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Planning-based knowing how: A unified approach Artif. Intell. (IF 6.628) Pub Date : 2021-02-24 Yanjun Li; Yanjing Wang
Various logical notions of know-how have been recently proposed and studied in the literature based on different types of epistemic planning in different frameworks. This paper proposes a unified logical framework to incorporate the existing and some new notions of know-how. We define the semantics of the know-how operator using a unified notion of epistemic planning with parameters of different types
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A unifying look at sequence submodularity Artif. Intell. (IF 6.628) Pub Date : 2021-02-24 Sara Bernardini; Fabio Fagnani; Chiara Piacentini
Several real-world problems in engineering and applied science require the selection of sequences that maximize a given reward function. Optimizing over sequences as opposed to sets requires exploring an exponentially larger search space and can become prohibitive in most cases of practical interest. However, if the objective function is submodular (intuitively, it exhibits a diminishing return property)
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Ballooning Multi-armed Bandits Artif. Intell. (IF 6.628) Pub Date : 2021-02-24 Ganesh Ghalme; Swapnil Dhamal; Shweta Jain; Sujit Gujar; Y. Narahari
In this paper, we introduce ballooning multi-armed bandits (BL-MAB), a novel extension of the classical stochastic MAB model. In the BL-MAB model, the set of available arms grows (or balloons) over time. In contrast to the classical MAB setting where the regret is computed with respect to the best arm overall, the regret in a BL-MAB setting is computed with respect to the best available arm at each
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Using ontologies to enhance human understandability of global post-hoc explanations of black-box models Artif. Intell. (IF 6.628) Pub Date : 2021-02-15 Roberto Confalonieri; Tillman Weyde; Tarek R. Besold; Fermín Moscoso del Prado Martín
The interest in explainable artificial intelligence has grown strongly in recent years because of the need to convey safety and trust in the ‘how’ and ‘why’ of automated decision-making to users. While a plethora of approaches has been developed, only a few focus on how to use domain knowledge and how this influences the understanding of explanations by users. In this paper, we show that by using ontologies
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Enhanced aspect-based sentiment analysis models with progressive self-supervised attention learning Artif. Intell. (IF 6.628) Pub Date : 2021-02-19 Jinsong Su; Jialong Tang; Hui Jiang; Ziyao Lu; Yubin Ge; Linfeng Song; Deyi Xiong; Le Sun; Jiebo Luo
In aspect-based sentiment analysis (ABSA), many neural models are equipped with an attention mechanism to quantify the contribution of each context word to sentiment prediction. However, such a mechanism suffers from one drawback: only a few frequent words with sentiment polarities are tended to be taken into consideration for final sentiment decision while abundant infrequent sentiment words are ignored
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Predicting winner and estimating margin of victory in elections using sampling Artif. Intell. (IF 6.628) Pub Date : 2021-02-20 Arnab Bhattacharyya; Palash Dey
Predicting the winner of an election and estimating the margin of victory of that election are favorite problems both for news media pundits and computational social choice theorists. Since it is often infeasible to elicit the preferences of all the voters in a typical prediction scenario, a common algorithm used for predicting the winner and estimating the margin of victory is to run the election
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Differential privacy of hierarchical Census data: An optimization approach Artif. Intell. (IF 6.628) Pub Date : 2021-02-19 Ferdinando Fioretto; Pascal Van Hentenryck; Keyu Zhu
This paper is motivated by applications of a Census Bureau interested in releasing aggregate socio-economic data about a large population without revealing sensitive information about any individual. The released information can be the number of individuals living alone, the number of cars they own, or their salary brackets. Recent events have identified some of the privacy challenges faced by these
