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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 (policy language);

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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 (G91), 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

  • 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

  • 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

  • Interestingness elements for explainable reinforcement learning: Understanding agents' capabilities and limitations
    Artif. Intell. (IF 6.628) Pub Date : 2020-08-12
    Pedro Sequeira; Melinda Gervasio

    We propose an explainable reinforcement learning (XRL) framework that analyzes an agent's history of interaction with the environment to extract interestingness elements that help explain its behavior. The framework relies on data readily available from standard RL algorithms, augmented with data that can easily be collected by the agent while learning. We describe how to create visual summaries of

  • Knowledge-based programs as succinct policies for partially observable domains
    Artif. Intell. (IF 6.628) Pub Date : 2020-08-14
    Bruno Zanuttini; Jérôme Lang; Abdallah Saffidine; François Schwarzentruber

    We suggest to express policies for contingent planning by knowledge-based programs (KBPs). KBPs, introduced by Fagin et al. (1995) [32], are high-level protocols describing the actions that the agent should perform as a function of their current knowledge: branching conditions are epistemic formulas that are interpretable by the agent. The main aim of our paper is to show that KBPs can be seen as a

  • 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,

  • Utilitarian welfare and representation guarantees of approval-based multiwinner rules
    Artif. Intell. (IF 6.628) Pub Date : 2020-08-05
    Martin Lackner; Piotr Skowron

    To choose a suitable multiwinner voting rule is a hard and ambiguous task. Depending on the context, it varies widely what constitutes the choice of an “optimal” subset of alternatives. In this paper, we provide a quantitative analysis of multiwinner voting rules using methods from the theory of approximation algorithms—we estimate how well multiwinner rules approximate two extreme objectives: a representation

  • Price of Pareto Optimality in hedonic games
    Artif. Intell. (IF 6.628) Pub Date : 2020-07-27
    Edith Elkind; Angelo Fanelli; Michele Flammini

    The Price of Anarchy measures the welfare loss caused by selfish behavior: it is defined as the ratio of the social welfare in a socially optimal outcome and in a worst Nash equilibrium. Similar measures can be derived for other classes of stable outcomes. We observe that Pareto optimality can be seen as a notion of stability: an outcome is Pareto optimal if and only if it does not admit a deviation

  • Negotiating team formation using deep reinforcement learning
    Artif. Intell. (IF 6.628) Pub Date : 2020-07-25
    Yoram Bachrach; Richard Everett; Edward Hughes; Angeliki Lazaridou; Joel Z. Leibo; Marc Lanctot; Michael Johanson; Wojciech M. Czarnecki; Thore Graepel

    When autonomous agents interact in the same environment, they must often cooperate to achieve their goals. One way for agents to cooperate effectively is to form a team, make a binding agreement on a joint plan, and execute it. However, when agents are self-interested, the gains from team formation must be allocated appropriately to incentivize agreement. Various approaches for multi-agent negotiation

  • Combining experts' causal judgments
    Artif. Intell. (IF 6.628) Pub Date : 2020-07-13
    Dalal Alrajeh; Hana Chockler; Joseph Y. Halpern

    Consider a policymaker who wants to decide which intervention to perform in order to change a currently undesirable situation. The policymaker has at her disposal a team of experts, each with their own understanding of the causal dependencies between different factors contributing to the outcome. The policymaker has varying degrees of confidence in the experts' opinions. She wants to combine their

  • A technical survey on statistical modelling and design methods for crowdsourcing quality control
    Artif. Intell. (IF 6.628) Pub Date : 2020-06-30
    Yuan Jin; Mark Carman; Ye Zhu; Yong Xiang

    Online crowdsourcing provides a scalable and inexpensive means to collect knowledge (e.g. labels) about various types of data items (e.g. text, audio, video). However, it is also known to result in large variance in the quality of recorded responses which often cannot be directly used for training machine learning systems. To resolve this issue, a lot of work has been conducted to control the response

  • An approach for combining ethical principles with public opinion to guide public policy
    Artif. Intell. (IF 6.628) Pub Date : 2020-06-16
    Edmond Awad; Michael Anderson; Susan Leigh Anderson; Beishui Liao

    We propose a framework for incorporating public opinion into policy making in situations where values are in conflict. This framework advocates creating vignettes representing value choices, eliciting the public's opinion on these choices, and using machine learning to extract principles that can serve as succinct statements of the policies implied by these choices and rules to guide the behavior of

