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A new definition for feature selection stability analysis Ann. Math. Artif. Intel. (IF 1.2) Pub Date : 2024-03-01 Teddy Lazebnik, Avi Rosenfeld
Feature selection (FS) stability is an important topic of recent interest. Finding stable features is important for creating reliable, non-overfitted feature sets, which in turn can be used to generate machine learning models with better accuracy and explanations and are less prone to adversarial attacks. There are currently several definitions of FS stability that are widely used. In this paper, we
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Sequential composition of propositional logic programs Ann. Math. Artif. Intel. (IF 1.2) Pub Date : 2024-02-15 Christian Antić
This paper introduces and studies the sequential composition and decomposition of propositional logic programs. We show that acyclic programs can be decomposed into single-rule programs and provide a general decomposition result for arbitrary programs. We show that the immediate consequence operator of a program can be represented via composition which allows us to compute its least model without any
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Analogical proportions in monounary algebras Ann. Math. Artif. Intel. (IF 1.2) Pub Date : 2024-02-10 Christian Antić
This paper studies analogical proportions in monounary algebras consisting only of a universe and a single unary function, where we analyze the role of congruences, and we show that the analogical proportion relation is characterized in the infinite monounary algebra formed by the natural numbers together with the successor function via difference proportions.
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A category theory approach to the semiotics of machine learning Ann. Math. Artif. Intel. (IF 1.2) Pub Date : 2024-02-09 Fernando Tohmé, Rocco Gangle, Gianluca Caterina
The successes of Machine Learning, and in particular of Deep Learning systems, have led to a reformulation of the Artificial Intelligence agenda. One of the pressing issues in the field is the extraction of knowledge out of the behavior of those systems. In this paper we propose a semiotic analysis of that behavior, based on the formal model of learners. We analyze the topos-theoretic properties that
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Bounds on depth of decision trees derived from decision rule systems with discrete attributes Ann. Math. Artif. Intel. (IF 1.2) Pub Date : 2024-02-08 Kerven Durdymyradov, Mikhail Moshkov
Systems of decision rules and decision trees are widely used as a means for knowledge representation, as classifiers, and as algorithms. They are among the most interpretable models for classifying and representing knowledge. The study of relationships between these two models is an important task of computer science. It is easy to transform a decision tree into a decision rule system. The inverse
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Theoretical aspects of robust SVM optimization in Banach spaces and Nash equilibrium interpretation Ann. Math. Artif. Intel. (IF 1.2) Pub Date : 2024-02-08 Mohammed Sbihi, Nicolas Couellan
There are many real life applications where data can not be effectively represented in Hilbert spaces and/or where the data points are uncertain. In this context, we address the issue of binary classification in Banach spaces in presence of uncertainty. We show that a number of results from classical support vector machines theory can be appropriately generalized to their robust counterpart in Banach
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Learning preference representations based on Choquet integrals for multicriteria decision making Ann. Math. Artif. Intel. (IF 1.2) Pub Date : 2024-02-07 Margot Herin, Patrice Perny, Nataliya Sokolovska
This paper concerns preference elicitation and learning of decision models in the context of multicriteria decision making. We propose an approach to learn a representation of preferences by a non-additive multiattribute utility function, namely a Choquet or bi-Choquet integral. This preference model is parameterized by one-dimensional utility functions measuring the attractiveness of consequences
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A metaheuristic for inferring a ranking model based on multiple reference profiles Ann. Math. Artif. Intel. (IF 1.2) Pub Date : 2024-02-06 Arwa Khannoussi, Alexandru-Liviu Olteanu, Patrick Meyer, Bastien Pasdeloup