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Acceptance in incomplete argumentation frameworks Artif. Intell. (IF 6.628) Pub Date : 2021-02-10 Dorothea Baumeister; Matti Järvisalo; Daniel Neugebauer; Andreas Niskanen; Jörg Rothe
Abstract argumentation frameworks (AFs), originally proposed by Dung, constitute a central formal model for the study of computational aspects of argumentation in AI. Credulous and skeptical acceptance of arguments in a given AF are well-studied problems both in terms of theoretical analysis—especially computational complexity—and the development of practical decision procedures for the problems. However
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Open-world probabilistic databases: Semantics, algorithms, complexity Artif. Intell. (IF 6.628) Pub Date : 2021-02-15 İsmail İlkan Ceylan; Adnan Darwiche; Guy Van den Broeck
Large-scale probabilistic knowledge bases are becoming increasingly important in academia and industry. They are continuously extended with new data, powered by modern information extraction tools that associate probabilities with knowledge base facts. The state of the art to store and process such data is founded on probabilistic databases. Many systems based on probabilistic databases, however, still
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What do we want from Explainable Artificial Intelligence (XAI)? A stakeholder perspective on XAI and a conceptual model guiding interdisciplinary XAI research Artif. Intell. (IF 6.628) Pub Date : 2021-02-15 Markus Langer; Daniel Oster; Lena Kästner; Timo Speith; Kevin Baum; Holger Hermanns; Eva Schmidt; Andreas Sesing
Previous research in Explainable Artificial Intelligence (XAI) suggests that a main aim of explainability approaches is to satisfy specific interests, goals, expectations, needs, and demands regarding artificial systems (we call these “stakeholders' desiderata”) in a variety of contexts. However, the literature on XAI is vast, spreads out across multiple largely disconnected disciplines, and it often
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New width parameters for SAT and #SAT Artif. Intell. (IF 6.628) Pub Date : 2021-01-29 Robert Ganian; Stefan Szeider
We study the parameterized complexity of the propositional satisfiability (SAT) and the more general model counting (#SAT) problems and obtain novel fixed-parameter algorithms that exploit the structural properties of input formulas. In the first part of the paper, we parameterize by the treewidth of the following two graphs associated with CNF formulas: the consensus graph and the conflict graph.
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A review of possible effects of cognitive biases on interpretation of rule-based machine learning models Artif. Intell. (IF 6.628) Pub Date : 2021-01-26 Tomáš Kliegr; Štěpán Bahník; Johannes Fürnkranz
While the interpretability of machine learning models is often equated with their mere syntactic comprehensibility, we think that interpretability goes beyond that, and that human interpretability should also be investigated from the point of view of cognitive science. The goal of this paper is to discuss to what extent cognitive biases may affect human understanding of interpretable machine learning
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Learning Modulo Theories for constructive preference elicitation Artif. Intell. (IF 6.628) Pub Date : 2021-01-21 Paolo Campigotto; Stefano Teso; Roberto Battiti; Andrea Passerini
This paper introduces CLEO, a novel preference elicitation algorithm capable of recommending complex configurable objects characterized by both discrete and continuous attributes and constraints defined over them. While existing preference elicitation techniques focus on searching for the best instance in a database of candidates, CLEO takes a constructive approach to recommendation through interactive
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Counterfactual state explanations for reinforcement learning agents via generative deep learning Artif. Intell. (IF 6.628) Pub Date : 2021-01-27 Matthew L. Olson; Roli Khanna; Lawrence Neal; Fuxin Li; Weng-Keen Wong
Counterfactual explanations, which deal with “why not?” scenarios, can provide insightful explanations to an AI agent's behavior [Miller [38]]. In this work, we focus on generating counterfactual explanations for deep reinforcement learning (RL) agents which operate in visual input environments like Atari. We introduce counterfactual state explanations, a novel example-based approach to counterfactual