  • DEL-based epistemic planning: Decidability and complexity
    Artif. Intell. (IF 6.628) Pub Date : 2020-06-22
    Thomas Bolander; Tristan Charrier; Sophie Pinchinat; François Schwarzentruber

    Epistemic planning can be used for decision making in multi-agent systems with distributed knowledge and capabilities. Dynamic Epistemic Logic (DEL) has been shown to provide a very natural and expressive framework for epistemic planning. In this paper, we present a systematic overview of known complexity and decidability results for epistemic planning based on DEL, as well as provide some new results

  • Probabilistic reasoning about epistemic action narratives
    Artif. Intell. (IF 6.628) Pub Date : 2020-06-29
    Fabio Aurelio D'Asaro; Antonis Bikakis; Luke Dickens; Rob Miller

    We propose the action language EPEC – Epistemic Probabilistic Event Calculus – that supports probabilistic, epistemic reasoning about narratives of action occurrences and environmentally triggered events, and in particular facilitates reasoning about future belief-conditioned actions and their consequences in domains that include both perfect and imperfect sensing actions. To provide a declarative

  • Automated temporal equilibrium analysis: Verification and synthesis of multi-player games
    Artif. Intell. (IF 6.628) Pub Date : 2020-06-26
    Julian Gutierrez; Muhammad Najib; Giuseppe Perelli; Michael Wooldridge

    In the context of multi-agent systems, the rational verification problem is concerned with checking which temporal logic properties will hold in a system when its constituent agents are assumed to behave rationally and strategically in pursuit of individual objectives. Typically, those objectives are expressed as temporal logic formulae which the relevant agent desires to see satisfied. Unfortunately

  • Old techniques in new ways: Clause weighting, unit propagation and hybridization for maximum satisfiability
    Artif. Intell. (IF 6.628) Pub Date : 2020-06-29
    Shaowei Cai; Zhendong Lei

    Maximum Satisfiability (MaxSAT) is a basic and important constraint optimization problem. When dealing with both hard and soft constraints, the MaxSAT problem is referred to as Partial MaxSAT, which has been used to effectively solve many combinatorial optimization problems in real world. The local search method and the SAT-based method are two popular methods for Partial MaxSAT. Nevertheless, local

  • Evaluation of the moral permissibility of action plans
    Artif. Intell. (IF 6.628) Pub Date : 2020-06-16
    Felix Lindner; Robert Mattmüller; Bernhard Nebel

    Research in classical planning so far has been mainly concerned with generating a satisficing or an optimal plan. However, if such systems are used to make decisions that are relevant to humans, one should also consider the ethical consequences generated plans can have. Traditionally, ethical principles are formulated in an action-based manner, allowing to judge the execution of one action. We show

  • Designing normative theories for ethical and legal reasoning: LogiKEy framework, methodology, and tool support
    Artif. Intell. (IF 6.628) Pub Date : 2020-06-12
    Christoph Benzmüller; Xavier Parent; Leendert van der Torre

    A framework and methodology—termed LogiKEy—for the design and engineering of ethical reasoners, normative theories and deontic logics is presented. The overall motivation is the development of suitable means for the control and governance of intelligent autonomous systems. LogiKEy's unifying formal framework is based on semantical embeddings of deontic logics, logic combinations and ethico-legal domain

  • Memetic algorithms outperform evolutionary algorithms in multimodal optimisation
    Artif. Intell. (IF 6.628) Pub Date : 2020-06-11
    Phan Trung Hai Nguyen; Dirk Sudholt

    Memetic algorithms integrate local search into an evolutionary algorithm to combine the advantages of rapid exploitation and global optimisation. We provide a rigorous runtime analysis of memetic algorithms on the Hurdle problem, a landscape class of tunable difficulty with a “big valley structure”, a characteristic feature of many hard combinatorial optimisation problems. A parameter called hurdle

  • Boolean algebras of conditionals, probability and logic
    Artif. Intell. (IF 6.628) Pub Date : 2020-06-10
    Tommaso Flaminio; Lluis Godo; Hykel Hosni

