BRIAN: a basic regimen for intelligent analysis using networks
First Claim
1. A method for creative machine-learning by analogy, the method comprising the following steps:
- providing a network of component computer systems, each component comprises a plurality of sequential layers, each layer comprises a plurality of parallel segments, each segment comprises a processor and a memory store, each memory store is configured to store a domain-specific case base, wherein each case is defined by a situation-action pair;
stochastically transforming cases in each case base through automatic deterministic generalization and analogy when the corresponding processor is in a dream mode to create transformed cases, and selecting pairs of cases for generalization and analogy with a 3-2-1 skew during the transforming;
providing a user-entered situation, wherein the situations are sets of parametized natural language questions, and wherein each situation comprises a vertically-stacked, AND-operator-connected set of questions;
modifying the user-entered situation by expanding its contextual mnemonics;
searching each case base for cases and transformed cases that include contextual subsets of the modified, user-entered situation;
for a given case base, mapping the modified, user-entered situation to a matched action within the given case base, wherein the actions are sequences of parametized natural language statements, and wherein each action comprises a vertically-stacked sequence of action statements;
using the 3-2-1 skew to independently find candidate macro situations and macro actions from within the most-frequently-used (MFU) segments; and
using the 3-2-1 skew to iteratively replace macros within each segment with a corresponding macro definition when found during dream mode;
creating a new case comprising the user-entered situation and the matched action, wherein the 3-2-1 skew moves newly acquired cases to a logical head of a case base and progressively and stochastically selects from the logical head until the 3-2-1 skew uniformly selects from the entire case base; and
calculating a possibility for a fired action in a static domain by using the heuristic
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Abstract
A method for machine-learning by analogy, comprising: providing a network of component computer systems, each component having sequential layers, each layer having parallel segments, each segment comprises a processor and a memory store, each memory store is configured to store a domain-specific case base, which is defined by a situation-action pair; stochastically transforming cases in each case base through automatic deterministic generalization and analogy when the corresponding processor is in a dream mode to create transformed cases; providing a user-entered situation; modifying the user-entered situation by expanding its contextual mnemonics; searching each case base for cases and transformed cases that include contextual subsets of the modified, user-entered situation; for a given case base, mapping the modified, user-entered situation to a matched action within the given case base; creating a new case comprising the user-entered situation and the matched action.
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Citations
4 Claims
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1. A method for creative machine-learning by analogy, the method comprising the following steps:
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providing a network of component computer systems, each component comprises a plurality of sequential layers, each layer comprises a plurality of parallel segments, each segment comprises a processor and a memory store, each memory store is configured to store a domain-specific case base, wherein each case is defined by a situation-action pair; stochastically transforming cases in each case base through automatic deterministic generalization and analogy when the corresponding processor is in a dream mode to create transformed cases, and selecting pairs of cases for generalization and analogy with a 3-2-1 skew during the transforming; providing a user-entered situation, wherein the situations are sets of parametized natural language questions, and wherein each situation comprises a vertically-stacked, AND-operator-connected set of questions; modifying the user-entered situation by expanding its contextual mnemonics; searching each case base for cases and transformed cases that include contextual subsets of the modified, user-entered situation; for a given case base, mapping the modified, user-entered situation to a matched action within the given case base, wherein the actions are sequences of parametized natural language statements, and wherein each action comprises a vertically-stacked sequence of action statements; using the 3-2-1 skew to independently find candidate macro situations and macro actions from within the most-frequently-used (MFU) segments; and using the 3-2-1 skew to iteratively replace macros within each segment with a corresponding macro definition when found during dream mode; creating a new case comprising the user-entered situation and the matched action, wherein the 3-2-1 skew moves newly acquired cases to a logical head of a case base and progressively and stochastically selects from the logical head until the 3-2-1 skew uniformly selects from the entire case base; and calculating a possibility for a fired action in a static domain by using the heuristic - View Dependent Claims (2, 3, 4)
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Specification