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NSLPCD: Topic based tweets clustering using Node significance based label propagation community detection algorithm Ann. Math. Artif. Intel. (IF 0.778) Pub Date : 20200924
Jagrati Singh, Anil Kumar SinghSocial networks like Twitter, Facebook have recently become the most widely used communication platforms for people to propagate information rapidly. Fast diffusion of information creates accuracy and scalability issues towards topic detection. Most of the existing approaches can detect the most popular topics on a large scale. However, these approaches are not effective for faster detection. This

Categorical study for algebras of Fitting’s latticevalued logic and latticevalued modal logic Ann. Math. Artif. Intel. (IF 0.778) Pub Date : 20200921
Kumar Sankar Ray, Litan Kumar DasThe paper explores categorical interconnections between latticevalued relational systems and algebras of Fitting’s latticevalued modal logic. We define latticevalued Boolean systems, and then we study adjointness and coadjointness of functors defined on them. As a result, we get a duality for algebras of latticevalued logic. Following this duality result, we establish a duality for algebras of

Blackbox combinatorial optimization using models with integervalued minima Ann. Math. Artif. Intel. (IF 0.778) Pub Date : 20200919
Laurens Bliek, Sicco Verwer, Mathijs de WeerdtWhen a blackbox optimization objective can only be evaluated with costly or noisy measurements, most standard optimization algorithms are unsuited to find the optimal solution. Specialized algorithms that deal with exactly this situation make use of surrogate models. These models are usually continuous and smooth, which is beneficial for continuous optimization problems, but not necessarily for combinatorial

On integer closure in a system of unit two variable per inequality constraints Ann. Math. Artif. Intel. (IF 0.778) Pub Date : 20200915
K. Subramani, P. WojciechowskiIn this paper, we study the problem of computing the lattice point closure of a conjunction of Unit Two Variable Per Inequality (UTVPI) constraints. We accomplish this by adapting Johnson’s all pairs shortest path algorithm to UTVPI constraint systems (UCSs). Thus, we obtain a closure algorithm that is efficient for sparse constraint systems. This problem has been extremely wellstudied in the literature

Multivariate time series analysis from a Bayesian machine learning perspective Ann. Math. Artif. Intel. (IF 0.778) Pub Date : 20200904
Jinwen Qiu, S. Rao Jammalamadaka, Ning NingIn this paper, we perform multivariate time series analysis from a Bayesian machine learning perspective through the proposed multivariate Bayesian time series (MBTS) model. The multivariate structure and the Bayesian framework allow the model to take advantage of the association structure among target series, select important features, and train the datadriven model at the same time. Extensive analyses

Realtime solving of computationally hard problems using optimal algorithm portfolios Ann. Math. Artif. Intel. (IF 0.778) Pub Date : 20200828
Yair Nof, Ofer StrichmanVarious hard realtime systems have a desired requirement which is impossible to fulfill: to solve a computationally hard optimization problem within a short and fixed amount of time T, e.g., T = 0.5 seconds. For such a task, the exact, exponential algorithms, as well as various PolynomialTime Approximation Schemes, are irrelevant because they can exceed T. What is left in practice is to combine various

Classifying the valence of autobiographical memories from fMRI data Ann. Math. Artif. Intel. (IF 0.778) Pub Date : 20200804
Alex Frid, Larry M. Manevitz, Norberto Eiji NawaWe show that fMRI analysis using machine learning tools are sufficient to distinguish valence (i.e., positive or negative) of freely retrieved autobiographical memories in a crossparticipant setting. Our methodology uses feature selection (ReliefF) in combination with boosting methods, both applied directly to data represented in voxel space. In previous work using the same data set, Nawa and Ando

Correction to: Log A G : An algebraic nonmonotonic logic for reasoning with graded propositions Ann. Math. Artif. Intel. (IF 0.778) Pub Date : 20200802
Nourhan Ehab, Haythem O. IsmailDue to an oversight by the Publisher during the typesetting stage, an uncorrected version of the paper was published.