In the context of Multiple Criteria Decision Aiding, decision makers often face problems with multiple conflicting criteria that justify the use of preference models to help advancing towards a decision. In order to determine the parameters of these preference models, preference elicitation makes use of preference learning algorithms, usually taking as input holistic judgments, i.e., overall preferences
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A knowledge compilation perspective on queries and transformations for belief tracking Ann. Math. Artif. Intel. (IF 1.2) Pub Date : 2024-01-31 Alexandre Niveau, Hector Palacios, Sergej Scheck, Bruno Zanuttini
Nondeterministic planning is the process of computing plans or policies of actions achieving given goals, when there is nondeterministic uncertainty about the initial state and/or the outcomes of actions. This process encompasses many precise computational problems, from classical planning, where there is no uncertainty, to contingent planning, where the agent has access to observations about the current
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(DRAFT) personalized choice prediction with less user information Ann. Math. Artif. Intel. (IF 1.2) Pub Date : 2024-01-30
Abstract While most models of human choice are linear to ease interpretation, it is not clear whether linear models are good models of human decision making. And while prior studies have investigated how task conditions and group characteristics, such as personality or socio-demographic background, influence human decisions, no prior works have investigated how to use less personal information for
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Parallel homological calculus for 3D binary digital images Ann. Math. Artif. Intel. (IF 1.2) Pub Date : 2024-01-29 Fernando Díaz-del-Río, Helena Molina-Abril, Pedro Real, Darian Onchis, Sergio Blanco-Trejo
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Clique detection with a given reliability Ann. Math. Artif. Intel. (IF 1.2) Pub Date : 2024-01-29 Dmitry Semenov, Alexander Koldanov, Petr Koldanov, Panos Pardalos
In this paper we propose a new notion of a clique reliability. The clique reliability is understood as the ratio of the number of statistically significant links in a clique to the number of edges of the clique. This notion relies on a recently proposed original technique for separating inferences about pairwise connections between vertices of a network into significant and admissible ones. In this
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Weighted and Choquet $$L^p$$ distance representation of comparative dissimilarity relations on fuzzy description profiles Ann. Math. Artif. Intel. (IF 1.2) Pub Date : 2024-01-24 Giulianella Coletti, Davide Petturiti, Bernadette Bouchon-Meunier
We consider comparative dissimilarity relations on pairs on fuzzy description profiles, the latter providing a fuzzy set-based representation of pairs of objects. Such a relation expresses the idea of “no more dissimilar than” and is used by a decision maker when performing a case-based decision task under vague information. We first limit ourselves to those relations admitting a weighted \(\varvec{L}^p\)
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Stability of accuracy for the training of DNNs via the uniform doubling condition Ann. Math. Artif. Intel. (IF 1.2) Pub Date : 2024-01-19
Abstract We study the stability of accuracy during the training of deep neural networks (DNNs). In this context, the training of a DNN is performed via the minimization of a cross-entropy loss function, and the performance metric is accuracy (the proportion of objects that are classified correctly). While training results in a decrease of loss, the accuracy does not necessarily increase during the
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Best-effort adaptation Ann. Math. Artif. Intel. (IF 1.2) Pub Date : 2024-01-13 Pranjal Awasthi, Corinna Cortes, Mehryar Mohri
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RAMP experiments in solving the uncapacitated facility location problem Ann. Math. Artif. Intel. (IF 1.2) Pub Date : 2023-12-30 Telmo Matos
In this paper, we consider three Relaxation Adaptive Memory Programming (RAMP) approaches for solving the Uncapacitated Facility Location Problem (UFLP), whose objective is to locate a set of facilities and allocate these facilities to all clients at minimum cost. Different levels of sophistication were implemented to measure the performance of the RAMP approach. In the simpler level, (Dual-) RAMP