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An integrated approach to solving influence diagrams and finite-horizon partially observable decision processes Artif. Intell. (IF 6.628) Pub Date : 2020-12-02 Eric A. Hansen
We show how to integrate a variable elimination approach to solving influence diagrams with a value iteration approach to solving finite-horizon partially observable Markov decision processes (POMDPs). The integration of these approaches creates a variable elimination algorithm for influence diagrams that has much more relaxed constraints on elimination order, which allows improved scalability in many
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Explaining black-box classifiers using post-hoc explanations-by-example: The effect of explanations and error-rates in XAI user studies Artif. Intell. (IF 6.628) Pub Date : 2021-01-26 Eoin M. Kenny; Courtney Ford; Molly Quinn; Mark T. Keane
In this paper, we describe a post-hoc explanation-by-example approach to eXplainable AI (XAI), where a black-box, deep learning system is explained by reference to a more transparent, proxy model (in this situation a case-based reasoner), based on a feature-weighting analysis of the former that is used to find explanatory cases from the latter (as one instance of the so-called Twin Systems approach)
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Epistemic GDL: A logic for representing and reasoning about imperfect information games Artif. Intell. (IF 6.628) Pub Date : 2021-01-19 Guifei Jiang; Dongmo Zhang; Laurent Perrussel; Heng Zhang
This paper proposes a logical framework for representing and reasoning about imperfect information games. We first extend Game Description Language (GDL) with the standard epistemic operators and provide it with a semantics based on the epistemic state transition model. We then demonstrate how to use the language to represent the rules of an imperfect information game and formalize common game properties
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GLocalX - From Local to Global Explanations of Black Box AI Models Artif. Intell. (IF 6.628) Pub Date : 2021-01-22 Mattia Setzu; Riccardo Guidotti; Anna Monreale; Franco Turini; Dino Pedreschi; Fosca Giannotti
Artificial Intelligence (AI) has come to prominence as one of the major components of our society, with applications in most aspects of our lives. In this field, complex and highly nonlinear machine learning models such as ensemble models, deep neural networks, and Support Vector Machines have consistently shown remarkable accuracy in solving complex tasks. Although accurate, AI models often are “black
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Show or suppress? Managing input uncertainty in machine learning model explanations Artif. Intell. (IF 6.628) Pub Date : 2021-01-27 Danding Wang; Wencan Zhang; Brian Y. Lim
Feature attribution is widely used in interpretable machine learning to explain how influential each measured input feature value is for an output inference. However, measurements can be uncertain, and it is unclear how the awareness of input uncertainty can affect the trust in explanations. We propose and study two approaches to help users to manage their perception of uncertainty in a model explanation:
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A semantics for Hybrid Probabilistic Logic programs with function symbols Artif. Intell. (IF 6.628) Pub Date : 2021-01-12 Damiano Azzolini; Fabrizio Riguzzi; Evelina Lamma
Probabilistic Logic Programming (PLP) is a powerful paradigm for the representation of uncertain relations among objects. Recently, programs with continuous variables, also called hybrid programs, have been proposed and assigned a semantics. Hybrid programs are capable of representing real-world measurements but unfortunately the semantics proposal was imprecise so the definition did not assign a probability
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On the noise estimation statistics Artif. Intell. (IF 6.628) Pub Date : 2021-01-08 Wei Gao; Teng Zhang; Bin-Bin Yang; Zhi-Hua Zhou
Learning with noisy labels has attracted much attention during the past few decades. A fundamental problem is how to estimate noise proportions from corrupted data. Previous studies on this issue resort to the estimations of class distributions, conditional distributions, or the kernel embedding of distributions. In this paper, we present another simple and effective approach for noise estimation.
