Hume's famous character Cleanthes claims that there is no difficulty in explaining the existence of causal chains with no first cause since in them each item is causally explained by its predecessor. Relying on logico-mathematical resources, we argue for two theses: (1) if the existence of Cleanthes' chain can be explained at all, it must be explained by the fact that the causal law ruling it is in force, and (2) the fact that such a causal law is in force cannot explain the occurrence of the events in the chain. In order to perform (1), we manage to express in mathematical terms the intuitive idea that indefinitely delayed explanation is ultimately no explanation. In order to achieve (2), we identify a logical relation we can prove to be as strong as the causal relation at issue in the Cleanthes passage, according to a precise notion of strength of relations. Keywords: cause; chain; autonomous causal chain; ungrounded causal chain; causal law; L-nomologically possible worlds.
Modern artificial systems, such as cooperative traffic systems or swarm robotics, are made of multiple autonomous agents, each handling uncertain, partial and potentially inconsistent information, ...
This paper presents two major aspects of Frege's and Peirce's views on assertion and denial: first, their arguments for the notational choices concerning the representation of assertion and denial in Begriffsschrift (BS) and Existential Graphs (EGs), respectively; and second, those properties of BS and EGs which reflect their inventors' views on assertion and denial. We show that while Frege's notation has an ad hoc sign of assertion and an ad hoc sign of negation, Peirce has a sign of assertion which is also a sign of logical conjunction, and a sign of scope which is also a sign of negation.
Most Relevant Explanation (MRE) is an inference problem in Bayesian networks that finds the most relevant partial instantiation of target variables as an explanation for given evidence. It has been shown in recent literature that it addresses the overspecification problem of existing methods, such as MPE and MAP. In this paper, we propose a novel hierarchical beam search algorithm for solving MRE. The main idea is to use a second-level beam to limit the number of successors generated by the same parent so as to limit the similarity between the solutions in the first-level beam and result in a more diversified population. Three pruning criteria are also introduced to achieve further diversity. Empirical results show that the new algorithm outperforms local search and regular beam search.
We present a natural deduction system for dual-intuitionistic logic. Its distinctive feature is that it is a single-premise multiple-conclusions system. Its relationships with the natural deduction systems for intuitionistic and classical logic are discussed.
In this paper we present an integral solution for law-compliance privacy-protection into trust models for agent systems. Several privacy issues are concerned into trust relationships. Specifically, we define which privacy rights must legally be guaranteed in trusting communities of agents. From them, we describe additional interaction protocols that are required to implement such guarantees. Next, we apply additional message exchanges into a specific application domain (the Agent Trust and Reputation testbed) using JADE agent platform. The decisions about how to apply these control mechanisms (about when to launch the corresponding JADE protocol) has been efficiently carried out by neural computing. It uses past behavior of agents to decide (classify) which agents are worthy to share privacy with, considering which number of past interactions we should take into account. Furthermore, we also enumerate the corresponding privacy violations that would have taken place if these control mechanisms (in form of interaction protocols) were ignored or misused. From the possible existence of privacy violations, a regulatory structure is required to address (prevent and fix) the corresponding harmful consequences. We use Islander (an electronic institution editor) to formally define the scenes where privacy violation may be produced, attached to the ways to repair it: the defeasible actions that could voluntarily reduce or eliminate the privacy damage, and the obligations that the electronic institution would impose as penalties.
Supplier assessment and selection mapping as an essential component of supply chain management are usually multi-criteria decision-making problems. Decision making is the thought process of selecting a logical choice from the available options. This is generally made under fuzzy environment. Fuzzy decision-making is a decision process using the sets whose boundaries are not sharply defined. The aim of this paper is to show how fuzzy set theory, fuzzy decision-making and hybrid solutions based on fuzzy can be used in the various models for supplier assessment and selection in a 50 year period.
