Probabilistic logic network (original) (raw)
A probabilistic logic network (PLN) is a conceptual, mathematical, and computational approach to uncertain inference; inspired by logic programming, but using probabilities in place of crisp (true/false) truth values, and fractional uncertainty in place of crisp known/unknown values. In order to carry out effective reasoning in real-world circumstances, artificial intelligence software must robustly handle uncertainty. However, previous approaches to uncertain inference do not have the breadth of scope required to provide an integrated treatment of the disparate forms of cognitively critical uncertainty as they manifest themselves within the various forms of pragmatic inference. Going beyond prior probabilistic approaches to uncertain inference, PLN is able to encompass within uncertain lo
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| dbo:abstract | A probabilistic logic network (PLN) is a conceptual, mathematical, and computational approach to uncertain inference; inspired by logic programming, but using probabilities in place of crisp (true/false) truth values, and fractional uncertainty in place of crisp known/unknown values. In order to carry out effective reasoning in real-world circumstances, artificial intelligence software must robustly handle uncertainty. However, previous approaches to uncertain inference do not have the breadth of scope required to provide an integrated treatment of the disparate forms of cognitively critical uncertainty as they manifest themselves within the various forms of pragmatic inference. Going beyond prior probabilistic approaches to uncertain inference, PLN is able to encompass within uncertain logic such ideas as induction, abduction, analogy, fuzziness and speculation, and reasoning about time and causality. PLN was developed by Ben Goertzel, Matt Ikle, Izabela Lyon Freire Goertzel, and Ari Heljakka for use as a cognitive algorithm used by MindAgents within the OpenCog Core. PLN was developed originally for use within the Novamente Cognition Engine. (en) A rede lógica probabilística (RLP) é uma abordagem conceitual, matemática e computacional para inferência incerta; inspirado em lógica de programação, mas usando probabilidade em lugar de valorações verdade , e incerteza fracionária no lugar de valores (conhecido / desconhecido) . A fim de realizar um raciocínio eficaz em circunstâncias do mundo real, O software de inteligência artificial deve lidar com a incerteza. No entanto, as abordagens anteriores a inferência incerta não tem o escopo necessário para fornecer cognitivamente um tratamento integrado das formas díspares de incerteza, como eles se manifestam dentro das várias formas de inferência pragmáticas. Indo além das abordagens probabilísticas anteriores a inferência incerta, RLP é capaz de abarcar dentro da lógica tais idéias incertas como indução, abdução, analogia, imprecisão e especulação, e do raciocinar sobre o tempo e causalidade. RLP foi desenvolvido por Ben Goertzel, Matt Ikle, Izabela Lyon Freire Goertzel e Ari Heljakka para uso como um algoritmo cognitivo utilizado por MindAgents (empresa) dentro do núcleo OpenCog. RLP foi desenvolvido originalmente para uso dentro do Novamente Cognição Engine (empresa). (pt) |
| dbo:wikiPageExternalLink | https://archive.org/details/probabilisticlog00goer https://archive.org/details/probabilisticlog00goer/page/n330 https://web.archive.org/web/20090907133322/http:/www.opencog.org/wiki/Probabilistic_Logic_Networks |
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| rdfs:comment | A probabilistic logic network (PLN) is a conceptual, mathematical, and computational approach to uncertain inference; inspired by logic programming, but using probabilities in place of crisp (true/false) truth values, and fractional uncertainty in place of crisp known/unknown values. In order to carry out effective reasoning in real-world circumstances, artificial intelligence software must robustly handle uncertainty. However, previous approaches to uncertain inference do not have the breadth of scope required to provide an integrated treatment of the disparate forms of cognitively critical uncertainty as they manifest themselves within the various forms of pragmatic inference. Going beyond prior probabilistic approaches to uncertain inference, PLN is able to encompass within uncertain lo (en) A rede lógica probabilística (RLP) é uma abordagem conceitual, matemática e computacional para inferência incerta; inspirado em lógica de programação, mas usando probabilidade em lugar de valorações verdade , e incerteza fracionária no lugar de valores (conhecido / desconhecido) . A fim de realizar um raciocínio eficaz em circunstâncias do mundo real, O software de inteligência artificial deve lidar com a incerteza. No entanto, as abordagens anteriores a inferência incerta não tem o escopo necessário para fornecer cognitivamente um tratamento integrado das formas díspares de incerteza, como eles se manifestam dentro das várias formas de inferência pragmáticas. Indo além das abordagens probabilísticas anteriores a inferência incerta, RLP é capaz de abarcar dentro da lógica tais idéias incer (pt) |
| rdfs:label | Probabilistic logic network (en) Rede Lógica Probabilística (pt) |
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