    This paper presents an investigation on the structure of conditional events and on the probability measures which arise naturally in that context. In particular we introduce a construction which defines a (finite) Boolean algebra of conditionals from any (finite) Boolean algebra of events. By doing so we distinguish the properties of conditional events which depend on probability and those which are

  • Effective footstep planning using homotopy-class guidance
    Artif. Intell. (IF 6.628) Pub Date : 2020-06-10
    Vinitha Ranganeni; Sahit Chintalapudi; Oren Salzman; Maxim Likhachev

    Planning the motion for humanoid robots is a computationally-complex task due to the high dimensionality of the system. Thus, a common approach is to first plan in the low-dimensional space induced by the robot's feet—a task referred to as footstep planning. This low-dimensional plan is then used to guide the full motion of the robot. One approach that has proven successful in footstep planning is

  • Handling and measuring inconsistency in non-monotonic logics
    Artif. Intell. (IF 6.628) Pub Date : 2020-06-10
    Markus Ulbricht; Matthias Thimm; Gerhard Brewka

    We address the issue of quantitatively assessing the severity of inconsistencies in non-monotonic frameworks. While measuring inconsistency in classical logics has been investigated for some time now, taking the non-monotonicity into account poses new challenges. In order to tackle them, we focus on the structure of minimal strongly K-inconsistent subsets of a knowledge base K—a sound generalization

  • On the limits of forgetting in Answer Set Programming
    Artif. Intell. (IF 6.628) Pub Date : 2020-06-10
    Ricardo Gonçalves; Matthias Knorr; João Leite; Stefan Woltran

    Selectively forgetting information while preserving what matters the most is becoming an increasingly important issue in many areas, including in knowledge representation and reasoning. Depending on the application at hand, forgetting operators are defined to obey different sets of desirable properties. It turns out that, of the myriad of desirable properties discussed in the context of forgetting

  • Interpretable time series kernel analytics by pre-image estimation
    Artif. Intell. (IF 6.628) Pub Date : 2020-06-10
    Thao Tran Thi Phuong; Ahlame Douzal-Chouakria; Saeed Varasteh Yazdi; Paul Honeine; Patrick Gallinari

    Kernel methods are known to be effective to analyse complex objects by implicitly embedding them into some feature space. To interpret and analyse the obtained results, it is often required to restore in the input space the results obtained in the feature space, by using pre-image estimation methods. This work proposes a new closed-form pre-image estimation method for time series kernel analytics that

  • The distortion of distributed voting
    Artif. Intell. (IF 6.628) Pub Date : 2020-06-09
    Aris Filos-Ratsikas; Evi Micha; Alexandros A. Voudouris

    Voting can abstractly model any decision-making scenario and as such it has been extensively studied over the decades. Recently, the related literature has focused on quantifying the impact of utilizing only limited information in the voting process on the societal welfare for the outcome, by bounding the distortion of voting rules. Even though there has been significant progress towards this goal

  • Dynamic term-modal logics for first-order epistemic planning
    Artif. Intell. (IF 6.628) Pub Date : 2020-05-27
    Andrés Occhipinti Liberman; Andreas Achen; Rasmus Kræmmer Rendsvig

    Many classical planning frameworks are built on first-order languages. The first-order expressive power is desirable for compactly representing actions via schemas, and for specifying quantified conditions such as ¬∃xblocks_door(x). In contrast, several recent epistemic planning frameworks are built on propositional epistemic logic. The epistemic language is useful to describe planning problems involving

  • On the equivalence of optimal recommendation sets and myopically optimal query sets
    Artif. Intell. (IF 6.628) Pub Date : 2020-05-26
    Paolo Viappiani; Craig Boutilier

    Preference elicitation is an important component in many AI applications, including decision support and recommender systems. Such systems must assess user preferences, based on interactions with their users, and make recommendations using (possibly incomplete and imprecise) beliefs about those preferences. Mechanisms for explicit preference elicitation—asking users to answer direct queries about their

  • The logic of gossiping
    Artif. Intell. (IF 6.628) Pub Date : 2020-05-26
    Hans van Ditmarsch; Wiebe van der Hoek; Louwe B. Kuijer

    The so-called gossip problem is a formal model of peer-to-peer communication. In order to perform such communication efficiently, it is important to keep track of what agents know about who holds what information at a given point in time. The knowledge that the agents possess depends strongly on the particular type of communication that is used. Here, we formally define a large number of different

