In this paper we reexamine the place and role of stable model semantics in logic programming and contrast it with a least Herbrand model approach to Horn programs. We demonstrate that inherent features of stable model semantics naturally lead to a logic programming system that offers an interesting alternative to more traditional logic programming styles of Horn logic programming, stratified logic programming and logic programming with well-founded semantics. The proposed approach is based on the interpretation of program clauses as constraints. In this setting, a program does not describe a single intended model, but a family of its stable models. These stable models encode solutions to the constraint satisfaction problem described by the program. Our approach imposes restrictions on the syntax of logic programs. In particular, function symbols are eliminated from the language. We argue that the resulting logic programming system is well-attuned to problems in the class NP, has a well-defined domain of applications, and an emerging methodology of programming. We point out that what makes the whole approach viable is recent progress in implementations of algorithms to compute stable models of propositional logic programs.
The pressure of fundamental limits on classical computation and the promise of exponential speedups from quantum effects have recently brought quantum circuits (Proc. R. Soc. Lond. A, Math. Phys. Sci., vol. 425, p. 73, 1989) to the attention of the electronic design automation community (Proc. 40th ACM/IEEE Design Automation Conf., 2003), (Phys. Rev. A, At. Mol. Opt. Phy., vol. 68, p. 012318, 2003), (Proc. 41st Design Automation Conf., 2004), (Proc. 39th Design Automation Conf., 2002), (Proc. Design, Automation, and Test Eur., 2004), (Phys. Rev. A, At. Mol. Opt. Phy., vol. 69, p. 062321, 2004), (IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst., vol. 22, p. 710, 2003). Efficient quantum-logic circuits that perform two tasks are discussed: 1) implementing generic quantum computations, and 2) initializing quantum registers. In contrast to conventional computing, the latter task is nontrivial because the state space of an n-qubit register is not finite and contains exponential superpositions of classical bitstrings. The proposed circuits are asymptotically optimal for respective tasks and improve earlier published results by at least a factor of 2. The circuits for generic quantum computation constructed by the algorithms are the most efficient known today in terms of the number of most expensive gates [quantum controlled-NOTs (CNOTs)]. They are based on an analog of the Shannon decomposition of Boolean functions and a new circuit block, called quantum multiplexor (QMUX), which generalizes several known constructions. A theoretical lower bound implies that the circuits cannot be improved by more than a factor of 2. It is additionally shown how to accommodate the severe architectural limitation of using only nearest neighbor gates, which is representative of current implementation technologies. This increases the number of gates by almost an order of magnitude, but preserves the asymptotic optimality of gate counts
This book presents the advancements and applications of neutrosophics. Chapter 1 first introduces the interval neutrosophic sets which is an instance of neutrosophic sets. In this chapter, the definition of interval neutrosophic sets and set-theoretic operators are given and various properties of interval neutrosophic set are proved. Chapter 2 defines the interval neutrosophic logic based on interval neutrosophic sets including the syntax and semantics of first order interval neutrosophic propositional logic and first order interval neutrosophic predicate logic. The interval neutrosophic logic can reason and model fuzzy, incomplete and inconsistent information. In this chapter, we also design an interval neutrosophic inference system based on first order interval neutrosophic predicate logic. The interval neutrosophic inference system can be applied to decision making. Chapter 3 gives one application of interval neutrosophic sets and logic in the field of relational databases. Neutrosophic data model is the generalization of fuzzy data model and paraconsistent data model. Here, we generalize various set-theoretic and relation-theoretic operations of fuzzy data model to neutrosophic data model. Chapter 4 gives another application of interval neutrosophic logic. A soft semantic Web Services agent framework is proposed to faciliate the registration and discovery of high quality semantic Web Services agent. The intelligent inference engine module of soft Semantic Web Services agent is implemented using interval neutrosophic logic.
what exactly are these logical This essay is the text of a retiring presidential address to the Association for Symbolic Logic. For a number of historical and sociological reasons, the largely mathematical membership of the Association is aware of the three great schools in the philosophy of mathematics at the turn of the 20th century—Platonism, Formalism, and Intuitionism—but unfamiliar with any school of thought in the philosophy of logic. This address was an effort to introduce the range of philosophical views about logic by rough analogy with the big three about mathematics. Along the way, it sketches the positions of Kant, the 19th-century German scientific materialists, Frege, Mill, early Wittgenstein, Carnap, Ayer, Quine, and Putnam, with gestures toward Descartes, Bolzano, and Russell, and ends with a second-philosophical alternative.
