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FIGURE 4-10 <Histogram/> discovery + RXML knowledge yield informed query.
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<Noise/>, it could request an algorithm from the <Authority/> to differentiate signals from noise. The <Authority/> could download a <Squelch/> object in response. A <Self/>-referential <Goal/> to <Minimize/> the computational resources could motivate the query, substituting <Squelch/> for <Histogram/>, replacing AML with an encapsulated skill. Expression 4-18 To <Optimize/> <Resources/> <Query/> an <Authority/> for a <Skill/> <Self> <Procedure> <De nition> A <List/> of <Goals/> in a <Plan/> </De nition> </ Procedure > <Optimize> <Domain> < Procedure /> </Domain> <Range> <Skill/> </Range> </Optimize> <Procedure> <Goal> <Optimize> <Sequence/> </Optimize> </Goal> <Procedure> <Query> <Authority/> <Skill> <Resource/> </Skill> </Query> </Procedure> </Procedure> </Self> The iCR downloads <Squelch/>, the optimized computing <Resource/> offered. The CRA incorporates explicit and detailed representations of <Space/>, <Time/>, and <RF/> to associate knowledge with <Scene/> to acquire skills and to share knowledge. The <Histogram/> discovery process applies to signal phase space, the complex plane as well as the frequency domain of the PSD. The two classes discoverable by the <Histogram/> would be the two states of a BPSK channel symbol, for example, with zero and radians the most commonly observed values of the BPSK phase plane. Researchers have published approaches to classifying relatively large collections of such channel symbols [114]. A library of <Signal-type/> functions may be structured into an ontological collection <Signal-types>. <Signal-type/> might express degree of belief in the class. PSD-related recognizers might estimate signal bandwidth and the number of peaks in the spectrum. Although a specialized algorithm may perform better than AML using <Histogram/>, AML will deal with previously unknown cases. Subsequently, an interference recognizer synthesized by a GA from samples of previously unknown interference, could outperform a preprogrammed recognizer [115]. General AML techniques like <Histogram/> raise a strategic question about the level of learning. How much of what should be learned or attempted How much should be preprogrammed What should the iCR ask of a cognitive network in a prayer cycle, versus introspection versus interaction Each of these questions implies multidisciplinary research issues identi ed and addressed but not yet solved.
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The Ontological RF <Histogram/>
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The <Self> assimilates <Histogram/> by description as an ontological primitive in terms of other primitives (Expression 4-19). Expression 4-19 Evolving Operational Ontology of the iCR <Self/>
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<Self> <Name /> <Owner> KTH </Owner> <iCR-platform> <RF-environment> <RF-capabilities> <Waveforms/> RF-sensors </RF-capabilities> <RF-knowledge> <Spatial-knowledge-DB/> </RF-Knowledge> </RF-environment> <User> <Current-user/> <Authorized-user/> <User-situation/> </User> <Autonomous-control> methods </Autonomous-control> <Environment> <Space-time-grounding/> </Environment> <Histogram> <Domain>Y[1 N] </Domain> <Memory/> <MIPS><Domain>Y[1 N] </Domain> 38*N </MIPS> <Goal> <Scene> <User>/> <RF>/>Learn(Y) </Goal> <Reinforcement> <Positive> <Learn> <Domain> <User>/> <Scene/> Map-of-the-World .jpg </Domain> <Align> Ocean <Earth> <Ocean/> </Earth> </Align> <Align> Land <Earth> <Land/> </Earth> </Align> </Learn> <Learn> <Domain/> <RF/> <Scene/> PSD </Domain> <Align> Signal <RF> <Signal/> </RF> </Align> <Align> Noise <RF> <Noise/> </RF> </Align> <Align> <Histogram> <PSD> <Signal/> <Noise/> </PSD> </Histogram> <Detect> <Signal/> </Detect> </Align> </Learn> </Positive> <Negative/> </Reinforcement> </Histogram> </iCR-platform> </Self> This internalization shows that <Histogram/> learned the semantic alignment of <User/> speech segments Ocean and Earth to prior <Self/> ontological primitives. Ocean refers to <User> <Speech> <wav> Ocean </wav> </Speech> </User>. <Histogram/> positive learning experiences are internalized in terms of the <Domain/> of learning in case the knowledge applies only to that <Scene/> or place and time. The <Histogram/> with the related <Interestingness/> detector is a general skill. From the ability to acquire <User/>, knowledge of oceans and land, it
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Reinforcement learning among iCR components.
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supports <User>-domain skills. From the ability to learn about <Signals/> and <Noise/>, it supports the <RF> domain. Learning also is a property of the <iCR-platform/>, a top-level capability of the <Self>, where <Histogram> constitutes a general capability to <Learn/>. <Alignment/> de nes equivalence classes for reactive responses, deliberative planning, and CBR. The RXML above expresses dynamic knowledge as well as repository knowledge. Real-time performance requires ef cient application of these associations.
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4.5 REINFORCEMENT, EXTENSION, AND CONSTRAINT DISCOVERY This section further develops AML for AACR with methods for re nement, extension, and validation of discoveries. 4.5.1 Reinforcement Learning
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The many types of reinforcement learning modify existing behavior by suppressing behavior that yields negative reinforcement and facilitating behavior that yields positive reinforcement. Figure 4-11 shows a ow of reinforcement learning among iCR cognition components. The iCR acquires experience through its sensors. In the CRA, the iCR remembers everything, constantly comparing new sensory stimuli to prior experience, identifying new stimuli, sensory primitives, and stimulus sequences via novelty detection. Hierarchical novelty detection realizes a hierarchical multidimensional novelty vector (a tensor) of newness of current experience. To perceive positive and negative reinforcements, the iCR recognizes and isolates from the <Scene/> speci c cues to actions, perceiving reinforcement via matching, binding, scoring, and annotation. Matching aligns current stimuli (sensory stimuli, perceived objects, and related abstractions) with stimulus memory. Binding associates speci c stimuli in the <Scene/> with related internalized stimulus experience-response sets that are abstractions of prior scenes. When identical items (stimuli or responses) are bound in a scene, they form conceptual anchors ( Islands of understanding [116]). Dis-
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