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FIGURE 2-4 CRA represents the iCR s Self, User, and RF mutually grounded in a scene.
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or active in the RF environment as perceived through the <RF/> inference hierarchy. <Self/>, <RF/>, and <User/> are grounded in space time at the Scene level of the CRA hierarchy. 2.5.2 Cognitive Processes
In this CRA, the iCR reasons over the <RF-environment/>, the <User/>, and the <Self/> with roles summarized as follows: Core Perception Action Process in the CRA 1. Sensing (a) <RF/> physical and protocol stacks (b) The local visual scene (c) The local acoustic scene (d) Other sensing modes such as temperature and acceleration 2. Perceiving (a) The <Self/> in Space Time RF (b) The <User/> in the acoustic visual Space Time Scene (c) Other Objects contributing to the Scene 3. Orienting (a) Detecting known and new patterns of stimuli (b) Reacting immediately (if necessary) 4. Planning (a) Generating alternative response plans with associated actions (b) Assigning QoI metrics to plans 5. Decision Making (a) Choosing among alternative actions based on QoI value (b) Forcing choices within time and other resources 6. Acting in both (a) RF and (b) Physical domains (e.g., speech synthesis, composing email) 7. Learning (a) Learning by being told (b) Assimilating positive and negative reinforcement This is the sequence of perception action in the CRA, ontologically referred to as the <Cognition-cycle/>, although a strict time ordering of these functions is not needed and may be counterproductive. The CRA, then, rests on the following pillars: 1. The <Self/> in the <Universe/> described in formal semantics (e.g., RXML).
ARCHITECTURE
2. The <iCR-platform/>, with SDR, sensors, perception, memory, and computational resources needed to ground the <Scene/> to experience. 3. AML illustrated by serModel acquisition and use. 4. The seven core perception action capabilities of iCR organized into a cognitive process. 5. A comprehensive inference hierarchy of abstractions that integrate perceptions, enabling well informed responsive information actions that enhance QoI for the <User/>. This text develops one candidate CRA that includes these pillars in which the iCR remembers everything and constantly acquires incremental knowledge by resolving newly acquired experience against its experience base. CR1 implements the representation hierarchy, the seven core perception action capabilities, a sequential cognition cycle, and continuous learning via ubiquitous CBR. CR1 has many limitations, but it illustrates the architecture pillars. Industry may de ne more than one CRA. If such a CRA accommodates evolution to iCR learning from experience, that CRA has the full potential of the iCR vision. Such a CRA advances the goal of mass customization but presents challenges of self-referential loops, QoI semantics, and adherence to social norms including privacy. 2.5.2.1 Self-Awareness Without the G del Flaw Since radio communications are isochronous, CRA RF and network-oriented behavior must be isochronous, with actions that occur on temporal boundaries precisely de ned by radio protocols, for example, 10 millisecond code division multiple access (CDMA) frames. Thus, the cognition cycle must be deterministic with respect to the realization and control of radio resources. Similarly, its perception of and interactions with the <User/> must be isochronous on temporal boundaries de ned by the user s expectations, typically seconds to minutes, with detailed expectations learned per-user. Thus, the cognition cycle for the user also must have deterministic bounds. Such isochronous behavior may be obtained from bounded-recursive functions computer programs with iteration, but not with recursion or with Until or While loops [142]. The prohibition on such loops precludes resolution theorem proving (one method for reasoning with logic statements). At rst, this seems excessively restrictive, but radio and user interaction timetables preclude unbounded inference, so the constraint formalizes practical engineering properties of market-worthy systems. Bounded recursion allows incremental learning of <User/> preferences, <RF/> procedures, and serModels by incremental extension of prior knowledge. 2.5.2.2 Mass Customization The introduction into markets of CRs that learn substantial new behaviors from their users will usher in an era of mass customization unlike anything previously experienced. Think of it. Your CR isn t a xed-function information system. It is a clean sheet of paper. Unlike a laptop-style clean sheet of