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Learning from masked analogies between sentences at multiple levels of formality Ann. Math. Artif. Intel. (IF 1.2) Pub Date : 2023-12-26
Abstract This paper explores the inference of sentence analogies not restricted to the formal level. We introduce MaskPrompt, a prompt-based method that addresses the analogy task as masked analogy completion. This enables us to fine-tune, in a lightweight manner, pre-trained language models on the task of reconstructing masked spans in analogy prompts. We apply constraints which are approximations
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An abstract view on optimizations in propositional frameworks Ann. Math. Artif. Intel. (IF 1.2) Pub Date : 2023-12-16 Yuliya Lierler
Search/optimization problems are plentiful in scientific and engineering domains. Artificial intelligence has long contributed to the development of search algorithms and declarative programming languages geared toward solving and modeling search/optimization problems. Automated reasoning and knowledge representation are the subfields of AI that are particularly vested in these developments. Many popular
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A faster implementation of EQ and SE queries for switch-list representations Ann. Math. Artif. Intel. (IF 1.2) Pub Date : 2023-12-16 Ondřej Čepek, James Weigle
A switch-list representation (SLR) of a Boolean function is a compressed truth table representation of a Boolean function in which only (i) the function value of the first row in the truth table and (ii) a list of switches are stored. A switch is a Boolean vector whose function value differs from the value of the preceding Boolean vector in the truth table. The paper Čepek and Chromý (JAIR 2020) systematically
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Collective combinatorial optimisation as judgment aggregation Ann. Math. Artif. Intel. (IF 1.2) Pub Date : 2023-12-15 Linus Boes, Rachael Colley, Umberto Grandi, Jérôme Lang, Arianna Novaro
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Logic program proportions Ann. Math. Artif. Intel. (IF 1.2) Pub Date : 2023-12-06 Christian Antić
The purpose of this paper is to present a fresh idea on how symbolic learning might be realized via analogical reasoning. For this, we introduce directed analogical proportions between logic programs of the form “P transforms into Q as R transforms into S” as a mechanism for deriving similar programs by analogy-making. The idea is to instantiate a fragment of a recently introduced abstract algebraic
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Tractable representations for Boolean functional synthesis Ann. Math. Artif. Intel. (IF 1.2) Pub Date : 2023-12-05 S. Akshay, Supratik Chakraborty, Shetal Shah
Given a Boolean relational specification \(F(\textbf{X}, \textbf{Y})\), where \(\textbf{X}\) is a vector of inputs and \(\textbf{Y}\) is a vector of outputs, Boolean functional synthesis requires us to compute a vector of (Skolem) functions \(\varvec{\Psi }(\textbf{X})\), one for each output in \(\textbf{Y}\), such that \(F(\textbf{X}, \varvec{\Psi }(\textbf{X})) \leftrightarrow \exists \textbf{Y}\
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An improvement of Random Node Generator for the uniform generation of capacities Ann. Math. Artif. Intel. (IF 1.2) Pub Date : 2023-12-01 Peiqi Sun, Michel Grabisch, Christophe Labreuche
Capacity is an important tool in decision-making under risk and uncertainty and multi-criteria decision-making. When learning a capacity-based model, it is important to be able to generate uniformly a capacity. Due to the monotonicity constraints of a capacity, this task reveals to be very difficult. The classical Random Node Generator (RNG) algorithm is a fast-running speed capacity generator, however
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Modeling and shadowing paraconsistent BDI agents Ann. Math. Artif. Intel. (IF 1.2) Pub Date : 2023-11-23 Barbara Dunin-Kęplicz, Andrzej Szałas
The Bdi model of rational agency has been studied for over three decades. Many robust multiagent systems have been developed, and a number of Bdi logics have been studied. Following this intensive development phase, the importance of integrating Bdi models with inconsistency handling and revision theory have been emphasized. There is also a demand for a tighter connection between Bdi-based implementations