Tareq Salameh, Mena Maurice Farag, Abdul-Kadir Hamid
et al.
This study addresses the fundamental challenge of accurately forecasting power generation from photovoltaic (PV) systems, which is crucial for effective grid integration and energy management. The intermittency and variability of solar power due to environmental factors pose significant difficulties in achieving reliable predictions. An adaptive neuro-fuzzy inference system (ANFIS) model is proposed for forecasting the performance of a 2.88 kW on-grid PV system in Sharjah, UAE. The model leverages extensive real-time data collected during the peak energy generation season to predict critical variables such as the maximum power point (MPP), voltage, and current. The ANFIS model achieves high prediction accuracy, with a Coefficient of Determination (R2) of 0.9967 for power generation, 0.9076 for voltage generation, and 0.9913 for current generation. These results highlight the model’s robustness in capturing the nonlinear dependencies between environmental factors and PV output. The study compares the ANFIS model with other established machine learning models, including Linear Regression, Decision Tree, Support Vector Machine (SVM), and Random Forest. The ANFIS model outperforms these models in terms of prediction accuracy, demonstrating its superior generalization capabilities. The findings underscore the potential of the ANFIS model for robust forecasting and effective PV performance management, providing a reliable tool for early fault detection and system assessment. Future work will focus on integrating fault detection capabilities and extending model validation across different seasons to ensure a comprehensive investigation of the system dynamics under fluctuating weather conditions.
Nazzla Rauzatul, Jaya Indra, Panggabean Donwill
et al.
This study aims to determine the potential locations for alternative energy sources from waves and currents. Located in the Indian Ocean, the hydrodynamics potential of Bengkulu waters is quite high. In this study, we used data obtained from the OSCAR satellite series and bathymetric data obtained from Dishidrosal. The data series for currents are 5-year from 2019 to 2023 and were analyzed to classify the distribution values of ocean currents and bathymetry to generate the seabed topography profile. The method used in this study employ Inverse Distance Weighting and Fuzzy Logic. The sea surface current velocity is represented by the distribution of the average current speed (cm/s), which is divided into three classes slow (3.08–3.50), medium (3.5-7.84), and fast (7.84 – 12.65). The fuzzy analysis results show the estimation of suitable sites using defuzzification results at approximately 12 m. The classes for sea depth (m) were shallow (0.13-5.0), medium (5.0-20), and deep (20-315.35). The potential location is in the northern part of the province, specifically in North Bengkulu, Central Bengkulu, Bengkulu City, Seluma, and South Bengkulu, which topographically allows energy accumulation. These three districts can be designated as locations for the development of alternative electrical energy using ocean waves and currents.
Current imitation learning approaches, predominantly based on deep neural networks (DNNs), offer efficient mechanisms for learning driving policies from real-world datasets. However, they suffer from inherent limitations in interpretability and generalizability—issues of critical importance in safety-critical domains such as autonomous driving. In this paper, we introduce Symbolic Imitation Learning (SIL), a novel framework that leverages Inductive Logic Programming (ILP) to derive explainable and generalizable driving policies from synthetic datasets. We evaluate SIL on real-world HighD and NGSim datasets, comparing its performance with state-of-the-art neural imitation learning methods using metrics such as collision rate, lane change efficiency, and average speed. The results indicate that SIL significantly enhances policy transparency while maintaining strong performance across varied driving conditions. These findings highlight the potential of integrating ILP into imitation learning to promote safer and more reliable autonomous systems.