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Credibility Dynamics: A belief-revision-based trust model with pairwise comparisons Artif. Intell. (IF 6.628) Pub Date : 2021-01-07 David Jelenc; Luciano H. Tamargo; Sebastian Gottifredi; Alejandro J. García
Trust models have become invaluable in dynamic scenarios, such as Internet applications, since they provide means for estimating trustworthiness of potential interaction counterparts. Currently, the majority of trust models require ratings to be expressed absolutely, that is as values from some predefined scale. However, literature shows that expressing ratings absolutely can be challenging for users
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Multiple object tracking: A literature review Artif. Intell. (IF 6.628) Pub Date : 2020-12-30 Wenhan Luo; Junliang Xing; Anton Milan; Xiaoqin Zhang; Wei Liu; Tae-Kyun Kim
Multiple Object Tracking (MOT) has gained increasing attention due to its academic and commercial potential. Although different approaches have been proposed to tackle this problem, it still remains challenging due to factors like abrupt appearance changes and severe object occlusions. In this work, we contribute the first comprehensive and most recent review on this problem. We inspect the recent
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Making sense of sensory input Artif. Intell. (IF 6.628) Pub Date : 2021-01-05 Richard Evans; José Hernández-Orallo; Johannes Welbl; Pushmeet Kohli; Marek Sergot
This paper attempts to answer a central question in unsupervised learning: what does it mean to “make sense” of a sensory sequence? In our formalization, making sense involves constructing a symbolic causal theory that both explains the sensory sequence and also satisfies a set of unity conditions. The unity conditions insist that the constituents of the causal theory – objects, properties, and laws
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Fair division of mixed divisible and indivisible goods Artif. Intell. (IF 6.628) Pub Date : 2021-01-05 Xiaohui Bei; Zihao Li; Jinyan Liu; Shengxin Liu; Xinhang Lu
We study the problem of fair division when the set of resources contains both divisible and indivisible goods. Classic fairness notions such as envy-freeness (EF) and envy-freeness up to one good (EF1) cannot be directly applied to this mixed goods setting. In this work, we propose a new fairness notion, envy-freeness for mixed goods (EFM), which is a direct generalization of both EF and EF1 to the
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Computational Complexity of Flat and Generic Assumption-Based Argumentation, with and without Probabilities Artif. Intell. (IF 6.628) Pub Date : 2021-01-05 Kristijonas Čyras; Quentin Heinrich; Francesca Toni
Reasoning with probabilistic information has recently attracted considerable attention in argumentation, and formalisms of Probabilistic Abstract Argumentation (PAA), Probabilistic Bipolar Argumentation (PBA) and Probabilistic Structured Argumentation (PSA) have been proposed. These foundational advances have been complemented with investigations on the complexity of some approaches to PAA and PBA
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A Lightweight Epistemic Logic and its Application to Planning Artif. Intell. (IF 6.628) Pub Date : 2020-12-28 Martin C. Cooper; Andreas Herzig; Faustine Maffre; Frédéric Maris; Elise Perrotin; Pierre Régnier
We study multiagent epistemic planning with a simple epistemic logic whose language is a restriction of that of standard epistemic logic. Its formulas are boolean combinations of observability atoms: sequences of ‘knowing whether’ operators followed by propositional variables. This compares favourably with other restricted languages where formulas are boolean combinations of epistemic literals: sequences
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Embedding deep networks into visual explanations Artif. Intell. (IF 6.628) Pub Date : 2020-12-02 Zhongang Qi; Saeed Khorram; Li Fuxin
In this paper, we propose a novel Explanation Neural Network (XNN) to explain the predictions made by a deep network. The XNN works by learning a nonlinear embedding of a high-dimensional activation vector of a deep network layer into a low-dimensional explanation space while retaining faithfulness i.e., the original deep learning predictions can be constructed from the few concepts extracted by our
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Spatial relation learning for explainable image classification and annotation in critical applications Artif. Intell. (IF 6.628) Pub Date : 2020-12-02 Régis Pierrard; Jean-Philippe Poli; Céline Hudelot