  • PopMNet: Generating structured pop music melodies using neural networks
    Artif. Intell. (IF 6.628) Pub Date : 2020-05-26
    Jian Wu; Xiaoguang Liu; Xiaolin Hu; Jun Zhu

    Recently, many deep learning models have been proposed to generate symbolic melodies. However, generating pop music melodies with well organized structures remains to be challenging. In this paper, we present a melody structure-based model called PopMNet to generate structured pop music melodies. The melody structure is defined by pairwise relations, specifically, repetition and sequence, between all

  • Verification of multi-agent systems with public actions against strategy logic
    Artif. Intell. (IF 6.628) Pub Date : 2020-05-15
    Francesco Belardinelli; Alessio Lomuscio; Aniello Murano; Sasha Rubin

    Model checking multi-agent systems, in which agents are distributed and thus may have different observations of the world, against strategic behaviours is known to be a complex problem in a number of settings. There are traditionally two ways of ameliorating this complexity: imposing a hierarchy on the observations of the agents, or restricting agent actions so that they are observable by all agents

  • Special issue on autonomous agents modelling other agents: Guest editorial
    Artif. Intell. (IF 6.628) Pub Date : 2020-05-13
    Stefano V. Albrecht; Peter Stone; Michael P. Wellman

    Much research in artificial intelligence is concerned with enabling autonomous agents to reason about various aspects of other agents (such as their beliefs, goals, plans, or decisions) and to utilise such reasoning for effective interaction. This special issue contains new technical contributions addressing open problems in autonomous agents modelling other agents, as well as research perspectives

  • Compact and efficient encodings for planning in factored state and action spaces with learned Binarized Neural Network transition models
    Artif. Intell. (IF 6.628) Pub Date : 2020-05-12
    Buser Say; Scott Sanner

    In this paper, we leverage the efficiency of Binarized Neural Networks (BNNs) to learn complex state transition models of planning domains with discretized factored state and action spaces. In order to directly exploit this transition structure for planning, we present two novel compilations of the learned factored planning problem with BNNs based on reductions to Weighted Partial Maximum Boolean Satisfiability

  • Complexity of abstract argumentation under a claim-centric view
    Artif. Intell. (IF 6.628) Pub Date : 2020-05-12
    Wolfgang Dvořák; Stefan Woltran

    Abstract argumentation frameworks have been introduced by Dung as part of an argumentation process, where arguments and conflicts are derived from a given knowledge base. It is solely this relation between arguments that is then used in order to identify acceptable sets of arguments. A final step concerns the acceptance status of particular statements by reviewing the actual contents of the acceptable

  • On strengthening the logic of iterated belief revision: Proper ordinal interval operators
    Artif. Intell. (IF 6.628) Pub Date : 2020-05-07
    Richard Booth; Jake Chandler

    Darwiche and Pearl's seminal 1997 article outlined a number of baseline principles for a logic of iterated belief revision. These principles, the DP postulates, have been supplemented in a number of alternative ways. However, most of the suggestions for doing so have been radical enough to result in a dubious ‘reductionist’ principle that identifies belief states with orderings of worlds. The present

  • On the complexity of reasoning about opinion diffusion under majority dynamics
    Artif. Intell. (IF 6.628) Pub Date : 2020-04-27
    Vincenzo Auletta; Diodato Ferraioli; Gianluigi Greco

    We study opinion diffusion on social graphs where agents hold binary opinions and where social pressure leads them to conform to the opinion manifested by the majority of their neighbors. We provide bounds relating the number of agents that suffice to spread an opinion to all other agents with the number of required propagation steps. Bounds are established constructively, via polynomial time algorithms

  • Knowing the price of success
    Artif. Intell. (IF 6.628) Pub Date : 2020-04-24
    Rui Cao; Pavel Naumov

    If an agent, or a coalition of agents, knows that it has a strategy to achieve a certain outcome, it does not mean that the agent knows what the strategy is. Even if the agent knows what the strategy is, she might not know the price of executing this strategy. The article considers modality “the coalition not only knows the strategy, but also knows an upper bound on the price of executing the strategy”

  • CPCES: A planning framework to solve conformant planning problems through a counterexample guided refinement
    Artif. Intell. (IF 6.628) Pub Date : 2020-03-26
    Alban Grastien; Enrico Scala

