A Multiagent-Based Tool for the Simulation of Social Production and Management of Urban Ecosystems: A Case Study on San Jerónimo Vegetable Garden - Seville, Spain

Santos, Flávia Cardoso Pereira dos; Adamatti, Diana Francisca; Rodrigues, Henrique Donâncio N.; Dimuro, Glenda; Jeréz, Esteban de Manuel; Dimuro, Graçaliz Pereira

doi:10.18564/jasss.3128

Cited by 4 publications

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“…We are also aiming at applications in the context of hybrid BDI-fuzzy [59] 8 agent models, commonly used in social simulation [61,62], where the evaluation of social values and exchanges are of a qualitative, subjective, vague nature [63,64]. Overlap functions and grouping functions can be used for dealing with indifference and incomparability when reasoning on the agent's fuzzy belief base, where a kind of weak preference relation may be defined.…”

Section: Resultsmentioning

confidence: 99%

The law of O-conditionality for fuzzy implications constructed from overlap and grouping functions

Dimuro

Bedregal

Fernández

et al. 2019

International Journal of Approximate Reasoning

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Overlap and grouping functions are special kinds of non necessarily associative aggregation operators proposed for many applications, mainly when the associativity property is not strongly required. The classes of overlap and grouping functions are richer than the classes of t-norms and t-conorms, respectively, concerning some properties like idempotency, homogeneity, and, mainly, the self-closedness feature with respect to the convex sum and the aggregation by generalized composition of overlap/grouping functions. In previous works, we introduced some classes of fuzzy implications derived by overlap and/or grouping functions, namely, the residual implications R O -implications, the strong implications (G, N )-implications and the Quantum Logic implications QL-implications, for overlap functions O, grouping functions G and fuzzy negations N . Such implications do not necessarily satisfy certain properties, but only weaker versions of these properties, e.g., the exchange principle. However, in general, such properties are not demanded for many applications. In this paper, we analyze the so-called law of O-Conditionality, O(x, I(x, y)) ≤ y, for any fuzzy implication I and overlap function O, and, in particular, for R O -implications, (G, N )-implications, QL-implications and D-implications derived from tuples (O, G, N ), the latter also introduced in this paper. We also study the conditional antecedent boundary condition for such fuzzy implications, since we prove that this property, associated to the left ordering property, is important for the analysis of the O-Conditionality. We show that the use of overlap functions to implement de generalized Modus Ponens, as the scheme enabled by the law of O-Conditionality, provides more generality than the laws of T -conditionality and U -conditionality, for t-norms T and uninorms U , respectively. © 2018. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/ inference processes via the generalized Modus Ponens (GMP) and Modus Tollens (GMT), carried out via the Zadeh's Compositional Rule of Inference [5]. 1 The GMP, which is the focus of this paper, can be implemented by a scheme enabled by the functional inequality called the Law of T -Conditionality, which, for a t-norm T and a fuzzy implication I, is stated by:In fact, (TC) means x * T (x → y) ≤ y, which generalizes the Modus Ponens x ∧ T (x → y) y to the fuzzy context [7, 8].In the literature, the studies on the T -Conditionality have been done just for the three main families of fuzzy implications, namely, R-, (S, N )and QL-implications. 2 R-implications are generalizations to [0, 1] of Boolean implications defined by the identity given, for a universe set X, bywhere A, B ⊆ X, and the intersection is generalized by a t-norm. This class of implications is related to a residuation concept from the intuitionistic logic. On the other hand, (S, N )-implications are generalizations to [0, 1] of the Boolean material implication defined aswhere ∨ and ¬ are...

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Section: Resultsmentioning

confidence: 99%

The law of O-conditionality for fuzzy implications constructed from overlap and grouping functions

Dimuro

Bedregal

Fernández

et al. 2019

International Journal of Approximate Reasoning

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A Variable Dimensional Fuzzy Logic-Based Reputation Model for MAS

Rodrigues

Dimuro

Adamatti

2018

Multi-Agent Based Simulation XVIII

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Generalized interval-valued OWA operators with interval weights derived from interval-valued overlap functions

Bedregal

Bustince

Palmeira

et al. 2017

International Journal of Approximate Reasoning

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100

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In this work we extend to the interval-valued setting the notion of an overlap functions and we discuss a method which makes use of interval-valued overlap functions for constructing OWA operators with interval-valued weights. . Some properties of intervalvalued overlap functions and the derived interval-valued OWA operators are analysed. We specially focus on the homogeneity and migrativity properties.

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A Multiagent-Based Tool for the Simulation of Social Production and Management of Urban Ecosystems: A Case Study on San Jerónimo Vegetable Garden - Seville, Spain

Cited by 4 publications

References 0 publications

The law of O-conditionality for fuzzy implications constructed from overlap and grouping functions

The law of O-conditionality for fuzzy implications constructed from overlap and grouping functions

A Variable Dimensional Fuzzy Logic-Based Reputation Model for MAS

Generalized interval-valued OWA operators with interval weights derived from interval-valued overlap functions

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