With the recent successes of black-box models in Artificial Intelligence (AI) and the growing interactions between humans and AIs, explainability issues have risen. In this article, in the context of high-stake applications, we propose an approach for explainable classification and annotation of images. It is based on a transparent model, whose reasoning is accessible and human understandable, and
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Game description language and dynamic epistemic logic compared Artif. Intell. (IF 6.628) Pub Date : 2020-12-01 Thorsten Engesser; Robert Mattmüller; Bernhard Nebel; Michael Thielscher
Several different frameworks have been proposed to model and reason about knowledge in dynamic multi-agent settings, among them the logic-programming-based game description language GDL-III and dynamic epistemic logic (DEL). GDL-III and DEL have complementary strengths and weaknesses in terms of ease of modeling and simplicity of semantics. In this paper, we formally study the expressiveness of GDL-III
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Metamodeling and metaquerying in OWL 2 QL Artif. Intell. (IF 6.628) Pub Date : 2020-11-30 Maurizio Lenzerini; Lorenzo Lepore; Antonella Poggi
OWL 2 QL is a standard profile of the OWL 2 ontology language, specifically tailored to Ontology-Based Data Management. Inspired by recent work on higher-order Description Logics, in this paper we present a new semantics for OWL 2 QL ontologies, called Metamodeling Semantics (MS), and show that, in contrast to the official Direct Semantics (DS) for OWL 2, it allows exploiting the metamodeling capabilities
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Weakly-supervised sensor-based activity segmentation and recognition via learning from distributions Artif. Intell. (IF 6.628) Pub Date : 2020-11-30 Hangwei Qian; Sinno Jialin Pan; Chunyan Miao
Sensor-based activity recognition aims to recognize users' activities from multi-dimensional streams of sensor readings received from ubiquitous sensors. It has been shown that data segmentation and feature extraction are two crucial steps in developing machine learning-based models for sensor-based activity recognition. However, most previous studies were only focused on the latter step by assuming
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Time-delayed collective flow diffusion models for inferring latent people flow from aggregated data at limited locations Artif. Intell. (IF 6.628) Pub Date : 2020-11-20 Yusuke Tanaka; Tomoharu Iwata; Takeshi Kurashima; Hiroyuki Toda; Naonori Ueda; Toshiyuki Tanaka
The rapid adoption of wireless sensor devices has made it easier to record location information of people in a variety of spaces (e.g., exhibition halls). Location information is often aggregated due to privacy and/or cost concerns. The aggregated data we use as input consist of the numbers of incoming and outgoing people at each location and at each time step. Since the aggregated data lack tracking
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Using POMDPs for learning cost sensitive decision trees Artif. Intell. (IF 6.628) Pub Date : 2020-11-11 Shlomi Maliah; Guy Shani
In classification, an algorithm learns to classify a given instance based on a set of observed attribute values. In many real world cases testing the value of an attribute incurs a cost. Furthermore, there can also be a cost associated with the misclassification of an instance. Cost sensitive classification attempts to minimize the expected cost of classification, by deciding after each observed attribute
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Dependency-based syntax-aware word representations Artif. Intell. (IF 6.628) Pub Date : 2020-11-16 Meishan Zhang; Zhenghua Li; Guohong Fu; Min Zhang
Dependency syntax has been demonstrated highly useful for a number of natural language processing (NLP) tasks. Typical approaches of utilizing dependency syntax include Tree-RNN and Tree-Linearization, both of which exploit explicit 1-best tree outputs from a well-trained parser as inputs. However, these approaches may suffer from error propagation due to the inevitable errors contained in the 1-best
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Evaluating local explanation methods on ground truth Artif. Intell. (IF 6.628) Pub Date : 2020-11-14 Riccardo Guidotti
Evaluating local explanation methods is a difficult task due to the lack of a shared and universally accepted definition of explanation. In the literature, one of the most common ways to assess the performance of an explanation method is to measure the fidelity of the explanation with respect to the classification of a black box model adopted by an Artificial Intelligent system for making a decision
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X*: Anytime Multi-Agent Path Finding for Sparse Domains using Window-Based Iterative Repairs Artif. Intell. (IF 6.628) Pub Date : 2020-11-17 Kyle Vedder; Joydeep Biswas