Derivation and analysis of parallelintime neural ordinary differential equations Ann. Math. Artif. Intel. (IF 0.778) Pub Date : 20200725
E. LorinThe introduction in 2015 of Residual Neural Networks (RNN) and ResNET allowed for outstanding improvements of the performance of learning algorithms for evolution problems containing a “large” number of layers. Continuousdepth RNNlike models called Neural Ordinary Differential Equations (NODE) were then introduced in 2019. The latter have a constant memory cost, and avoid the a priori specification

Digitized rotations of 12 neighbors on the triangular grid Ann. Math. Artif. Intel. (IF 0.778) Pub Date : 20200722
Aydın Avkan; Benedek Nagy; Müge SaadetoğluThere are various geometric transformations, e.g., translations, rotations, which are always bijections in the Euclidean space. Their digital counterpart, i.e., their digitized variants are defined on discrete grids, since most of our pictures are digital nowadays. Usually, these digital versions of the transformations have different properties than the original continuous variants have. Rotations

Mutual conditional independence and its applications to model selection in Markov networks Ann. Math. Artif. Intel. (IF 0.778) Pub Date : 20200721
Niharika Gauraha; Swapan K. ParuiThe fundamental concepts underlying Markov networks are the conditional independence and the set of rules called Markov properties that translate conditional independence constraints into graphs. We introduce the concept of mutual conditional independence in an independent set of a Markov network, and we prove its equivalence to the Markov properties under certain regularity conditions. This extends

On a hypergraph probabilistic graphical model Ann. Math. Artif. Intel. (IF 0.778) Pub Date : 20200710
Mohammad Ali Javidian; Zhiyu Wang; Linyuan Lu; Marco ValtortaWe propose a directed acyclic hypergraph framework for a probabilistic graphical model that we call Bayesian hypergraphs. The space of directed acyclic hypergraphs is much larger than the space of chain graphs. Hence Bayesian hypergraphs can model much finer factorizations than Bayesian networks or LWF chain graphs and provide simpler and more computationally efficient procedures for factorizations

L o g A G : An algebraic NonMonotonic logic for reasoning with graded propositions Ann. Math. Artif. Intel. (IF 0.778) Pub Date : 20200620
Nourhan Ehab, Haythem O. IsmailWe present LogAG, a weighted algebraic nonmonotonic logic for reasoning with graded beliefs. LogAG is algebraic in that it is a language of only terms, some of which denote propositions and may be associated with ordered grades. The grades could be taken to represent a wide variety of phenomena including preference degrees, priority levels, trust ranks, and uncertainty measures. Reasoning in LogAG

Generalized feature similarity measure Ann. Math. Artif. Intel. (IF 0.778) Pub Date : 20200520
Firuz KamalovQuantifying the degree of relation between a feature and target class is one of the key aspects of machine learning. In this regard, information gain (IG) and χ2 are two of the most widely used measures in feature evaluation. In this paper, we discuss a novel approach to unifying these and other existing feature evaluation measures under a common framework. In particular, we introduce a new generalized

On biased random walks, corrupted intervals, and learning under adversarial design Ann. Math. Artif. Intel. (IF 0.778) Pub Date : 20200518
Daniel Berend; Aryeh Kontorovich; Lev Reyzin; Thomas RobinsonWe tackle some fundamental problems in probability theory on corrupted random processes on the integer line. We analyze when a biased random walk is expected to reach its bottommost point and when intervals of integer points can be detected under a natural model of noise. We apply these results to problems in learning thresholds and intervals under a new model for learning under adversarial design

Leveraging cluster backbones for improving MAP inference in statistical relational models Ann. Math. Artif. Intel. (IF 0.778) Pub Date : 20200507
MohamedHamza Ibrahim; Christopher Pal; Gilles PesantA wide range of important problems in machine learning, expert system, social network analysis, bioinformatics and information theory can be formulated as a maximum aposteriori (MAP) inference problem on statistical relational models. While offtheshelf inference algorithms that are based on local search and messagepassing may provide adequate solutions in some situations, they frequently give poor

Instance space analysis for a personnel scheduling problem Ann. Math. Artif. Intel. (IF 0.778) Pub Date : 20200424
Lucas Kletzander, Nysret Musliu, Kate SmithMilesThis paper considers the Rotating Workforce Scheduling Problem, and shows how the strengths and weaknesses of various solution methods can be understood by the indepth evaluation offered by a recently developed methodology known as Instance Space Analysis. We first present a set of features aiming to describe hardness of test instances. We create a new, more diverse set of instances based on an initial

