DOI: 10.3897/jucs.124087

Authors: Michele Berti, Matheus Camilo da Silva, Sebastiano Saccani, Sylvio Barbon Junior

Abstract: Synthetic data generation is a promising alternative to traditional data anonymization, with Variational Autoencoders (VAEs) excelling at generating high-quality synthetic tabular datasets. However, VAE hyperparameter selection is often computationally expensive or suboptimal. We propose a meta-learning (MtL) method for hyperparameter recommendation, which achieves competitive performance to state-of-the-art Bayesian Optimization (BO) with median AUC values of 0.660 ± 0.038 (MtL) and 0.650 ± 0.041 (BO), showing no statistically significant difference. Notably, our approach reduces configuration time to under three minutes, compared to BO’s multi-hour requirement, while also enabling incremental improvements through new data integration. This combination of efficiency, adaptability, and performance establishes MtL as a practical solution for hyperparameter tuning in synthetic data generation.

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DOI: 10.3897/jucs.76563

Authors: Ashot Baghdasaryan, Hovhannes Bolibekyan

Abstract: There are three main problems for theorem proving with a standard cut-free system for the first order minimal logic. The first problem is the possibility of looping. Secondly, it might generate proofs which are permutations of each other. Finally, during the proof some choice should be made to decide which rules to apply and where to use them. New systems with history mechanisms were introduced for solving the looping problems of automated theorem provers in the first order minimal logic. In order to solve the rule selection problem, recurrent neural networks are deployed and they are used to determine which formula from the context should be used on further steps. As a result, it yields to the reduction of time during theorem proving.

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DOI: 10.3217/jucs-020-10-1459

Authors: Mohammed Algabri, Hassan Mathkour, Hedjar Ramdane, Mansour Alsulaiman, Khalid Al-Mutib

Abstract: An autonomous mobile robot operating in an unstructured environment must be able to learn with dynamic changes to that environment. Learning navigation and control of mobile robot in an unstructured environment is one of the most challenging problems. Fuzzy logic control is a useful tool in the field of navigation of mobile robot. In this research, we optimized a performance of fuzzy logic controller by evolutionary learning technique. Two proposed approaches have been designed and implemented: Fuzzy Logic Controller (FLC) and Genetic-Fuzzy Controller (GA-FLC). The Genetic Algorithm is used for automatically learning to tune the membership function parameters for mobile robot motion control. Moreover, the performance of these approaches are compared through simulation.

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DOI: 10.3217/jucs-016-01-0159

Authors: Li Yang, Yang Xu

Abstract: For the processing of decision making with uncertainty information, this paper establishes a decision model based on lattice-valued logic and researches the algorithm for extracting the maximum decision rules. Firstly, we further research the lattice-valued fuzzy concept lattice by combining the lattice implication algebra and classical concept lattice; secondly, we define the lattice-valued decision context as the equivalent form of decision information system and establish the single-target decision model and talk about some properties of the decision rules; finally, we give the calculating methods of decision rules with different decision values and the algorithm for extracting the maximum decision rules.

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DOI: 10.3217/jucs-016-01-0121

Authors: Özgür Kabak, Füsun Ülengin

Abstract: Several supply chain and production planning models in the literature assume the demands are fuzzy but most of them do not offer a specific technique to derive the fuzzy demands. In this study, we propose a methodology to obtain a fuzzy-demand forecast that is represented by a possibilistic distribution. The fuzzy-demand forecast is found by aggregating forecasts based on different sources; namely statistical forecasting methods and experts’ judgments. In the methodology, initially, the forecast derived from the statistical forecasting techniques and experts’ judgments are represented by triangular possibilistic distributions. Subsequently, those results are combined by using weights assigned to each of them. A new objective weighting approach is used to find the weights. The proposed methodology is illustrated by an example and a sensitivity analysis is provided.

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DOI: 10.3217/jucs-015-17-3231

Authors: Achraf Telmoudi, Lotfi Nabli, Radhi Hiri

Abstract: In this article a new Control-Supervisory architecture of Flexible Manufacturing Systems (FMS) is presented. We are interested particularly in construction and modelling of FMS robust control of flow-shop type to time constraints. Other than the control of production system, the goal is to observe and interpreted the robustness of resources and of manufacturing system. The P-time Petri Nets which is used for modeling of the time constraints. A methodology of construction of a robust control system generating the margins of passive and active robustness is elaborated. The redundancy of the robustness of the elementary parameters between passive and active leads us to define the ways ensuring the total robus tness of the system. To do so, a set of definitions lemmas and theorems are developed and affirmed by examples of applications.