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Socially conscious stability for tiered coalition formation games Ann. Math. Artif. Intel. (IF 1.2) Pub Date : 2023-11-15 Nathan Arnold, Sarah Snider, Judy Goldsmith
We investigate Tiered Coalition Formation Games (TCFGs), a cooperative game inspired by the stratification of Pokémon on the fan website, Smogon. It is known that, under match-up oriented preferences, Nash and core stability are equivalent. We previously introduced a notion of socially conscious stability for TCFGs, and introduced a game variant with fixed k-length tier lists. In this work we show
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On the benefits of knowledge compilation for feature-model analyses Ann. Math. Artif. Intel. (IF 1.2) Pub Date : 2023-11-06 Chico Sundermann, Elias Kuiter, Tobias Heß, Heiko Raab, Sebastian Krieter, Thomas Thüm
Feature models are commonly used to specify the valid configurations of product lines. As industrial feature models are typically complex, researchers and practitioners employ various automated analyses to study the configuration spaces. Many of these automated analyses require that numerous complex computations are executed on the same feature model, for example by querying a SAT or #SATsolver. With
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A novel method for solving universum twin bounded support vector machine in the primal space Ann. Math. Artif. Intel. (IF 1.2) Pub Date : 2023-11-02 Hossein Moosaei, Saeed Khosravi, Fatemeh Bazikar, Milan Hladík, Mario Rosario Guarracino
In supervised learning, the Universum, a third class that is not a part of either class in the classification task, has proven to be useful. In this study we propose (N\( \mathfrak {U} \)TBSVM), a Newton-based approach for solving in the primal space the optimization problems related to Twin Bounded Support Vector Machines with Universum data (\( \mathfrak {U} \)TBSVM). In the N\( \mathfrak {U} \)TBSVM
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Families of multi-level Legendre-like arrays Ann. Math. Artif. Intel. (IF 1.2) Pub Date : 2023-11-02 Timothy Petersen, Benjamin Cavy, David Paganin, Imants Svalbe
Families of new, multi-level integer 2D arrays are introduced here as an extension of the well-known binary Legendre sequences that are derived from quadratic residues. We present a construction, based on Fourier and Finite Radon Transforms, for families of periodic perfect arrays, each of size \(p\times p\) for many prime values p. Previously delta functions were used as the discrete projections which
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Hedonic Expertise Games Ann. Math. Artif. Intel. (IF 1.2) Pub Date : 2023-10-16 Bugra Caskurlu, Fatih Erdem Kizilkaya, Berkehan Ozen
We introduce a hedonic game form, Hedonic Expertise Games (HEGs), that naturally models a variety of settings where agents with complementary qualities would like to form groups. Students forming groups for class projects, and hackathons in which software developers, graphic designers, project managers, and other domain experts collaborate on software projects, are typical scenarios modeled by HEGs
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On the universal approximation property of radial basis function neural networks Ann. Math. Artif. Intel. (IF 1.2) Pub Date : 2023-10-16 Aysu Ismayilova, Muhammad Ismayilov
In this paper we consider a new class of RBF (Radial Basis Function) neural networks, in which smoothing factors are replaced with shifts. We prove under certain conditions on the activation function that these networks are capable of approximating any continuous multivariate function on any compact subset of the d-dimensional Euclidean space. For RBF networks with finitely many fixed centroids we
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Automated programming, symbolic computation, machine learning: my personal view Ann. Math. Artif. Intel. (IF 1.2) Pub Date : 2023-10-10 Bruno Buchberger
In this note, I present my personal view on the interaction of the three areas Automated Programming, Symbolic Computation, and Machine Learning. Programming is the activity of finding a (hopefully) correct program (algorithm) for a given problem. Programming is central to automation in all areas and is considered one of the most creative human activities. However, already very early in the history
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Making model checking feasible for GOAL Ann. Math. Artif. Intel. (IF 1.2) Pub Date : 2023-10-05 Yi Yang, Tom Holvoet