What do you really want to do? Towards a Theory of Intentions for HumanRobot Collaboration Ann. Math. Artif. Intel. (IF 0.778) Pub Date : 20200324
Rocio Gomez, Mohan Sridharan, Heather RileyThe architecture described in this paper encodes a theory of intentions based on the key principles of nonprocrastination, persistence, and automatically limiting reasoning to relevant knowledge and observations. The architecture reasons with transition diagrams of any given domain at two different resolutions, with the fineresolution description defined as a refinement of, and hence tightlycoupled

Learning nonconvex abstract concepts with regulated activation networks Ann. Math. Artif. Intel. (IF 0.778) Pub Date : 20200321
Rahul Sharma, Bernardete Ribeiro, Alexandre Miguel Pinto, F. Amílcar CardosoPerceivable objects are customarily termed as concepts and their representations (localistdistributed, modalityspecific, or experiencedependent) are ingrained in our lives. Despite a considerable amount of computational modeling research focuses on concrete concepts, no comprehensible method for abstract concepts has hitherto been considered. Abstract concepts can be viewed as a blend of concrete

Humanintheloop active learning via brain computer interface Ann. Math. Artif. Intel. (IF 0.778) Pub Date : 20200316
Eitan Netzer, Amir B. GevaThis paper develops and examines an innovative methodology for training an artificial neural network to identify and tag target visual objects in a given database. While the field of Artificial Intelligence in general, and computer vision in particular, has greatly advanced in recent years, fast and efficient methods for tagging (i.e., labeling) visual targets are still lacking. Tagging data is important

Multiple k −opt evaluation multiple k −opt moves with GPU high performance local search to largescale traveling salesman problems Ann. Math. Artif. Intel. (IF 0.778) Pub Date : 20200416
WenBao Qiao; JeanCharles CréputThe 2opt, 3opt or k–opt heuristics are classical local search algorithms for traveling salesman problems (TSP) in combinatorial optimization area, while sequential k–opt complete neighborhood examination takes polynomial time complexity which is timeconsuming to approach large scale TSP instances. This paper introduces a reasonable methodology called “multiple k–opt evaluation, multiple k–opt moves”

Approximate kernel partial least squares Ann. Math. Artif. Intel. (IF 0.778) Pub Date : 20200327
Xiling Liu; Shuisheng ZhouAs an extension of partial least squares (PLS), kernel partial least squares (KPLS) is an very important methods to find nonlinear patterns from data. However, the application of KPLS to largescale problems remains a big challenge, due to storage and computation issues in the number of examples. To address this limitation, we utilize randomness to design scalable new variants of the kernel matrix

Characterization Of sampling patterns for lowttrank tensor retrieval Ann. Math. Artif. Intel. (IF 0.778) Pub Date : 20200316
Morteza Ashraphijuo; Xiaodong WangIn this paper, we analyze the fundamental conditions for lowrank tensor completion given the separation or tensortrain (TT) rank, i.e., ranks of TT unfoldings. We exploit the algebraic structure of the TT decomposition to obtain the deterministic necessary and sufficient conditions on the locations of the samples to ensure finite completability. Specifically, we propose an algebraic geometric analysis

Energy allocation and payment: a gametheoretic approach Ann. Math. Artif. Intel. (IF 0.778) Pub Date : 20200218
Paolo Giuliodori; Stefano Bistarelli; Dimitri MugnaiNowadays, energy represents the most important resource; however, we need to face several energyrelated rising issues, one main concern is how energy is consumed. In particular, how we can stimulate consumers on a specific behaviour. In this work, we present a model facing energy allocation and payment. Thus, we start with the explanation of the first step of our work concerning a mechanism design

DiscrepancyBased Theory and Algorithms for Forecasting NonStationary Time Series Ann. Math. Artif. Intel. (IF 0.778) Pub Date : 20200204
Vitaly Kuznetsov; Mehryar MohriWe present datadependent learning bounds for the general scenario of nonstationary nonmixing stochastic processes. Our learning guarantees are expressed in terms of a datadependent measure of sequential complexity and a discrepancy measure that can be estimated from data under some mild assumptions. Our learning bounds guide the design of new algorithms for nonstationary time series forecasting

Semantic string operation for specializing AHC algorithm for text clustering Ann. Math. Artif. Intel. (IF 0.778) Pub Date : 20200113
Taeho JoThis article proposes the modified AHC (Agglomerative Hierarchical Clustering) algorithm which clusters string vectors, instead of numerical vectors, as the approach to the text clustering. The results from applying the string vector based algorithms to the text clustering were successful in previous works and synergy effect between the text clustering and the word clustering is expected by combining