Human-activity recognition and seizure-detection techniques have gathered pace with the widespread availability of wearable devices. A study of the literature shows various studies for 3D accelerometer-based seizure detection that describe the selection of acceleration variables and controlled transformations, while discarding the remaining input variable contributions. The aim of this research is to evaluate feature extraction based on different techniques and with the advantage of an overview of all information on the problem. Three feature extraction techniques – namely, Locally Linear Embedding, Principal Component Analysis (PCA) and a Distance-Based PCA – are analyzed and their outcomes compared against K-Nearest Neighbor and Decision Trees. A realistic experimentation simulating epileptic mioclonic convulsions was performed. The PCA-based methods were found to produce solutions that managed the problem perfectly well, either learning specific models for each individual or learning generalized models.
The aim of this paper is twofold: First, we present and develop a system of logic for pragmatics including the act of denial. Second, we analyse in our framework the so-called paradox of assertability. We show that it is possible to yield sentences that are not assertable. Moreover, under certain conditions, a symmetric result can be obtained: There is a specular paradox of deniability. However, this paradox is based on the problematic principle of classical denial equivalence.
This paper presents two major aspects of Frege's and Peirce's views on assertion and denial: first, their arguments for the notational choices concerning the representation of assertion and denial in Begriffsschrift (BS) and Existential Graphs (EGs), respectively; and second, those properties of BS and EGs which reflect their inventors' views on assertion and denial. We show that while Frege's notation has an ad hoc sign of assertion and an ad hoc sign of negation, Peirce has a sign of assertion which is also a sign of logical conjunction, and a sign of scope which is also a sign of negation.
A comprehensive analysis of clustering techniques is presented in this paper through their application to data on meteorological conditions. Six partitional and hierarchical clustering techniques ( k -means, k -medoids, SOM k -means, Agglomerative Hierarchical Clustering, and Clustering based on Gaussian Mixture Models) with different distance criteria, together with some clustering evaluation measures (Calinski–Harabasz, Davies–Bouldin, Gap and Silhouette criterion clustering evaluation object), present various analyses of the main climatic zones in Spain. Real-life data sets, recorded by AEMET (Spanish Meteorological Agency) at four of its weather stations, are analyzed in order to characterize the actual weather conditions at each location. The clustering techniques process the data on some of the main daily meteorological variables collected at these stations over six years between 2004 and 2010.
The number of endangered species has been increased due to shifts in the agricultural production, climate change, and poor urban planning. This has led to investigating new methods to address the problem of plant species identification/classification. In this paper, a plant identification approach using 2D digital leaves images was proposed. The approach used two features extraction methods based on one-dimensional (1D) and two-dimensional (2D) and the Bagging classifier. For the 1D-based methods, Principal Component Analysis (PCA), Direct Linear Discriminant Analysis (DLDA), and PCA + LDA techniques were applied, while 2DPCA and 2DLDA algorithms were used for the 2D-based method. To classify the extracted features in both methods, the Bagging classifier, with the decision tree as a weak learner was used. The five variants, i.e. PCA, PCA + LDA, DLDA, 2DPCA, and 2DLDA, of the approach were tested using the Flavia public dataset which consists of 1907 colored leaves images. The accuracy of these variants was evaluated and the results showed that the 2DPCA and 2DLDA methods were much better than using the PCA, PCA + LDA, and DLDA. Furthermore, it was found that the 2DLDA method was the best one and the increase of the weak learners of the Bagging classifier yielded a better classification accuracy. Also, a comparison with the most related work showed that our approach achieved better accuracy under the same dataset and same experimental setup. (C) 2016 Elsevier B.V. All rights reserved.
In a previous research, a multi-agent artificial immune system for network intrusion detection and classification was proposed and tested, where a multi-layer detection and classification process was executed on each agent, for each host in the network. In this paper, we show the experiments that were held to chose the appropriate classifiers by testing different classifiers and comparing them to increase the detection accuracy and obtain more information on the detected anomalies. It will be shown that no single classifier should be used for all types of attacks, due to different classification rates obtained. This is due to attacks representations in the train set and dependency between features used to detect them. It will also be shown that a basic and simple classifier such as Naive Bayes has better classification results in the case of low-represented attacks, and the basic decision trees such as Naive-Bayes Tree and Best-First Tree give very good results compared to well-known J48 (Weka implementation of C4.5) and Random Forest decision trees. Based on these experiments and their results, Naive Bayes and Best-First tree classifiers were selected to classify the anomaly-detected traffic. It was shown that in the detection phase, 90% of anomalies were detected, and in the classification phase, 88% of false positives were successfully labeled as normal traffic connections, and 79% of DoS and Probe attacks were labeled correctly, mostly by NB, NBTree, and BFTree classifiers.
