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EFTA00624128_sub_001 - EFTA00624128_100
nference Control 277 36.1 Introduction 277 36.2 High-Level Control Mechanisms 277 36.2.1 The Need for Adaptive Inference Control 278 36.3 Inference Control in PLN 279 36.3.1 Representing PLN Rules as GroundedSchemallodes 279 36.3.2 Recording Executed PLN Inferences in the Atomspace 279 36.3.3 A
EFTA00624128_sub_003 - EFTA00624128_300
hosen an alternate strategy for PLN. We have one inference control scheme, which is quite simple, but which relies partially on EFTA00624425 36.3 Inference Control in PLN 279 structures coming from outside PLN proper. The requisite variety of inference control strategies is provided by variety in the non-PLN
EFTA00624128_sub_004 - EFTA00624128_400
36.3 Inference Control in PLN 281 36.3.4 Basic Forward and Backward Inference Steps The choice of an inference step, at the microscopic level, may be done in a number of
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PredicateNodes
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Combo
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OpenCog
OrganizationArtificial intelligence research project
MindAgents
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Adaptive
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Marc Rich
PersonAmerican commodities trader (1934–2013)
CogPrime
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SX SY
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Bayes
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Bridging
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Entropy
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Statics
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Schemallode
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Multisensory Integration
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The Importance of Semantic Feedback
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Mining Subtrees
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IRC Learning
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Spontaneous Learning
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