Agent Programming Languages have been studied for over 20 years for programming complex decision-making for autonomous systems. The GOAL agent programming language is particularly interesting since it depends on automated planning based on beliefs and goals to determine behavior rather than preprogrammed planning by developers. Model checking is a powerful verification technique to guarantee the safety
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Parameter tuning of continuous Hopfield network applied to combinatorial optimization Ann. Math. Artif. Intel. (IF 1.2) Pub Date : 2023-09-22 Safae Rbihou, Nour-Eddine Joudar, Khalid Haddouch
The continuous Hopfield network (CHN) has provided a powerful approach to optimization problems and has shown good performance in different domains. However, two primary challenges still remain for this network: defining appropriate parameters and hyperparameters. In this study, our objective is to address these challenges and achieve optimal solutions for combinatorial optimization problems, thereby
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Path planning algorithm for mobile robots based on clustering-obstacles and quintic trigonometric Bézier curve Ann. Math. Artif. Intel. (IF 1.2) Pub Date : 2023-09-20 Vahide Bulut
Finding a collision-free feasible path for mobile robots is very important because they are essential in many fields such as healthcare, military, and industry. In this paper, a novel Clustering Obstacles (CO)-based path planning algorithm for mobile robots is presented using a quintic trigonometric Bézier curve and its two shape parameters. The CO-based algorithm forms clusters of geometrically shaped
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kNN Classification: a review Ann. Math. Artif. Intel. (IF 1.2) Pub Date : 2023-09-01 Panos K. Syriopoulos, Nektarios G. Kalampalikis, Sotiris B. Kotsiantis, Michael N. Vrahatis
The k-nearest neighbors (k/NN) algorithm is a simple yet powerful non-parametric classifier that is robust to noisy data and easy to implement. However, with the growing literature on k/NN methods, it is increasingly challenging for new researchers and practitioners to navigate the field. This review paper aims to provide a comprehensive overview of the latest developments in the k/NN algorithm, including
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Digital continuity of rotations in the 2D regular grids Ann. Math. Artif. Intel. (IF 1.2) Pub Date : 2023-08-29 Müge Saadetoğlu, Benedek Nagy, Aydın Avkan
A digitized rigid motion is called digitally continuous if two neighbor pixels still stay neighbors after the motion. This concept plays important role when people or computers (artificial intelligence, machine vision) need to recognize the object shown in the image. In this paper, digital rotations of a pixel with its closest neighbors are of our interest. We compare the neighborhood motion map results
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A combinatorial technique for generation of digital plane using GCD Ann. Math. Artif. Intel. (IF 1.2) Pub Date : 2023-08-28 Somrita Saha, Arindam Biswas
A digital plane is a digitization of a Euclidean plane. A plane is specified by its normal, which is a 3D vector with integer coordinates, as considered in this case. It is established here that a 3D digital straight line segment, shifted by an integer amount, can produce the digitized plane. 3D plane’s normals are classified based on the Greatest Common Divisor (GCD) of its components, and the net
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On hedonic games with common ranking property Ann. Math. Artif. Intel. (IF 1.2) Pub Date : 2023-08-23 Bugra Caskurlu, Fatih Erdem Kizilkaya
Hedonic games are a prominent model of coalition formation, in which each agent’s utility only depends on the coalition she resides. The subclass of hedonic games that models the formation of general partnerships (Larson 2018), where all affiliates receive the same utility, is referred to as hedonic games with common ranking property (HGCRP). Aside from their economic motivation, HGCRP came into prominence
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Realtime gray-box algorithm configuration using cost-sensitive classification Ann. Math. Artif. Intel. (IF 1.2) Pub Date : 2023-08-18 Dimitri Weiss, Kevin Tierney
A solver’s runtime and the quality of the solutions it generates are strongly influenced by its parameter settings. Finding good parameter configurations is a formidable challenge, even for fixed problem instance distributions. However, when the instance distribution can change over time, a once effective configuration may no longer provide adequate performance. Realtime algorithm configuration (RAC)
