On Ruspini’s Models of Similarity-Based Approximate Reasoning

Esteva, Francesc; Godo, Lluı́s; Rodríguez, Ricardo Óscar; Vetterlein, Thomas

doi:10.1007/978-3-030-50146-4_1

Cited by 2 publications

(2 citation statements)

References 16 publications

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“…Similarity-based reasoning systems [5,7,8,18,22,23] belong to very interesting logically motivated inference systems with a strong background in fuzzy approximate reasoning. Their structure is slightly more flexible than, say, the more standard fuzzy relational ones, where a single fuzzy relation gathers all knowledge base information.…”

Section: Similarity-based Reasoningmentioning

confidence: 99%

Similarity-based Reasoning from the Perspective of Extensionality

Dvořák¹,

Jayaram²,

Štěpnička³

2021

Atlantis Studies in Uncertainty Modelling

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Similarity-based reasoning systems consider the notion of similarity as crucial for their motivation and the whole design. However, similarity can be seen not only as a general notion but also as a particular family of binary fuzzy relations modeling a fuzzy equality. Moreover, these relations are tightly connected to another crucial notion -to the extensionality of fuzzy sets. This contribution focuses on this natural bridge and studies similarity-based reasoning fuzzy inference systems from the point of view of extensionality. Robustness of these systems is the first property that is considered in the newly setup framework. However, the potential impact and topics of interest are much wider.

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Section: Similarity-based Reasoningmentioning

confidence: 99%

Similarity-based Reasoning from the Perspective of Extensionality

Dvořák¹,

Jayaram²,

Štěpnička³

2021

Atlantis Studies in Uncertainty Modelling

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“…Actually, what people do in case of incomplete knowledge is to somehow measure the similarity between known/familiar situations and unknown/unfamiliar situations [16]. Several cognitive tasks, such as learning and interpolation require the concept of similarity to be performed [9]. There exists a vast literature on similarity measures, with many proposal arising in the field of machine learning [7].…”

Section: Introduction and Related Workmentioning

confidence: 99%

Learning to Classify Logical Formulas Based on Their Semantic Similarity

Ballout

Pereira²,

Tettamanzi³

2022

PRIMA 2022: Principles and Practice of Multi-Agent Systems

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An important task in logic, given a formula and a knowledge base which represents what an agent knows of the current state of the world, is to be able to guess the truth value of the formula. Logic reasoners are designed to perform inferences, that is, to decide whether a formula is a logical consequence of the knowledge base, which is stronger than that and can be intractable in some cases. In addition, under the open-world assumption, it may turn out impossible to infer a formula or its negation. In many practical situations, however, when an agent has to make a decision, it is acceptable to resort to heuristic methods to determine the probable veracity or falsehood of a formula, even in the absence of a guarantee of correctness, to avoid blocking the decisionmaking process and move forward. This is why we propose a method to train a classification model based on available knowledge in order to be able of accurately guessing whether an arbitrary, unseen formula is true or false. Our method exploits a kernel representation of logical formulas based on a model-theoretic measure of semantic similarity. The results of experiments show that the proposed method is highly effective and accurate.

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On Ruspini’s Models of Similarity-Based Approximate Reasoning

Cited by 2 publications

References 16 publications

Similarity-based Reasoning from the Perspective of Extensionality

Similarity-based Reasoning from the Perspective of Extensionality

Learning to Classify Logical Formulas Based on Their Semantic Similarity

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