Probabilistic Logic Networks

Goertzel, Ben; Pennachin, Cassio; Geisweiller, Nil

doi:10.1007/978-0-387-76872-4

Cited by 34 publications

(3 citation statements)

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“…In terms of demonstrated practical utility, PLN has not yet confronted any really ambitious AGI-type problems, but it has shown itself capable of simple practical problem-solving in areas such as virtual agent control and natural language based scientific reasoning [GIGH08]. The current PLN implementation within CogPrime can be used to learn to play fetch or tag, draw analogies based on observed objects, or figure out how to carry out tasks like finding a cat.…”

Section: Why Is Pln a Good Idea?mentioning

confidence: 99%

“…Prototype systems [Goe10b,LGK+12] have also been written mapping the output of RelEx into even more abstract semantic form, consistent with the semantics of the Probabilistic Logic Networks [GIGH08] formalism as implemented in CogPrime. One may view semantic relationships (including semantic relationships close to the syntax level, which we may call "syntactico-semantic" relationships) as ensuing from syntactic relationships, via a similar but separate learning process to the one proposed above.…”

Section: Learning Semanticsmentioning

confidence: 99%

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Engineering General Intelligence, Part 2

Goertzel¹,

Pennachin²,

Geisweiller³

2014

Atlantis Thinking Machines

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This series publishes books resulting from theoretical research on and reproductions of general Artificial Intelligence (AI). The book series focuses on the establishment of new theories and paradigms in AI. At the same time, the series aims at exploring multiple scientific angles and methodologies, including results from research in cognitive science, neuroscience, theoretical and experimental AI, biology and from innovative interdisciplinary methodologies.For more information on this series and our other book series, please visit our website at: www.atlantis-press.com/publications/books AMSTERDAM-PARIS-BEIJING ATLANTIS PRESS Atlantis Press

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Section: Why Is Pln a Good Idea?mentioning

confidence: 99%

Section: Learning Semanticsmentioning

confidence: 99%

Engineering General Intelligence, Part 2

Goertzel¹,

Pennachin²,

Geisweiller³

2014

Atlantis Thinking Machines

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“…OpenCog's primary tool for handling declarative knowledge is an uncertain inference framework called Probabilistic Logic Networks (PLN). The complexities of PLN are the topic of a lengthy technical monograph [12]. A key point to note is that, as a logic, PLN is broadly integrative: it combines certain term logic rules with more standard predicate logic rules, and utilizes both fuzzy truth values and a variant of imprecise probabilities called indefinite probabilities.…”

Section: Destin and Opencogmentioning

confidence: 99%

Integrating Deep Learning Based Perception with Probabilistic Logic via Frequent Pattern Mining

Goertzel

Sanders²,

O'Neill

2013

Artificial General Intelligence

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Abstract. The bridging of the gap between 1) subsymbolic pattern recognition and learning algorithms and 2) symbolic reasoning algorithms, has been a major issue for AI since the early days of the field. One class of approaches involves integrating subsymbolic and symbolic systems, but this raises the question of how to effectively translate between the very different languages involved. In the approach described here, a frequent subtree mining algorithm is used to identify recurrent patterns in the state of a hierarchical deep learning system (DeSTIN) that is exposed to visual stimuli. The relationships between state-subtrees and percepts are then input to a probabilistic logic system (OpenCog's Probabilistic Logic Networks), which conducts uncertain inferences using them as axioms. The core conceptual idea is to use patterns in the states inferred by a perceptual hierarchy, as inputs to an uncertain logic system. Simple illustrative examples are presented based on the presentation of images of typed letters to DeSTIN. This work forms a component of a larger project to integrate perceptual, motoric and cognitive processing within the integrative OpenCog cognitive architecture.

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An Inferential Approach to Mining Surprising Patterns in Hypergraphs

Geisweiller¹,

Goertzel²

2019

Artificial General Intelligence

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Probabilistic Logic Networks

Cited by 34 publications

References 0 publications

Engineering General Intelligence, Part 2

Engineering General Intelligence, Part 2

Integrating Deep Learning Based Perception with Probabilistic Logic via Frequent Pattern Mining

An Inferential Approach to Mining Surprising Patterns in Hypergraphs

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