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DOI: 10.3217/jucs-015-04-0877

Authors: Trong Duong, Geun-Sik Jo, Jason Jung, Ngoc Nguyen

Abstract: Most previous research on ontology integration has focused on similarity measure-ments between ontological entities, e.g., lexicons, instances, schemas and taxonomies, resulting in high computational costs of considering all possible pairs between two given ontologies. In this paper, we propose a novel approach to reducing computational complexity in ontology integration. Thereby, we address the importance and types of concepts, for priority matching anddirect matching between concepts, respectively. Identity-based similarity is computed, to avoid comparisons of all properties related to each concept, while matching between concepts. Theproblem of conflict in ontology integration has initially been explored on the instance-level and concept-level. This is useful to avoid many cases of mismatching.

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DOI: 10.3217/jucs-014-10-1678

Authors: Alfredo Rosado-Muñoz, Luis Gomez-Chova, Joan Francés

Abstract: This paper describes the development of an Intellectual Property (IP) core in VHDL able to implement a Multilayer Perceptron (MLP) artificial neural network (ANN) topology with up to 2 hidden layers, 128 neurons, and 31 inputs per neuron. Neural network models are usually developed by using programming languages, such as Matlab®. However, their implementation in configurable logic hardware requires the use of some other tools and hardware description languages, such as as VHDL. For easy migration, a Matlab Graphical User Interface (GUI) to automatically translate the ANN architecture to VHDL code has been developed. In addition, the use of an activation function based on fuzzy logic for the implementation of the MLP neural network simplifies the logic and improves the results. The environment was tested using a typical prediction problem, the Mackey-Glass series, where several ANN topologies were generated, tested and implemented in an FPGA. Results show the excellent agreement between the results provided by the software model and the hardware implementation.

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DOI: 10.3217/jucs-013-09-1300

Authors: Yasser Damavandi, Karim Mohammadi

Abstract: Evolvable Hardware (EHW) is a new concept that aims the application of evolutionary algorithms to hardware design. EHW can adapt itself to unknown environment based on features of the reconfigurable hardware. This paper presents outlines of the idea of using some EHW agents in a distributed system. These agents need to set up a self-organized communication to achieve the predesigned goal. The experiment that is demonstrated during the presentation, is to distribute a serial adder into two EHW parts, where good results has been shown in a co-evolutionary process.

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DOI: 10.3217/jucs-013-06-0701

Authors: Jacques Robin, Jairson Vitorino, Armin Wolf

Abstract: Most automated reasoning tasks with prac tical applications can be automatically reformulated into a constraint solving task. A constraint programming platform can thus act as a unique, underlying engine to be reused for mu ltiple automated reasoning tasks in intelligent agents and systems. We identify six key requirements for such platform: expressive task modeling language, rapid solving method custom ization and combination, adaptive solving method, user-friendly solution explanation, efficient execution, and seamless integration within larger systems and practical applications. We then propose a novel, model-driven, component and rule-based architecture for such a platform that better satisfies as a whole this set of requirements than those of currently available platforms.

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DOI: 10.3217/jucs-012-11-1679

Authors: Pascual Julián, Ginés Moreno, Jaime Penabad

Abstract: Multi-adjoint logic programming represents a very recent, extremely flexible attempt for introducing fuzzy logic into logic programming. In this setting, the execution of a goal w.r.t. a given program is done in two separate phases. During the operational one, admissible steps are systematically applied in a similar way to classical resolution steps in pure logic programming, thus returning a computed substitution together with an expression where all atoms have been exploited. This last expression is then interpreted under a given lattice during the so called interpretive phase, hence returning a value which represents the fuzzy component (truth degree) of the computed answer. On the other hand, unfolding is a well known transformation rule widely used in declarative programming for optimizing and specializing programs, among other applications. In essence, it is usually based on the application of operational steps on the body of program rules. The novelty of this paper consists in showing that this process can also be made in terms of interpretive steps. We present two strongly related kinds of unfolding (operational and interpretive), which, apart from exhibiting strong correctness properties (i.e. they preserve the semantics of computed substitutions and truth degrees) they are able to significantly simplify the two execution phases when solving goals.

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DOI: 10.3217/jucs-012-09-1087

Authors: Ágnes Achs

Abstract: In this paper we give a possible model for handling uncertain information. The concept of fuzzy knowledge-base will be defined as a quadruple of any background knowledge, defined by the proximity of predicates and terms; a deduction mechanism: a fuzzy Datalog program; a connecting algorithm, which connects the background knowledge with the program and a decoding set of the program, which help us to determine the uncertainty level of the results. Evaluation strategies will also be presented.

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DOI: 10.3217/jucs-010-11-1519

Authors: John Andrew Andrew Van Der Poll, Paula Kotzé, Willem Labuschagne

Abstract: A condensed specification of a multi-level marketing (MLM) enterprise which can be modelled by mathematical forests and trees is presented in Z. We thereafter identify a number of proof obligations that result from operations on the state space. Z is based on first-order logic and a strongly-typed fragment of Zermelo-Fraenkel set theory, hence the feasibility of using certain reasoning heuristics developed for proving theorems in set theory is investigated for discharging the identified proof obligations. Using the automated reasoner OTTER, we illustrate how these proof obligations from a real-life enterprise may successfully be discharged using a suite of well-chosen heuristics.