    We introduce cpces, a novel planner for the problem of deterministic conformant planning. cpces solves the problem by producing candidate plans based on a sample of the initial belief state, searching for counter-examples to these plans, and assigning these counter-examples to the sample, until a valid plan has been produced or the problem has been proved unfeasible. On top of providing a means to

  • Combining gaze and AI planning for online human intention recognition
    Artif. Intell. (IF 6.628) Pub Date : 2020-04-01
    Ronal Singh; Tim Miller; Joshua Newn; Eduardo Velloso; Frank Vetere; Liz Sonenberg

    Intention recognition is the process of using behavioural cues, such as deliberative actions, eye gaze, and gestures, to infer an agent's goals or future behaviour. In artificial intelligence, one approach for intention recognition is to use a model of possible behaviour to rate intentions as more likely if they are a better ‘fit’ to actions observed so far. In this paper, we draw from literature linking

  • On quasi-inconsistency and its complexity
    Artif. Intell. (IF 6.628) Pub Date : 2020-04-15
    Carl Corea; Matthias Thimm

    We address the issue of analyzing potential inconsistencies in knowledge bases. This refers to knowledge bases that contain rules which will always be activated together, and the knowledge base will become inconsistent, should these rules be activated. We investigate this problem in the context of the industrial use-case of business rule management, where it is often required that sets of (only) rules

  • Swarm Intelligence for Self-Organized Clustering
    Artif. Intell. (IF 6.628) Pub Date : 2020-01-28
    Michael C. Thrun; Alfred Ultsch

    Algorithms implementing populations of agents which interact with one another and sense their environment may exhibit emergent behavior such as self-organization and swarm intelligence. Here a swarm system, called Databionic swarm (DBS), is introduced which is able to adapt itself to structures of high-dimensional data characterized by distance and/or density-based structures in the data space. By

  • Compatibility, desirability, and the running intersection property
    Artif. Intell. (IF 6.628) Pub Date : 2020-03-30
    Enrique Miranda; Marco Zaffalon

    Compatibility is the problem of checking whether some given probabilistic assessments have a common joint probabilistic model. When the assessments are unconditional, the problem is well established in the literature and finds a solution through the running intersection property (RIP). This is not the case of conditional assessments. In this paper, we study the compatibility problem in a very general

  • An epistemic logic of blameworthiness
    Artif. Intell. (IF 6.628) Pub Date : 2020-03-26
    Pavel Naumov; Jia Tao

    Blameworthiness of an agent or a coalition of agents can be defined in terms of the principle of alternative possibilities: for the coalition to be responsible for an outcome, the outcome must take place and the coalition should be a minimal one that had a strategy to prevent the outcome. In this article we argue that in the settings with imperfect information, not only should the coalition have had

  • Intention as commitment toward time
    Artif. Intell. (IF 6.628) Pub Date : 2020-03-26
    Marc van Zee; Dragan Doder; Leendert van der Torre; Mehdi Dastani; Thomas Icard; Eric Pacuit

    In this paper we address the interplay among intention, time, and belief in dynamic environments. The first contribution is a logic for reasoning about intention, time and belief, in which assumptions of intentions are represented by preconditions of intended actions. Intentions and beliefs are coherent as long as these assumptions are not violated, i.e. as long as intended actions can be performed

  • On pruning search trees of impartial games
    Artif. Intell. (IF 6.628) Pub Date : 2020-03-24
    Piotr Beling; Marek Rogalski

    In this paper we study computing Sprague-Grundy values for short impartial games under the normal play convention. We put forward new game-agnostic methods for effective pruning search trees of impartial games. These algorithms are inspired by the α-β, a well-known pruning method for minimax trees. However, our algorithms prune trees whose node values are assigned by the mex function instead of min/max

  • Adapting a kidney exchange algorithm to align with human values
    Artif. Intell. (IF 6.628) Pub Date : 2020-03-24
    Rachel Freedman; Jana Schaich Borg; Walter Sinnott-Armstrong; John P. Dickerson; Vincent Conitzer

    The efficient and fair allocation of limited resources is a classical problem in economics and computer science. In kidney exchanges, a central market maker allocates living kidney donors to patients in need of an organ. Patients and donors in kidney exchanges are prioritized using ad-hoc weights decided on by committee and then fed into an allocation algorithm that determines who gets what—and who

