORMED "ON PROBATION" (i.e.. AS HYPOTHESIS) WITH PERMANENT ADOPTION ONLY FOLLOWING "CONFIRMATION" SUCH AS THROUGH "REINFORCEMENT" OR "REHEARSAL" MEMORY ? CAPACITY TO MAINTAIN UNIVERSALS INDEFINITELY ? LONG-TERM RECALL AIDED BY SOME RECIRCULATING OR REPLICATING PROCESS ADVANCED MACHINE INTELLIGENCE ? A HIGHLY INTEGRATED MIX THAT INCLUDES ALL OF THE ABOVE ? PREFERABLY THE INTEGRATION IS EMBODIED IN A SINGLE FUNCTION OR PROCESS ? AUTONOMOUS MODE OF PROCESSING RECOGNITION ? CAPACITY TO IDENTIFY, OR CLASSIFY, INFORMATION PATTERNS PRESENT IN THE ENVIRONMENT ON THE BASIS OF PRE-ESTABLISHED UNIVERSALS
Figure 3.10.- Adaptive machine intelligence for advanced space exploration.