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DOI: 10.3217/jucs-009-07-0682

Authors: Eddie Moench, Mike Ullrich, Hans-Peter Schnurr, Jürgen Angele

Abstract: During the analysis of knowledge processes in enterprises it often turns out that simple access to existing enterprise knowledge which is covered in documents is not possible. To enable access to a companys document and data stocks Information Retrieval (IR) technologies play a central role. In the following we describe the underlying theory of the SemanticMiner system, including methods and technologies as well as continuing approaches to obtain Knowledge Retrieval (KR) by dint of semantic technologies.

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DOI: 10.3217/jucs-007-02-0124

Authors: Alessandro Armando, Silvio Ranise

Abstract: In this paper, we propose a generic mechanism for extending decision procedures by means of a lemma speculation mechanism. This problem is important in order to widen the scope of decision procedures incorporated in state-of-the-art verification systems. Soundness and termination of the extension schema are formally stated and proved. As a case study, we consider extensions of a decision procedure for the quantifier-free fragment of Presburger Arithmetic to significant fragments of non-linear arithmetic.

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DOI: 10.3217/jucs-006-09-0861

Authors: Stefan Ratschan

Abstract: When trying to solve quantified constraints (i.e., first-order formulas over the real numbers) exactly, one faces the following problems: First, constants coming from measurements are often only approximately given. Second, solving such constraints is in general undecidable and for special cases highly complex. Third, exact solutions are often extremely complicated symbolic expressions. In this paper we study how to do approximate computation instead of working on approximate inputs and producing approximate output. For this we show how quantiffied constraints can be viewed as expressions in heterogeneous algebra and study how to do uncertainty propagation there. Since set theory is a very fundamental approach for representing uncertainty, also here we represent uncertainty by sets. Our considerations result in a general framework for approximate computation that can be applied in various different domains.

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DOI: 10.3217/jucs-005-11-0743

Authors: Nathalie Chetcuti-Sperandio, Luis Fariñas del Cerro

Abstract: The Duration Calculus is an interval logic introduced for designing real-time systems. This calculus is able to capture important real-time problems like the specification of the behaviours of schedulers and classical examples like a gas burner. From a practical point of view an important challenge becomes to define automated proof procedures for this calculus. Since the propositional calculus is undecidable, we are interested then into isolating decidable fragments of this calculus. A first fragment was given and its decidability proved via regular languages. In this paper we isolate another fragment and we define a tableau method which gives a natural procedure to decide whether a given formula is satisfiable. This fragment is strong enough to embed Allen's Interval Algebra.

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DOI: 10.3217/jucs-005-07-0418

Authors: J. Wolff

Abstract: This article introduces the idea that probabilistic reasoning (PR) may be understood as information compression by multiple alignment, unification and search (ICMAUS). In this context, multiple alignment has a meaning which is similar to but distinct from its meaning in bio-informatics, while unification means a simple merging of matching patterns, a meaning which is related to but simpler than the meaning of that term in logic. A software model, SP61, has been developed for the discovery and formation of "good" multiple alignments, evaluated in terms of information compression. The model is described in outline. Using examples from the SP61 model, this article describes in outline how the ICMAUS framework can model various kinds of PR including: PR in best-match pattern recognition and information retrieval, one-step "deductive" and "abductive" PR, inheritance of attributes in a class hierarchy, chains of reasoning (probabilistic decision networks and decision trees, and PR with "rules" ), geometric analogy problems, nonmonotonic reasoning and reasoning with default values, modelling the function of a Bayesian network.

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DOI: 10.3217/jucs-005-03-0113

Authors: Gernot Stenz, Wolfgang Ahrendt, Bernhard Beckert

Abstract: Logic calculi, and Gentzen-type calculi in particular, can be categorised into two types: search-oriented and interaction-oriented calculi. Both these types have certain inherent characteristics stemming from the purpose for which they are designed. In this paper, we give a general characterisation of the two types and present two calculi that are typical representatives of their respective class. We introduce a method for transforming proofs in the search-oriented calculus into proofs in the interaction-oriented calculus, and we demonstrate that the difficulties arising with devising such a transformation do not pertain to the specific calculi we have chosen as examples but are general. We also give examples for the application of our transformation procedure.

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DOI: 10.3217/jucs-001-09-0658

Authors: Dongming Wang

Abstract: A zero decomposition algorithm is presented and used to devise a method for proving theorems automatically in differential geometry and mechanics. The method has been implemented and its practical efficiency is demonstrated by several non-trivial examples including Bertrand s theorem, Schell s theorem and Kepler-Newton s laws.

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