Real-world multi-agent systems such as warehouse robots operate under significant time constraints – in such settings, rather than spending significant amounts of time solving for optimal paths, it is instead preferable to find valid, collision-free paths quickly, even if suboptimal, and given additional time, to iteratively refine such paths to improve their cost. In such domains, we observe that
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Mis- and disinformation in a bounded confidence model Artif. Intell. (IF 6.628) Pub Date : 2020-11-09 Igor Douven; Rainer Hegselmann
The bounded confidence model has been widely used to formally study groups of agents who are sharing opinions with those in their epistemic neighborhood. We revisit the model with an eye toward studying mis- and disinformation campaigns, which have been much in the news of late. To that end, we introduce typed agents into the model, specifically agents who can be irresponsible in different ways, most
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Selecting goals in oversubscription planning using relaxed plans Artif. Intell. (IF 6.628) Pub Date : 2020-11-04 Angel García-Olaya; Tomás de la Rosa; Daniel Borrajo
Planning deals with the task of finding an ordered set of actions that achieves some goals from an initial state. In many real-world applications it is unfeasible to find a plan achieving all goals due to limitations in the available resources. A common case consists of having a bound on a given cost measure that is less than the optimal cost needed to achieve all goals. Oversubscription planning (OSP)
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Dynamically improved bounds bidirectional search Artif. Intell. (IF 6.628) Pub Date : 2020-11-09 E.C. Sewell; S.H. Jacobson
This paper presents a bidirectional search algorithm that dynamically improves the bounds during its execution. It has the property that it always terminates on or before the forward search meets the backward search. Computational experiments on the pancake problem, the sliding tile puzzle, and the topspin problem demonstrate that it is capable of solving problems using significantly fewer node expansions
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Stable Fractional Matchings Artif. Intell. (IF 6.628) Pub Date : 2020-11-06 Ioannis Caragiannis; Aris Filos-Ratsikas; Panagiotis Kanellopoulos; Rohit Vaish
We study a generalization of the classical stable matching problem that allows for cardinal preferences (as opposed to ordinal) and fractional matchings (as opposed to integral). In this cardinal setting, stable fractional matchings can have much larger social welfare than stable integral ones. Our goal is to understand the computational complexity of finding an optimal (i.e., welfare-maximizing) stable
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On fair price discrimination in multi-unit markets Artif. Intell. (IF 6.628) Pub Date : 2020-09-18 Michele Flammini; Manuel Mauro; Matteo Tonelli
Discriminatory pricing policies, even if often perceived as unfair, are widespread. In fact, pricing differences for the same item among different national markets are common, or forms of discrimination based on the time of purchase, like in tickets' sales. In this work, we propose a framework for capturing “fair” price discrimination policies that can be tolerated by customers, and study its application
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Robustness among multiwinner voting rules Artif. Intell. (IF 6.628) Pub Date : 2020-10-28 Robert Bredereck; Piotr Faliszewski; Andrzej Kaczmarczyk; Rolf Niedermeier; Piotr Skowron; Nimrod Talmon
We investigate how robust the results of committee elections are with respect to small changes in the input preference orders, depending on the voting rules used. We find that for typical rules the effect of making a single swap of adjacent candidates in a single preference order is either that (1) at most one committee member might be replaced, or (2) it is possible that the whole committee will be
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A reconstruction of multipreference closure Artif. Intell. (IF 6.628) Pub Date : 2020-10-12 Laura Giordano; Valentina Gliozzi
The paper describes a preferential approach for dealing with exceptions in KLM preferential logics, based on the rational closure. It is well known that rational closure does not allow an independent handling of inheritance of different defeasible properties of concepts. In this work, we consider an alternative closure construction, called Multi Preference closure (MP-closure), which has been first
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Protecting elections by recounting ballots Artif. Intell. (IF 6.628) Pub Date : 2020-10-16 Edith Elkind; Jiarui Gan; Svetlana Obraztsova; Zinovi Rabinovich; Alexandros A. Voudouris