Sufficient conditions for the existence of a sample mean of time series under dynamic time warping Ann. Math. Artif. Intel. (IF 0.778) Pub Date : 20200110
Brijnesh Jain; David SchultzTime series averaging is an important subroutine for several time series data mining tasks. The most successful approaches formulate the problem of time series averaging as an optimization problem based on the dynamic time warping (DTW) distance. The existence of an optimal solution, called sample mean, is an open problem for more than four decades. Its existence is a necessary prerequisite to formulate

A branch & bound algorithm to determine optimal crosssplits for decision tree induction Ann. Math. Artif. Intel. (IF 0.778) Pub Date : 20200103
Ferdinand Bollwein; Martin Dahmen; Stephan WestphalStateoftheart decision tree algorithms are topdown induction heuristics which greedily partition the attribute space by iteratively choosing the best split on an individual attribute. Despite their attractive performance in terms of runtime, simple examples, such as the XORProblem, point out that these heuristics often fail to find the best classification rules if there are strong interactions

The SAT+CAS method for combinatorial search with applications to best matrices Ann. Math. Artif. Intel. (IF 0.778) Pub Date : 20191205
Curtis Bright; Dragomir Ž. Đoković; Ilias Kotsireas; Vijay GaneshIn this paper, we provide an overview of the SAT+CAS method that combines satisfiability checkers (SAT solvers) and computer algebra systems (CAS) to resolve combinatorial conjectures, and present new results visàvis best matrices. The SAT+CAS method is a variant of the Davis–Putnam–Logemann–Loveland DPLL(T) architecture, where the T solver is replaced by a CAS. We describe how the SAT+CAS method

A subdivision algorithm to reason on highdegree polynomial constraints over finite domains Ann. Math. Artif. Intel. (IF 0.778) Pub Date : 20191205
Federico Bergenti; Stefania MonicaThis paper proposes an algorithm to reason on constraints expressed in terms of polynomials with integer coefficients whose variables take values from finite subsets of the integers. The proposed algorithm assumes that an initial approximation of the domains of variables is available in terms of a bounding box, and it recursively subdivides the box into disjoint boxes until a termination condition

Learning under p tampering poisoning attacks Ann. Math. Artif. Intel. (IF 0.778) Pub Date : 20191203
Saeed Mahloujifar; Dimitrios I. Diochnos; Mohammad MahmoodyRecently, Mahloujifar and Mahmoody (Theory of Cryptography Conference’17) studied attacks against learning algorithms using a special case of Valiant’s malicious noise, called ptampering, in which the adversary gets to change any training example with independent probability p but is limited to only choose ‘adversarial’ examples with correct labels. They obtained ptampering attacks that increase

Directed Lovász local lemma and Shearer’s lemma Ann. Math. Artif. Intel. (IF 0.778) Pub Date : 20191202
Lefteris Kirousis; John Livieratos; Kostas I. PsaromiligkosMoser and Tardos (J. ACM (JACM) 57(2), 11 2010) gave an algorithmic proof of the lopsided Lovász local lemma (LLL) in the variable framework, where each of the undesirable events is assumed to depend on a subset of a collection of independent random variables. For the proof, they define a notion of a lopsided dependency between the events suitable for this framework. In this work, we strengthen this

Nonterminating processes in the situation calculus Ann. Math. Artif. Intel. (IF 0.778) Pub Date : 20191118
Giuseppe De Giacomo; Eugenia Ternovska; Ray ReiterBy their very design, many robot control programs are nonterminating. This paper describes a situation calculus approach to expressing and proving properties of nonterminating programs expressed in Golog, a high level logic programming language for modeling and implementing dynamical systems. Because in this approach actions and programs are represented in classical (secondorder) logic, it is natural

Feature uncertainty bounds for explicit feature maps and large robust nonlinear SVM classifiers Ann. Math. Artif. Intel. (IF 0.778) Pub Date : 20191115
Nicolas Couellan; Sophie JanWe consider the binary classification problem when data are large and subject to unknown but bounded uncertainties. We address the problem by formulating the nonlinear support vector machine training problem with robust optimization. To do so, we analyze and propose two bounding schemes for uncertainties associated to random approximate features in low dimensional spaces. The proposed bound calculations