Multiple iterated revision requires advanced belief revision techniques that are able to integrate several pieces of new information into epistemic states. A crucial feature of this kind of revision is that the multiple pieces of information should be dealt with separately. Previous works have proposed several independence postulates which should ensure this. In this paper, we argue, first, that these postulates are too strong as they may enforce beliefs without justification, and second, that they are not necessary to ensure the principal aim of multiple revision. Instead, principles of conditional preservation guarantee a suitable handling of sets of sentences under revision. We formalize such a principle for multiple propositional revision for ranking functions, and we propose some novel postulates for multiple iterated revision that are in line with AGM and the Darwiche & Pearl postulates. We show that just a few fundamental postulates are enough to cover major approaches to (multiple) iterated belief revision, and that independence in the sense of Thielscher, Jin, and Delgrande is optional. As a proof of concept, we present propositional c-revisions of ranking functions.
The continuous time Bayesian network (CTBN) is a probabilistic graphical model that enables reasoning about complex, interdependent, and continuous-time subsystems. The model uses nodes to denote subsystems and arcs to denote conditional dependence. This dependence manifests in how the dynamics of a subsystem changes based on the current states of its parents in the network. While the original CTBN definition allows users to specify the dynamics of how the system evolves, users might also want to place value expressions over the dynamics of the model in the form of performance functions. We formalize these performance functions for the CTBN and show how they can be factored in the same way as the network, allowing what we argue is a more intuitive and explicit representation. For cases in which a performance function must involve multiple nodes, we show how to augment the structure of the CTBN to account for the performance interaction while maintaining the factorization of a single performance function for each node. We introduce the notion of optimization for CTBNs, and show how a family of performance functions can be used as the evaluation criteria for a multi-objective optimization procedure.
An anomaly, or outlier, is an object exhibiting differences that suggest it belongs to an as-yet undefined class or category. Early detection of anomalies often proves of great importance because they may correspond to events such as fraud, spam, or device malfunctions. By automating the creation of a ranking or list of deviations, we can save time and decrease the cognitive overload of the individuals or groups responsible for responding to such events.Over the years many anomaly and outlier metrics have been developed. In this paper we propose a clustering-based score ensembling method for outlier detection. Using benchmark datasets we evaluate quantitatively the robustness and accuracy of different ensemble strategies. We find that ensembling strategies offer only limited value for increasing overall performance, but provide robustness by negating the influence of severely underperforming models. (C) 2017 Elsevier B.V. All rights reserved.
SRAM-based FPGAs are increasingly popular in the aerospace industry due to their field programmability and low cost. However, they suffer from cosmic radiation induced Single Event Upsets (SEUs). In safety-critical applications, the dependability of the design is a prime concern since failures may have catastrophic consequences. An early analysis of the relationship between dependability metrics, performability-area trade-off, and different mitigation techniques for such applications can reduce the design effort while increasing the design confidence. This paper introduces a novel methodology based on probabilistic model checking, for the analysis of the reliability, availability, safety and performance-area tradeoffs of safety-critical systems for early design decisions. Starting from the high-level description of a system, a Markov reward model is constructed from the Control Data Flow Graph (CDFG) and a component characterization library targeting FPGAs. The proposed model and exhaustive analysis capture all the failure states (based on the fault detection coverage) and repairs possible in the system. We present quantitative results based on an FIR filter circuit to illustrate the applicability of the proposed approach and to demonstrate that a wide range of useful dependability and performability properties can be analyzed using the proposed methodology. The modeling results show the relationship between different mitigation techniques and fault detection coverage, exposing their direct impact on the design for early decisions.
