Autor:
Riaza Valverde, José Antonio

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E-mails conocidos
JoseAntonio.Riaza@uclm.es
JoseAntonio.Riaza@uclm.es
JoseAntonio.Riaza@uclm.es
joseantonio.riaza@alu.uclm.es
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Apellidos
Riaza Valverde
Nombre de pila
José Antonio
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Riaza Valverde, Jose Antonio
Afiliaciones conocidas
University of Castilla-La Mancha, Spain
Páginas web conocidas
http://jariaza.es
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2018 - 201922020 - 20233
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Mostrando 1 - 5 de 5
  • Miniatura
    Artículo
    Web development with Tau Prolog
    Riaza Valverde, José Antonio. Actas de las XXI Jornadas de Programación y Lenguajes (PROLE 2022), 2022-09-05.
    Tau Prolog is a client-side Prolog interpreter fully implemented in JavaScript, which aims at implementing ISO Prolog Standard. Tau Prolog has been developed to be used with either Node.js or a browser seamlessly, and therefore, it has been developed following a non-blocking, callback-based approach to avoid blocking web browsers. Taking the best from JavaScript and Prolog, Tau Prolog allows the programmer to handle browser events and manipulate the Document Object Model (DOM) of a web using Prolog predicates. In this paper we describe the main packages of Tau Prolog for interacting with the Web, and we present its programming environment.
  • Miniatura
    Artículo
    Seeking a Safe and Efficient Similarity-based Unfolding Rule
    Julián Iranzo, Pascual; Moreno, Ginés; Riaza Valverde, José Antonio. Actas de las XX Jornadas de Programación y Lenguajes (PROLE 2021), 2021-09-22.
    The unfolding transformation has been widely used in many declarative frameworks for improving the efficiency of programs. Inspired by our previous experiences in fuzzy logic languages not dealing with similarity relations, in this work we try to adapt such operation to the so-called FASILL language (acronym of "Fuzzy Aggregators and Similarity Into a Logic Language") which has been developed in our research group for coping with implicit/explicit truth degree annotations, a great variety of connectives and unification by similarity. The traditional unfolding transformation is based on the application of unifiers on the heads and computational steps on the bodies of program rules. However, when considering similarity relations, the premature generation and application of weak (similarity-based) unifers at unfolding time could destroy the correctness of the transformation. In this paper we study how to avoid this risk by compiling what we call similarity constraints on transformed rules, whose further evaluation is delayed at running time. Moreover, our technique minimizes the size and number of occurrences of such constructs in transformed programs to gain efficiency while preserving semantics.
  • Miniatura
    Artículo
    On the Safe Execution of Symbolic Similarity-based Fuzzy Logic Programs
    Moreno, Ginés; Riaza Valverde, José Antonio. Actas de las XXII Jornadas sobre Programación y Lenguajes (PROLE 2023), 2023-09-12.
    Introducing “Fuzzy Aggregators and Similarity Into a Logic Language” leads to FASILL, whose symbolic extension (called sFASILL) is able to manage both concrete and unknown (symbolic constants) truth degrees, similarity annotations and fuzzy connectives. The symbolic execution of sFASILL programs helps to guess the impact of such unknown symbols on the outputs before instantiating them with concrete values in program rules and similarity relations. In this paper we reason about the constraints that symbolic substitutions must satisfy in order to preserve the set of fuzzy computed answer obtained at execution time before or after instantiating a given sFASILL program. Based on such safeness conditions, we have formally proved two theoretical results with different levels of generality/practicability for correctly executing symbolic sFASILL and instantiated FASILL programs.
  • Miniatura
    Artículo
    Tuning Neural Networks in a Fuzzy Logic Programming Environment
    Moreno, Ginés; Pérez, Jesús; Riaza Valverde, José Antonio. Actas de las XIX Jornadas de Programación y Lenguajes (PROLE 2019), 2019-09-02.
    Wide datasets are usually used for training and validating neural networks, which can be later tuned in order to correct their behaviors according to a few number of test cases proposed by users. In this paper we show how the FLOPER system developed in our research group is able to perform this last task after coding a neural network with a fuzzy logic language where program rules extend the classical notion of clause by including on their bodies both fuzzy connectives (useful for modeling activation functions of neurons) and truth degrees (associated to weights and bias in neural networks). We present an online tool which helps to select such operators and values in an automatic way, accomplishing with our recent technique for tuning this kind of fuzzy programs. Moreover, we provide some experimental results revealing that our tool generates the choices that better fit user's preferences in a very efficient way, and producing relevant improvements on tuned neural networks.
  • Miniatura
    Artículo
    An Online Tool for Unfolding Symbolic Fuzzy Logic Programs (Demostración)
    Moreno, Ginés; Riaza Valverde, José Antonio. Actas de las XVIII Jornadas de Programación y Lenguajes (PROLE 2018), 2018-09-17.
    In many declarative frameworks, unfolding is a very well-known semantics-preserving transformation technique based on the application of computational steps on the bodies of program rules for improving efficiency. In this paper we describe an online tool which allows us to unfold a symbolic extension of a modern fuzzy logic language where program rules can embed concrete and/or symbolic fuzzy connectives and truth degrees on their bodies. The system offers a comfortable interaction with users for unfolding symbolic programs and it also provides useful options to navigate along the sequence of unfolded programs. Finally, the symbolic unfolding transformation is connected with some fuzzy tuning techniques that we previously implemented on the same tool.