Datadriven Koopman operator approach for computational neuroscience Ann. Math. Artif. Intel. (IF 0.778) Pub Date : 20191111
Natasza Marrouch, Joanna Slawinska, Dimitrios Giannakis, Heather L. ReadThis article presents a novel, nonlinear, datadriven signal processing method, which can help neuroscience researchers visualize and understand complex dynamical patterns in both time and space. Specifically, we present applications of a Koopman operator approach for eigendecomposition of electrophysiological signals into orthogonal, coherent components and examine their associated spatiotemporal

Multivalued logic in graph transformation theory and selfadaptive systems Ann. Math. Artif. Intel. (IF 0.778) Pub Date : 20191102
Dmitry Maximov; Sergey RyvkinGraph transformation theory uses rules to perform a graph transformation. However, there is no a way to choose between such different transformations in the case where several of them are applicable. A way to get the choice is suggested here based on the comparing of the values of implications which correspond to different transformation variants. The relationship between the topos of bundles, and

Mixed deterministic and probabilistic networks. Ann. Math. Artif. Intel. (IF 0.778) Pub Date : 20081101
Robert Mateescu,Rina DechterThe paper introduces mixed networks, a new graphical model framework for expressing and reasoning with probabilistic and deterministic information. The motivation to develop mixed networks stems from the desire to fully exploit the deterministic information (constraints) that is often present in graphical models. Several concepts and algorithms specific to belief networks and constraint networks are

Deep learning models for brain machine interfaces Ann. Math. Artif. Intel. (IF 0.778) Pub Date : 20191002
Lachezar Bozhkov, Petia GeorgievaDeep Learning methods have been rising in popularity in the past few years, and are now used as a fundamental component in various application domains such as computer vision, natural language processing, bioinformatics. Supervised learning with Convolutional Neural Networks has become the state of the art approach in many image related works. However, despite the great success of deep learning methods

Lattice map spiking neural networks (LMSNNs) for clustering and classifying image data Ann. Math. Artif. Intel. (IF 0.778) Pub Date : 20190921
Hananel Hazan, Daniel J. Saunders, Darpan T. Sanghavi, Hava Siegelmann, Robert KozmaSpiking neural networks (SNNs) with a lattice architecture are introduced in this work, combining several desirable properties of SNNs and selforganized maps (SOMs). Networks are trained with biologically motivated, unsupervised learning rules to obtain a selforganized grid of filters via cooperative and competitive excitatoryinhibitory interactions. Several inhibition strategies are developed and

Compilation of static and evolving conditional knowledge bases for computing induced nonmonotonic inference relations Ann. Math. Artif. Intel. (IF 0.778) Pub Date : 20190830
Christoph Beierle; Steven Kutsch; Kai SauerwaldSeveral different semantics have been proposed for conditional knowledge bases \(\mathcal {R}\) containing qualitative conditionals of the form “If A, then usually B”, leading to different nonmonotonic inference relations induced by \(\mathcal {R}\). For the notion of crepresentations which are a subclass of all ranking functions accepting \(\mathcal {R}\), a skeptical inference relation, called cinference

Statistical learning based on Markovian data maximal deviation inequalities and learning rates Ann. Math. Artif. Intel. (IF 0.778) Pub Date : 20190829
Stephan Clémençon; Patrice Bertail; Gabriela CiołekIn statistical learning theory, numerous works established nonasymptotic bounds assessing the generalization capacity of empirical risk minimizers under a large variety of complexity assumptions for the class of decision rules over which optimization is performed, by means of sharp control of uniform deviation of i.i.d. averages from their expectation, while fully ignoring the possible dependence

Analyzing cognitive processes from complex neurophysiologically based data: some lessons Ann. Math. Artif. Intel. (IF 0.778) Pub Date : 20190828
Alex Frid, Larry M. ManevitzIn the past few years, due to their ability to extract multivariate correlations, machine learning tools have become more and more important for discovery of information in very complex data sets. This has had specific application to various data sets related to human brain tasks. However, this is far from a simple and direct methodology. Some of the issues involve dealing with the extreme signal to

Dynamic search trajectory methods for global optimization Ann. Math. Artif. Intel. (IF 0.778) Pub Date : 20190827
StamatiosAggelos N. Alexandropoulos; Panos M. Pardalos; Michael N. VrahatisA detailed review of the dynamic search trajectory methods for global optimization is given. In addition, a family of dynamic search trajectories methods that are created using numerical methods for solving autonomous ordinary differential equations is presented. Furthermore, a strategy for developing globally convergent methods that is applicable to the proposed family of methods is given and the

