OpenNARS for Applications v0.8.7
This version consists of updates and additions for v0.8.6:
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Improved concept usefulness, increasing significantly the system's ability to learn and remember declarative knowledge.
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Missing NAL-3 decomposition rules added.
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More restricted nesting of terms for compound term formation.
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Var introduction slightly improved and left to sensorimotor inference.
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Support for questions with future/past tense.
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Vision channel can be chained with other input channels, for instance with english_to_narsese.py
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Concept usefulness filter added which allows to export the highest-useful concepts and their knowledge to Narsese files allowing for re-import. (see https://github.com/opennars/OpenNARS-for-Applications/wiki/Misc-Scripts)
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Derivation filter script added. (see https://github.com/opennars/OpenNARS-for-Applications/wiki/Misc-Scripts)
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Graph visualization and export to NetworkX graph. (see https://github.com/opennars/OpenNARS-for-Applications/wiki/Misc-Scripts)
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New more flexible English to Narsese converter with basic grammar learning ability and all innate knowledge specified in a declarative manner.
- Multiple new examples showing some of the system's capabilities which weren't shown before.
Alternative branches which are kept updated with master:
SkipEventsFIFO: Allows FIFO to skip events when building sequences. (higher noise tolerance)
MSC2: Sensorimotor reasoning only, see https://github.com/opennars/OpenNARS-for-Applications/tree/MSC2
Curiosity: Motorbabbling dependent on confidence of applicable hypotheses, making it prefer sampling the operations which consequences are less known in current context.
NegGoals: support for negative goals to inhibit decisions (like necessary in https://gist.github.com/patham9/47cfd750488a48c57259d049073c5280 )
QLearner: for comparison of Q-Learning with ONA.
More to come, potentially.