Temporal and Causal Inference

Goertzel, Ben; Iklé, Matthew; Goertzel, Izabela Freire; Heljakka, Ari

doi:10.1007/978-0-387-76872-4_14

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“…Syllogistic cases are an elementary concept of the fuzzy-syllogistic systems n S, for calculating truth ratios [8] of the moods algorithmically [9]. For three sets, 7 distinct spaces d i , i = [1,7] are possible, which can be easily identified in a Venn diagram (Table 1). There are in total j = 96 distinct combinations of the spaces [27], which constitute the universal set of syllogistic moods.…”

Section: Syllogistic-casesmentioning

confidence: 99%

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Fuzzy-Syllogistic Systems: A Generic Model for Approximate Reasoning

Kumova

2016

Trends in Applied Knowledge-Based Systems and Data Science

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The well known Aristotelian syllogistic system S consists of 256 moods. We have found earlier that 136 moods are distinct in terms of equal truth ratios that range in s = [0,1]. The truth ratio of a particular mood is calculated by relating the number of true and false syllogistic cases that the mood matches. The introduction of (n − 1) fuzzy existential quantifiers, extends the system to fuzzy-syllogistic systems n S, 1 < n, of which every fuzzy-syllogistic mood can be interpreted as a vague inference with a generic truth ratio, which is determined by its syllogistic structure. Here we introduce two new concepts, the relative truth ratio r s = [0,1] that is calculated from the cardinalities of the syllogistic cases of the mood and fuzzy-syllogistic ontology (FSO). We experimentally apply the fuzzy-syllogistic systems 2 S and 6 S as underlying logic of a FSO reasoner (FSR) and discuss sample cases for approximate reasoning.

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Section: Syllogistic-casesmentioning

confidence: 99%

“…A fuzzy-syllogistic ontology (FSO) extends the concept of ontology with the quantities that led to the ontological concepts. A FSO is usually generated probabilistically, but does not preserve any probabilities like probabilistic ontologies [14] or probabilistic logic networks [7] do. A FSO can be a fully connected and bidirectional graph.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy-Syllogistic Systems: A Generic Model for Approximate Reasoning

Kumova

2016

Trends in Applied Knowledge-Based Systems and Data Science

View full text Add to dashboard Cite

show abstract

Temporal and Causal Inference

Cited by 1 publication

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Fuzzy-Syllogistic Systems: A Generic Model for Approximate Reasoning

Fuzzy-Syllogistic Systems: A Generic Model for Approximate Reasoning

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