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Novel SVM-based classification approaches for evaluating pancreatic carcinoma Ann. Math. Artif. Intel. (IF 1.2) Pub Date : 2023-08-14 Ammon Washburn, Neng Fan, Hao Helen Zhang
In this paper, we develop two SVM-based classifiers named stable nested one-class support vector machines (SN-1SVMs) and decoupled margin-moment based SVMs (DMMB-SVMs), to predict the specific type of pancreatic carcinoma using quantitative histopathological signatures of images. For each patient, the diagnosis can produce hundreds of images, which can be used to classify the pancreatic tissues into
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To raise or not to raise: the autonomous learning rate question Ann. Math. Artif. Intel. (IF 1.2) Pub Date : 2023-08-08 Xiaomeng Dong, Tao Tan, Michael Potter, Yun-Chan Tsai, Gaurav Kumar, V. Ratna Saripalli, Theodore Trafalis
There is a parameter ubiquitous throughout the deep learning world: learning rate. There is likewise a ubiquitous question: what should that learning rate be? The true answer to this question is often tedious and time consuming to obtain, and a great deal of arcane knowledge has accumulated in recent years over how to pick and modify learning rates to achieve optimal training performance. Moreover
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Two parameter-tuned multi-objective evolutionary-based algorithms for zoning management in marine spatial planning Ann. Math. Artif. Intel. (IF 1.2) Pub Date : 2023-08-02 Mohadese Basirati, Romain Billot, Patrick Meyer
Strategic spatial planning is becoming more popular around the world as a decision-making way to build a unified vision for directing the medium- to long-term development of land/marine areas. Recently, the study of marine areas in terms of spatial planning such as Marine Spatial Planning (MSP) has received much attention. One of the challenging issues in MSP is to make a balance between determining
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A study on the predictive strength of fractal dimension of white and grey matter on MRI images in Alzheimer’s disease Ann. Math. Artif. Intel. (IF 1.2) Pub Date : 2023-08-01 Niccolò Di Marco, Azzurra di Palma, Andrea Frosini
Many recent studies have shown that Fractal Dimension (FD), a ratio for figuring out the complexity of a system given its measurements, can be used as an useful index to provide information about certain brain disease. Our research focuses on the Alzheimer’s disease changes in white and grey brain matters detected through the FD indexes of their contours. Data used in this study were obtained from
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Altruism in coalition formation games Ann. Math. Artif. Intel. (IF 1.2) Pub Date : 2023-07-31 Anna Maria Kerkmann, Simon Cramer, Jörg Rothe
Nguyen et al. (2016) introduced altruistic hedonic games in which agents’ utilities depend not only on their own preferences but also on those of their friends in the same coalition. We propose to extend their model to coalition formation games in general, considering also the friends in other coalitions. Comparing our model to altruistic hedonic games, we argue that excluding some friends from the
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Using answer set programming to deal with boolean networks and attractor computation: application to gene regulatory networks of cells Ann. Math. Artif. Intel. (IF 1.2) Pub Date : 2023-07-31 Tarek Khaled, Belaid Benhamou, Van-Giang Trinh
Deciphering gene regulatory networks’ functioning is an essential step for better understanding of life, as these networks play a fundamental role in the control of cellular processes. Boolean networks have been widely used to represent gene regulatory networks. They allow to describe the dynamics of complex gene regulatory networks straightforwardly and efficiently. The attractors are essential in
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An improved multi-task least squares twin support vector machine Ann. Math. Artif. Intel. (IF 1.2) Pub Date : 2023-07-27 Hossein Moosaei, Fatemeh Bazikar, Panos M. Pardalos
In recent years, multi-task learning (MTL) has become a popular field in machine learning and has a key role in various domains. Sharing knowledge across tasks in MTL can improve the performance of learning algorithms and enhance their generalization capability. A new approach called the multi-task least squares twin support vector machine (MTLS-TSVM) was recently proposed as a least squares variant