Targeting solutions in Bayesian multiobjective optimization: sequential and batch versions Ann. Math. Artif. Intel. (IF 0.778) Pub Date : 20190820
David Gaudrie; Rodolphe Le Riche; Victor Picheny; Benoît Enaux; Vincent HerbertMultiobjective optimization aims at finding tradeoff solutions to conflicting objectives. These constitute the Pareto optimal set. In the context of expensivetoevaluate functions, it is impossible and often noninformative to look for the entire set. As an enduser would typically prefer a certain part of the objective space, we modify the Bayesian multiobjective optimization algorithm which uses

Fundamental conditions on the sampling pattern for union of lowrank subspaces retrieval Ann. Math. Artif. Intel. (IF 0.778) Pub Date : 20190816
Morteza Ashraphijuo; Xiaodong WangThis paper is concerned with investigating the fundamental conditions on the locations of the sampled entries, i.e., sampling pattern, for finite completability of a matrix that represents the union of several subspaces with given ranks. In contrast with the existing analysis on Grassmannian manifold for the conventional matrix completion, we propose a geometric analysis on the manifold structure for

The price to pay for forgoing normalization in fair division of indivisible goods Ann. Math. Artif. Intel. (IF 0.778) Pub Date : 20190816
Pascal Lange; NhanTam Nguyen; Jörg RotheWe study the complexity of fair division of indivisible goods and consider settings where agents can have nonzero utility for the empty bundle. This is a deviation from a common normalization assumption in the literature, and we show that this inconspicuous change can lead to an increase in complexity: In particular, while an allocation maximizing social welfare by the Nash product is known to be easy

Checking inferenceproofness of attributedisjoint and duplicatepreserving fragmentations Ann. Math. Artif. Intel. (IF 0.778) Pub Date : 20190809
Joachim Biskup; Marcel PreußThe transmission of own and partly confidential data to another agent comes along with the risk of enabling the receiver to infer information he is not entitled to learn. We consider a specific countermeasure against unwanted inferences about associations between data values whose combination of attributes are declared to be sensitive. This countermeasure fragments a relation instance into attributedisjoint

A linear relation between input and first layer in neural networks Ann. Math. Artif. Intel. (IF 0.778) Pub Date : 20190808
Sebastián A. GrilloArtificial neural networks grow on the number of applications and complexity, which require a minimization on the number of units for some practical implementations. A particular problem is the minimum number of units that a feed forward neural network needs on its first layer. In order to study this problem, it is defined a family of classification problems following a continuity hypothesis, where

Kernel collaborative online algorithms for multitask learning Ann. Math. Artif. Intel. (IF 0.778) Pub Date : 20190808
A. Aravindh; S. S. Shiju; S. SumitraIn many real time applications, we often have to deal with classification, regression or clustering problems that involve multiple tasks. The conventional machine learning approaches solve these tasks independently by ignoring the task relatedness. In multitask learning (MTL), these related tasks are learned simultaneously by extracting and utilizing the shared information across tasks. This approach

Practical reasoning using values: an argumentative approach based on a hierarchy of values Ann. Math. Artif. Intel. (IF 0.778) Pub Date : 20190806
Juan C. L. Teze; Antoni PerellóMoragues; Lluis Godo; Pablo NoriegaValues are at the heart of human decisionmaking. They are used to decide whether something or some state of affairs is good or not, and they are also used to address the moral dilemma of the right thing to do under given circumstances. Both uses are present in several everyday situations, from the design of a public policy to the negotiation of employee benefit packages. Both uses of values are specially

Two AGMstyle characterizations of model repair Ann. Math. Artif. Intel. (IF 0.778) Pub Date : 20190802
Paulo T. Guerra; Renata WassermannThis work explores formal aspects of model repair, i.e., how to rationally modify Kripke models representing the behavior of a system in order to satisfy a desired property. We investigate the problem in the light of Alchourrón, Gärdenfors, and Makinson’s work on belief revision. We propose two AGMstyle characterizations of model repair: one based on belief sets and the other based on structural changes