Complexity of voting manipulation is a prominent topic in computational social choice. In this work, we consider a two-stage voting manipulation scenario. First, a malicious party (an attacker) attempts to manipulate the election outcome in favor of a preferred candidate by changing the vote counts in some of the voting districts. Afterwards, another party (a defender), which cares about the voters'
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Pruning external minimality checking for answer set programs using semantic dependencies Artif. Intell. (IF 6.628) Pub Date : 2020-10-16 Thomas Eiter; Tobias Kaminski
Answer set programming (ASP) has become an increasingly popular approach for declarative problem solving. In order to address the needs of applications, ASP has been extended in different approaches with means for interfacing the outside world, of which hex programs are one of the most powerful such extension that provides API-style interfaces to access arbitrary external sources of information and
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Evaluating XAI: A comparison of rule-based and example-based explanations Artif. Intell. (IF 6.628) Pub Date : 2020-10-28 Jasper van der Waa; Elisabeth Nieuwburg; Anita Cremers; Mark Neerincx
Current developments in Artificial Intelligence (AI) led to a resurgence of Explainable AI (XAI). New methods are being researched to obtain information from AI systems in order to generate explanations for their output. However, there is an overall lack of valid and reliable evaluations of the effects on user's experience and behaviour of explanations. New XAI methods are often based on an intuitive
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Quantifying controllability in temporal networks with uncertainty Artif. Intell. (IF 6.628) Pub Date : 2020-09-23 Shyan Akmal; Savana Ammons; Hemeng Li; Michael Gao; Lindsay Popowski; James C. Boerkoel
Controllability for Simple Temporal Networks with Uncertainty (STNUs) has thus far been limited to three levels: strong, dynamic, and weak. Because of this, there is currently no systematic way for an agent to assess just how far from being controllable an uncontrollable STNU is. We provide new insights inspired by a geometric interpretation of STNUs to introduce the degrees of strong and dynamic controllability
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On composition of bounded-recall plans Artif. Intell. (IF 6.628) Pub Date : 2020-10-07 Kaya Deuser; Pavel Naumov
The article studies the ability of agents with bounded memory to execute consecutive composition of plans. It gives an upper limit on the amount of memory required to execute the composed plans and shows that the limit cannot be improved. Furthermore, the article shows that there are, essentially, no other universal properties of plans for bounded-recall agents expressible through the relation “there
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Understanding the power of Max-SAT resolution through UP-resilience Artif. Intell. (IF 6.628) Pub Date : 2020-10-06 Mohamed Sami Cherif; Djamal Habet; André Abramé
A typical Branch and Bound algorithm for Max-SAT computes the lower bound by estimating the number of disjoint Inconsistent Subsets (IS) of the formula. The IS detection is ensured by Simulated Unit Propagation (SUP). Then, the inference rule for Max-SAT, Max-SAT resolution, is applied to ensure that the detected IS is counted only once. Learning Max-SAT resolution transformations can be detrimental
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SAT-based explicit LTLf satisfiability checking Artif. Intell. (IF 6.628) Pub Date : 2020-08-18 Jianwen Li; Geguang Pu; Yueling Zhang; Moshe Y. Vardi; Kristin Y. Rozier
Linear Temporal Logic over finite traces (LTLf) was proposed in 2013 and has attracted increasing interest around the AI community. Though the theoretic basis for LTLf has been thoroughly explored since that time, there are still few algorithmic tools that are able to provide an efficient reasoning strategy for LTLf. In this paper, we present a SAT-based framework for LTLf satisfiability checking,
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Real-time reasoning in OWL2 for GDPR compliance Artif. Intell. (IF 6.628) Pub Date : 2020-09-18 Piero A. Bonatti; Luca Ioffredo; Iliana M. Petrova; Luigi Sauro; Ida R. Siahaan
This paper shows how knowledge representation and reasoning techniques can be used to support organizations in complying with the GDPR, that is, the new European data protection regulation. This work is carried out in a European H2020 project called SPECIAL. Data usage policies, the consent of data subjects, and selected fragments of the GDPR are encoded in a fragment of OWL2 called PL (policy language);