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MADTwin: a framework for multi-agent digital twin development: smart warehouse case study Ann. Math. Artif. Intel. (IF 1.2) Pub Date : 2023-07-25 Hussein Marah, Moharram Challenger
A Digital Twin (DT) is a frequently updated virtual representation of a physical or a digital instance that captures its properties of interest. Incorporating both cyber and physical parts to build a digital twin is challenging due to the high complexity of the requirements that should be addressed and satisfied during the design, implementation and operation. In this context, we introduce the MADTwin
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Agents and Digital Twins for the engineering of Cyber-Physical Systems: opportunities, and challenges Ann. Math. Artif. Intel. (IF 1.2) Pub Date : 2023-07-20 Stefano Mariani, Marco Picone, Alessandro Ricci
Digital Twins (DTs) are emerging as a fundamental brick of engineering Cyber-Physical Systems (CPSs), but their notion is still mostly bound to specific business domains (e.g. manufacturing), goals (e.g. product design), or applications (e.g. the Internet of Things). As such, their value as general purpose engineering abstractions is yet to be fully revealed. In this paper, we relate DTs with agents
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Bayesian optimization over the probability simplex Ann. Math. Artif. Intel. (IF 1.2) Pub Date : 2023-07-18 Antonio Candelieri, Andrea Ponti, Francesco Archetti
Gaussian Process based Bayesian Optimization is largely adopted for solving problems where the inputs are in Euclidean spaces. In this paper we associate the inputs to discrete probability distributions which are elements of the probability simplex. To search in the new design space, we need a distance between distributions. The optimal transport distance (aka Wasserstein distance) is chosen due to
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Modelling a chain of command in the incident command system using sequential characteristic function games Ann. Math. Artif. Intel. (IF 1.2) Pub Date : 2023-07-15 Tabajara Krausburg, Rafael H. Bordini, Jürgen Dix
Disaster response is a major challenge given the social and economic impact on the communities affected by disaster incidents. We investigate how coalition formation can be used for the problem of forming a hierarchy of resources (e.g., personnel responding to the incident). As a case study, we consider the roaring river flood scenario and model the Incident Command System (ICS) framework—providing
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Commitment-based negotiation semantics for accountability in multi-agent systems Ann. Math. Artif. Intel. (IF 1.2) Pub Date : 2023-07-08 Phillip Sloan, Nirav Ajmeri
Negotiation is a key form of interaction in multi-agent systems. Negotiation enables agents to come to a mutual agreement about a goal or plan of action. Current negotiation approaches use traditional interaction protocols which do not capture the normative meaning of interactions and often restrict agent autonomy. These traditional negotiation approaches also have difficulty specifying accountability
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Controlling weighted voting games by deleting or adding players with or without changing the quota Ann. Math. Artif. Intel. (IF 1.2) Pub Date : 2023-07-06 Joanna Kaczmarek, Jörg Rothe
Weighted voting games are a well-studied class of succinct simple games that can be used to model collective decision-making in, e.g., legislative bodies such as parliaments and shareholder voting. Power indices [1,2,3,4] are used to measure the influence of players in weighted voting games. In such games, it has been studied how a distinguished player’s power can be changed, e.g., by merging or splitting
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Local critical analysis of inequalities related to the sum of distances between n points on the unit hemisphere for $$n=4,5$$ Ann. Math. Artif. Intel. (IF 1.2) Pub Date : 2023-07-06 Yaochen Xu, Zhenbing Zeng, Jian Lu, Yuzheng Wang, Liangyu Chen
In this paper, we study a geometrical inequality conjecture which states that for any four points on a hemisphere with the unit radius, the largest sum of distances between the points is \(4+4\sqrt{2}\), the best configuration is a regular square inscribed to the equator, and for any five points, the largest sum is \(5\sqrt{5+2\sqrt{5}}\) and the best configuration is the regular pentagon inscribed
