In order to meet the diverse needs of smart substation users for relay protection devices, and to realize the personalized customization function of protection logic by engineering users, a user secondary development ...
详细信息
Don’t Starve is a survival video game where the objective is for the player to survive as long as possible without dying. The game is challenging to play due to new situations being randomly generated making survival...
详细信息
Strong Persistence pSPq, since its perception ten years ago, has been at the center of attention in the realm of forgetting in logic programming. So-called forgetting instances, for which it is possible to obtain pSPq...
详细信息
The internal audits carried out in the first half of 2019 in water laboratories as part of quality accreditation in accordance with ISO/IEC 17025:2017 showed a high frequency of adverse events in connection with sampl...
详细信息
The internal audits carried out in the first half of 2019 in water laboratories as part of quality accreditation in accordance with ISO/IEC 17025:2017 showed a high frequency of adverse events in connection with sampling. These faults can be a consequence of a wide range of causes, and in some cases, the information about them can be insufficient or unclear. Considering that sampling has a major influence on the quality of the analytical results provided by water laboratories, this work presents a system for reporting and learning adverse events. Its aim is to record nonconformities, errors, and adverse events, making possible automatic data analysis aiming to ensure continuous improvement in operational sampling. The system is based on the Eindhoven Classification Model and enables automatic data analysis and reporting to identify the main causes of failure. logic programming is used to represent knowledge and support the reasoning mechanisms to model the universe of discourse in scenarios of incomplete, contradicting, or even unknown information. In addition to suggesting solutions to the problem, the system provides formal evidence of the solutions presented, which will help to continuously improve drinking water quality and promote public health.
The rise of powerful AI technology for a range of applications that are sensitive to legal, social, and ethical norms demands decision-making support in presence of norms and regulations. Normative reasoning is the re...
详细信息
The rise of powerful AI technology for a range of applications that are sensitive to legal, social, and ethical norms demands decision-making support in presence of norms and regulations. Normative reasoning is the realm of deontic logics, that are challenged by well-known benchmark problems (deontic paradoxes), and lack efficient computational tools. In this paper, we use Answer Set programming (ASP) for addressing these shortcomings and showcase how to encode and resolve several well-known deontic paradoxes utilizing weak constraints. By abstracting and generalizing this encoding, we present a methodology for translating normative systems in ASP with weak constraints. This methodology is applied to "ethical" versions of Pac-man, where we obtain a comparable performance with related works, but ethically preferable results.
Abstract argumentation is a popular toolkit for modeling, evaluating, and comparing arguments. Relationships between arguments are specified in argumentation frameworks (AFs), and conditions are placed on sets (extens...
详细信息
The fully connected topology, which coordinates the connection of each neuron with all other neurons, remains the most commonly used structure in Hopfield-type neural networks. However, fully connected neurons may for...
详细信息
The fully connected topology, which coordinates the connection of each neuron with all other neurons, remains the most commonly used structure in Hopfield-type neural networks. However, fully connected neurons may form a highly complex network, resulting in a high training cost and making the network biologically unrealistic. Biologists have observed a small-world topology with sparse connections in the actual brain cortex. The bionic small-world neural network structure has inspired various application scenarios. However, in previous studies, the long-range wirings in the small-world network have been found to cause network instability. In this study, we investigate the influence of neural network training on the small-world topology. The role of the path length and clustering coefficient of neurons is expounded in the neural network training process. We employ Watt and Strogatz's small-world model as the topology for the Hopfield neural network and conduct computer simulations. We observe that the random existence of neuron connections may cause unstable network energies and generate oscillations during the training process. A new method is proposed to mitigate the instability of small-world networks. The proposed method starts with a neuron as the pattern centroid along the radial, which arranges its wirings in compliance with the Gaussian distribution. The new method is tested on the MNIST handwritten digit dataset. The simulation confirms that the new small-world series has higher stability in terms of the learning accuracy and a higher convergence speed compared with Watt and Strogatz's small-world model.
作者：
Wotawa, Franz
Institute of Software Technology Inffeldgasse 16b/2 GrazA-8010 Austria
Allocating tasks to computing nodes in a network is an important configuration problem. In the case of fail-safe networks, such configuration must be changed during operation if a computing node fails. Hence, a fast c...
详细信息
Answer Set programming (ASP) is a well-known AI formalism. Traditional ASP systems, that follow the "ground&solve" approach, are intrinsically limited by the so-called grounding bottleneck. Basically, th...
详细信息
Modern applications combine information from a great variety of sources. Oftentimes, some of these sources, like machine-learning systems, are not strictly binary but associated with some degree of (lack of) confidenc...
详细信息
Modern applications combine information from a great variety of sources. Oftentimes, some of these sources, like machine-learning systems, are not strictly binary but associated with some degree of (lack of) confidence in the observation. We propose MV-Datalog and MV-Datalog(+/-) as extensions of Datalog and Datalog(+/-), respectively, to the fuzzy semantics of infinite-valued Lukasiewicz logic L as languages for effectively reasoning in scenarios where such uncertain observations occur. We show that the semantics of MV-Datalog exhibits similar model theoretic properties as Datalog. In particular, we show that (fuzzy) entailment can be decided via minimal fuzzy models. We show that when they exist, such minimal fuzzy models are unique and can be characterised in terms of a linear optimisation problem over the output of a fixed-point procedure. On the basis of this characterisation, we propose similar many-valued semantics for rules with existential quantification in the head, extending Datalog(+/-).
暂无评论