Probability, coherent belief and coherent belief changes Ann. Math. Artif. Intel. (IF 0.778) Pub Date : 20190726
John Cantwell; Hans RottThis paper is about the statics and dynamics of belief states that are represented by pairs consisting of an agent’s credences (represented by a subjective probability measure) and her categorical beliefs (represented by a set of possible worlds). Regarding the static side, we argue that the latter proposition should be coherent with respect to the probability measure and that its probability should

Blending under deconstruction Ann. Math. Artif. Intel. (IF 0.778) Pub Date : 20190725
Roberto Confalonieri; Oliver KutzThe cognitivelinguistic theory of conceptual blending was introduced by Fauconnier and Turner in the late 90s to provide a descriptive model and foundational approach for the (almost uniquely) human ability to invent new concepts. Whilst blending is often described as ‘fluid’ and ‘effortless’ when ascribed to humans, it becomes a highly complex, multiparadigm problem in Artificial Intelligence. This

Contextual rational closure for defeasible A ℒ C $\mathcal {ALC}$ Ann. Math. Artif. Intel. (IF 0.778) Pub Date : 20190720
Katarina Britz; Ivan VarzinczakDescription logics have been extended in a number of ways to support defeasible reasoning in the KLM tradition. Such features include preferential or rational defeasible concept inclusion, and defeasible roles in complex concept descriptions. Semantically, defeasible subsumption is obtained by means of a preference order on objects, while defeasible roles are obtained by adding a preference order to

Model simplification for supervised classification of metabolic networks Ann. Math. Artif. Intel. (IF 0.778) Pub Date : 20190717
Ilaria Granata; Mario R. Guarracino; Valery A. Kalyagin; Lucia Maddalena; Ichcha Manipur; Panos M. PardalosMany real applications require the representation of complex entities and their relations. Frequently, networks are the chosen data structures, due to their ability to highlight topological and qualitative characteristics. In this work, we are interested in supervised classification models for data in the form of networks. Given two or more classes whose members are networks, we build mathematical

Firstorder definable countingonly queries Ann. Math. Artif. Intel. (IF 0.778) Pub Date : 20190713
Jelle Hellings; Marc Gyssens; Dirk Van Gucht; Yuqing WuMany data sources can be represented easily by collections of sets of objects. For several practical queries on such collections of sets of objects, the answer does not depend on the precise composition of these sets, but only on the number of sets to which each object belongs. This is the case k= 1 for the more general situation where the query answer only depends on the number of sets to which each

Votingbased ensemble learning for partial lexicographic preference forests over combinatorial domains Ann. Math. Artif. Intel. (IF 0.778) Pub Date : 20190706
Xudong Liu; Miroslaw TruszczynskiWe study preference representation models based on partial lexicographic preference trees (PLPtrees). We propose to represent preference relations as forests of small PLPtrees (PLPforests), and to use voting rules to aggregate orders represented by the individual trees into a single order to be taken as a model of the agent’s preference relation. We show that when learned from examples, PLPforests

Combining intelligent heuristics with simulators in hotel revenue management Ann. Math. Artif. Intel. (IF 0.778) Pub Date : 20190703
Mauro Brunato; Roberto BattitiRevenue Management uses datadriven modelling and optimization methods to decide what to sell, when to sell, to whom to sell, and for which price, in order to increase revenue and profit. Hotel Revenue Management is a very complex context characterized by nonlinearities, many parameters and constraints, and stochasticity, in particular in the demand by customers. It suffers from the curse of dimensionality

Dual embeddings and metrics for word and relational similarity Ann. Math. Artif. Intel. (IF 0.778) Pub Date : 20190701
Dandan Li; Douglas SummersStayWord embedding models excel in measuring word similarity and completing analogies. Word embeddings based on different notions of context trade off strengths in one area for weaknesses in another. Linear bagofwords contexts, such as in word2vec, can capture topical similarity better, while dependencybased word embeddings better encode functional similarity. By combining these two word embeddings

Effective IG heuristics for a singlemachine scheduling problem with family setups and resource constraints Ann. Math. Artif. Intel. (IF 0.778) Pub Date : 20190627
Júlio C. S. N. Pinheiro; José Elias C. ArroyoIn this paper we investigate the problem of scheduling a set of jobs on a singlemachine. The jobs are classified in families and setup times are required between the processing of two jobs of different families. Each job requires a certain amount of a common resource that is supplied through upstream processes. The total resource consumed must not exceed the resource supply up. Therefore, jobs may