  • Qualitative Case-Based Reasoning and Learning
    Artif. Intell. (IF 6.628) Pub Date : 2020-03-20
    Thiago Pedro Donadon Homem; Paulo Eduardo Santos; Anna Helena Reali Costa; Reinaldo Augusto da Costa Bianchi; Ramon Lopez de Mantaras

    The development of autonomous agents that perform tasks with the same dexterity as performed by humans is one of the challenges of artificial intelligence and robotics. This motivates the research on intelligent agents, since the agent must choose the best action in a dynamic environment in order to maximise the final score. In this context, the present paper introduces a novel algorithm for Qualitative

  • Limited Lookahead in Imperfect-Information Games
    Artif. Intell. (IF 6.628) Pub Date : 2020-03-19
    Christian Kroer; Tuomas Sandholm

    Limited lookahead has been studied for decades in perfect-information games. We initiate a new direction via two simultaneous deviation points: generalization to imperfect-information games and a game-theoretic approach. We study how one should act when facing an opponent whose lookahead is limited. We study this for opponents that differ based on their lookahead depth, based on whether they, too,

  • Fair navigation planning: a resource for characterizing and designing fairness in mobile robots
    Artif. Intell. (IF 6.628) Pub Date : 2020-03-18
    Martim Brandão; Marina Jirtoka; Helena Webb; Paul Luff

    In recent years, the development and deployment of autonomous systems such as mobile robots have been increasingly common. Investigating and implementing ethical considerations such as fairness in autonomous systems is an important problem that is receiving increased attention, both because of recent findings of their potential undesired impacts and a related surge in ethical principles and guidelines

  • Batch repair actions for automated troubleshooting
    Artif. Intell. (IF 6.628) Pub Date : 2020-03-16
    Hilla Shinitzky; Roni Stern

    Repairing a set of components as a batch is often cheaper than repairing each of them separately. A primary reason for this is that initiating a repair action and testing the system after performing a repair action often incurs non-negligible overhead. However, most troubleshooting algorithms proposed to date do not consider the option of performing batch repair actions. In this work we close this

  • Automated construction of bounded-loss imperfect-recall abstractions in extensive-form games
    Artif. Intell. (IF 6.628) Pub Date : 2020-02-14
    Jiří Čermák; Viliam Lisý; Branislav Bošanský

    Extensive-form games (EFGs) model finite sequential interactions between players. The amount of memory required to represent these games is the main bottleneck of algorithms for computing optimal strategies and the size of these strategies is often impractical for real-world applications. A common approach to tackle the memory bottleneck is to use information abstraction that removes parts of information

  • How do fairness definitions fare? Testing public attitudes towards three algorithmic definitions of fairness in loan allocations
    Artif. Intell. (IF 6.628) Pub Date : 2020-02-20
    Nripsuta Ani Saxena; Karen Huang; Evan DeFilippis; Goran Radanovic; David C. Parkes; Yang Liu

    What is the best way to define algorithmic fairness? While many definitions of fairness have been proposed in the computer science literature, there is no clear agreement over a particular definition. In this work, we investigate ordinary people's perceptions of three of these fairness definitions. Across three online experiments, we test which definitions people perceive to be the fairest in the context

  • Autoepistemic equilibrium logic and epistemic specifications
    Artif. Intell. (IF 6.628) Pub Date : 2020-02-19
    Luis Fariñas del Cerro; Andreas Herzig; Ezgi Iraz Su

    Epistemic specifications extend disjunctive answer-set programs by an epistemic modal operator that may occur in the body of rules. Their semantics is in terms of world views, which are sets of answer sets, and the idea is that the epistemic modal operator quantifies over these answer sets. Several such semantics were proposed in the literature. We here propose a new semantics that is based on the

  • Robust Learning with Imperfect Privileged Information
    Artif. Intell. (IF 6.628) Pub Date : 2020-02-12
    Xue Li; Bo Du; Chang Xu; Yipeng Zhang; Lefei Zhang; Dacheng Tao

    In the learning using privileged information (LUPI) paradigm, example data cannot always be clean, while the gathered privileged information can be imperfect in practice. Here, imperfect privileged information can refer to auxiliary information that is not always accurate or perturbed by noise, or alternatively to incomplete privileged information, where privileged information is only available for

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