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Explanation in AI and law: Past, present and future Artif. Intell. (IF 6.628) Pub Date : 2020-09-16 Katie Atkinson; Trevor Bench-Capon; Danushka Bollegala
Explanation has been a central feature of AI systems for legal reasoning since their inception. Recently, the topic of explanation of decisions has taken on a new urgency, throughout AI in general, with the increasing deployment of AI tools and the need for lay users to be able to place trust in the decisions that the support tools are recommending. This paper provides a comprehensive review of the
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Intrinsic approaches to prioritizing diagnoses in multi-context systems Artif. Intell. (IF 6.628) Pub Date : 2020-09-14 Kedian Mu
Multi-context systems introduced by Brewka and Eiter provide a promising framework for interlinking heterogeneous and autonomous knowledge sources. The notion of diagnosis has been proposed for analyzing inconsistency in multi-context systems, which captures a pair of subsets of bridge rules of a multi-context system needed to be deactivated and activated unconditionally, respectively, in order to
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So, what exactly is a qualitative calculus? Artif. Intell. (IF 6.628) Pub Date : 2020-09-14 Armen Inants; Jérôme Euzenat
The paradigm of algebraic constraint-based reasoning, embodied in the notion of a qualitative calculus, is studied within two alternative frameworks. One framework defines a qualitative calculus as “a non-associative relation algebra (NA) with a qualitative representation”, the other as “an algebra generated by jointly exhaustive and pairwise disjoint (JEPD) relations”. These frameworks provide complementary
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Dashed strings for string constraint solving Artif. Intell. (IF 6.628) Pub Date : 2020-09-05 Roberto Amadini; Graeme Gange; Peter J. Stuckey
String processing is ubiquitous across computer science, and arguably more so in web programming — where it is also a critical part of security issues such as injection attacks. In recent years, a number of string solvers have been developed to solve combinatorial problems involving string variables and constraints. We examine the dashed string approach to string constraint solving, which represents
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Autoepistemic answer set programming Artif. Intell. (IF 6.628) Pub Date : 2020-09-01 Pedro Cabalar; Jorge Fandinno; Luis Fariñas del Cerro
Defined by Gelfond in 1991, epistemic specifications constitute an extension of Answer Set Programming (ASP) that introduces subjective literals. A subjective literal allows checking whether some regular literal is true in all (or in some of) the answer sets of the program, that are further collected in a set called world view. One epistemic program may yield several world views but, under the original
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When security games hit traffic: A deployed optimal traffic enforcement system Artif. Intell. (IF 6.628) Pub Date : 2020-09-01 Ariel Rosenfeld; Oleg Maksimov; Sarit Kraus
Road accidents are the leading causes of death among youths and young adults worldwide. Efficient traffic enforcement is an essential, yet complex, component in preventing road accidents. In this article, we present a novel model, an optimizing algorithm and a deployed system which together mitigate many of the computational and real-world challenges of traffic enforcement allocation in large road
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Probability pooling for dependent agents in collective learning Artif. Intell. (IF 6.628) Pub Date : 2020-08-19 Jonathan Lawry; Chanelle Lee
The use of copulas is proposed as a way of modelling dependencies between different agents' probability judgements when carrying out probability pooling. This is combined with an established Bayesian model in which pooling is viewed as a form of updating on the basis of probability values provided by different individuals. Adopting the Frank family of copulas we investigate the effect of different
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Fixed point semantics for stream reasoning Artif. Intell. (IF 6.628) Pub Date : 2020-08-18 Christian Antić
Reasoning over streams of input data is an essential part of human intelligence. During the last decade stream reasoning has emerged as a research area within the AI-community with many potential applications. In fact, the increased availability of streaming data via services like Google and Facebook has raised the need for reasoning engines coping with data that changes at high rate. Recently, the
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