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A Novel Method for Solving Universum Twin Bounded Support Vector Machine in the Primal Space Ann. Math. Artif. Intel. (IF 1.2) Pub Date : 2023-07-01 Hossein Moosaei, Saeed Khosravi, Fatemeh Bazikar, Milan Hladík , Mario Rosario Guarracino
In supervised learning, the Universum, a third class that is not a part of either class in the classification task, has proven to be useful. In this study we propose (N\( \mathfrak {U} \)TBSVM), a Newton-based approach for solving in the primal space the optimization problems related to Twin Bounded Support Vector Machines with Universum data (\( \mathfrak {U} \)TBSVM). In the N\( \mathfrak {U} \)TBSVM
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Single MCMC chain parallelisation on decision trees Ann. Math. Artif. Intel. (IF 1.2) Pub Date : 2023-07-02 Efthyvoulos Drousiotis, Paul Spirakis
Decision trees (DT) are highly famous in machine learning and usually acquire state-of-the-art performance. Despite that, well-known variants like CART, ID3, random forest, and boosted trees miss a probabilistic version that encodes prior assumptions about tree structures and shares statistical strength between node parameters. Existing work on Bayesian DT depends on Markov Chain Monte Carlo (MCMC)
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A novel deep learning approach for one-step conformal prediction approximation Ann. Math. Artif. Intel. (IF 1.2) Pub Date : 2023-07-01 Julia A. Meister, Khuong An Nguyen, Stelios Kapetanakis, Zhiyuan Luo
Deep Learning predictions with measurable confidence are increasingly desirable for real-world problems, especially in high-risk settings. The Conformal Prediction (CP) framework is a versatile solution that guarantees a maximum error rate given minimal constraints [1]. In this paper, we propose a novel conformal loss function that approximates the traditionally two-step CP approach in a single step
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Search algorithms for automated negotiation in large domains Ann. Math. Artif. Intel. (IF 1.2) Pub Date : 2023-07-02 Thimjo Koça, Dave de Jonge, Tim Baarslag
This work presents several new and efficient algorithms that can be used by negotiating agents to explore very large outcome spaces. The proposed algorithms can search for bids close to a utility target or above a utility threshold, and for win-win outcomes. While doing so, these algorithms strike a careful balance between being rapid, accurate, diverse, and scalable, allowing agents to explore spaces
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Automated generation of illustrated proofs in geometry and beyond Ann. Math. Artif. Intel. (IF 1.2) Pub Date : 2023-07-03 Predrag Janičić, Julien Narboux
Illustrations are only rarely formal components of mathematical proofs, however they are often very important for understanding proofs. Illustrations are almost unavoidable in geometry, and in many other fields illustrations are helpful for carrying ideas in a more suitable way than via words or formulas. The question is: if we want to automate theorem proving, can we automate creation of corresponding
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Design and implementation of symbolic algorithms for the computation of generalized asymptotes Ann. Math. Artif. Intel. (IF 1.2) Pub Date : 2023-07-03 Marián Fernández de Sevilla, Rafael Magdalena Benedicto, Sonia Pérez-Díaz
In this paper we present two algorithms for computing the g-asymptotes or generalized asymptotes, of a plane algebraic curve, \(\mathscr {C}\), implicitly or parametrically defined. The asymptotes of a curve \(\mathscr {C}\) reflect the status of \(\mathscr {C}\) at points with sufficiently large coordinates. It is well known that an asymptote of a curve \(\mathscr {C}\) is a line such that the distance
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Lattice point of view for argumentation framework Ann. Math. Artif. Intel. (IF 1.2) Pub Date : 2023-06-17 Mohammed Elaroussi, Lhouari Nourine, Mohammed Said Radjef
The main purpose of this article is to develop a lattice point of view for the study of argumentation framework extensions. We first characterize self-defending sets of an argumentation framework by the closed sets of an implicational system that can be computed in polynomial time from the argumentation framework. On the other hand, for any implicational system \(\Sigma \) over the set of arguments