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Artificial Intelligence
Journal Prestige (SJR): 0.88
Citation Impact (citeScore): 4
Number of Followers: 241  
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 0004-3702 - ISSN (Online) 0004-3702
Published by Elsevier Homepage  [3204 journals]
  • On pruning search trees of impartial games
    • Abstract: Publication date: Available online 24 March 2020Source: Artificial IntelligenceAuthor(s): Piotr Beling, Marek Rogalski
  • Adapting a kidney exchange algorithm to align with human values
    • Abstract: Publication date: Available online 24 March 2020Source: Artificial IntelligenceAuthor(s): Rachel Freedman, Jana Schaich Borg, Walter Sinnott-Armstrong, John P. Dickerson, Vincent Conitzer
  • Qualitative Case-Based Reasoning and Learning
    • Abstract: Publication date: Available online 20 March 2020Source: Artificial IntelligenceAuthor(s): Thiago Pedro Donadon Homem, Paulo Eduardo Santos, Anna Helena Reali Costa, Reinaldo Augusto da Costa Bianchi, Ramon Lopez de Mantaras
  • Limited Lookahead in Imperfect-Information Games
    • Abstract: Publication date: Available online 19 March 2020Source: Artificial IntelligenceAuthor(s): Christian Kroer, Tuomas Sandholm
  • Fair navigation planning: a resource for characterizing and designing
           fairness in mobile robots
    • Abstract: Publication date: Available online 18 March 2020Source: Artificial IntelligenceAuthor(s): Martim Brandão, Marina Jirtoka, Helena Webb, Paul Luff
  • Batch repair actions for automated troubleshooting
    • Abstract: Publication date: Available online 16 March 2020Source: Artificial IntelligenceAuthor(s): Hilla Shinitzky, Roni Stern
  • How do fairness definitions fare' Testing public attitudes towards
           three algorithmic definitions of fairness in loan allocations
    • Abstract: Publication date: Available online 20 February 2020Source: Artificial IntelligenceAuthor(s): 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 of loan decisions, and whether fairness perceptions change with the addition of sensitive information (i.e., race or gender of the loan applicants). Overall, one definition (calibrated fairness) tends to be more preferred than the others, and the results also provide support for the principle of affirmative action.
  • Autoepistemic equilibrium logic and epistemic specifications
    • Abstract: Publication date: Available online 19 February 2020Source: Artificial IntelligenceAuthor(s): 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 logic of here-and-there: we add epistemic modal operators to its language and define epistemic here-and-there models. We then successively define epistemic equilibrium models and autoepistemic equilibrium models. The former are obtained from epistemic here-and-there models in exactly the same way as Pearce's equilibrium models are obtained from here-and-there models, viz. by minimising truth; they provide an epistemic extension of equilibrium logic. The latter are obtained from the former by maximising the set of epistemic possibilities, and they provide a new semantics for Gelfond's epistemic specifications. For both semantics we establish a strong equivalence result: we characterise strong equivalence of two epistemic programs by means of logical equivalence in epistemic here-and-there logic. We finally compare our approach to the existing semantics of epistemic specifications and discuss which formalisms provide more intuitive results by pointing out some formal properties a semantics proposal should satisfy.
  • Automated construction of bounded-loss imperfect-recall abstractions in
           extensive-form games
    • Abstract: Publication date: Available online 14 February 2020Source: Artificial IntelligenceAuthor(s): 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 available to players thus reducing the number of decision points in the game. However, existing information-abstraction techniques are either specific for a particular domain, they do not provide any quality guarantees, or they are applicable to very small subclasses of EFGs. We present domain-independent abstraction methods for creating imperfect recall abstractions in extensive-form games that allow computing strategies that are (near) optimal in the original game. To this end, we introduce two novel algorithms, FPIRA and CFR+IRA, based on fictitious play and counterfactual regret minimization. These algorithms can start with an arbitrary domain specific, or the coarsest possible, abstraction of the original game. The algorithms iteratively detect the missing information they require for computing a strategy for the abstract game that is (near) optimal in the original game. This information is then included back into the abstract game. Moreover, our algorithms are able to exploit imperfect-recall abstractions that allow players to forget even history of their own actions. However, the algorithms require traversing the complete unabstracted game tree. We experimentally show that our algorithms can closely approximate Nash equilibrium of large games using abstraction with as little as 0.9% of information sets of the original game. Moreover, the results suggest that memory savings increase with the increasing size of the original games.
  • Robust Learning with Imperfect Privileged Information
    • Abstract: Publication date: Available online 12 February 2020Source: Artificial IntelligenceAuthor(s): 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 part of the training data. Because of the lack of clear strategies for handling noise in example data and imperfect privileged information, existing learning using privileged information (LUPI) methods may encounter serious issues. Accordingly, in this paper, we propose a Robust SVM+ method to tackle imperfect data in LUPI. In order to make the SVM+ model robust to noise in example data and privileged information, Robust SVM+ maximizes the lower bound of the perturbations that may influence the judgement based on a rigorous theoretical analysis. Moreover, in order to deal with the incomplete privileged information, we use the available privileged information to help us in approximating the missing privileged information of training data. The optimization problem of the proposed method can be efficiently solved by employing a two-step alternating optimization strategy, based on iteratively deploying off-the-shelf quadratic programming solvers and the alternating direction method of multipliers (ADMM) technique. Comprehensive experiments on real-world datasets demonstrate the effectiveness of the proposed Robust SVM+ method in handling imperfect privileged information.
  • Rethinking epistemic logic with belief bases
    • Abstract: Publication date: Available online 10 February 2020Source: Artificial IntelligenceAuthor(s): Emiliano Lorini We introduce a new semantics for a family of logics of explicit and implicit belief based on the concept of multi-agent belief base. Differently from standard semantics for epistemic logic in which the notions of possible world and doxastic/epistemic alternative are primitive, in our semantics they are non-primitive but are computed from the concept of belief base. We provide complete axiomatizations and prove decidability for our logics via finite model arguments. Furthermore, we provide polynomial embeddings of our logics into Fagin & Halpern's logic of general awareness and establish complexity results via the embeddings. We also present variants of the logics incorporating different forms of epistemic introspection for explicit and/or implicit belief and provide complexity results for some of these variants. Finally, we present a number of dynamic extensions of the static framework by informative actions of both public and private type, including public announcement, belief base expansion and forgetting. We illustrate the application potential of the logical framework with the aid of a concrete example taken from the domain of conversational agents.
  • Swarm Intelligence for Self-Organized Clustering
    • Abstract: Publication date: Available online 28 January 2020Source: Artificial IntelligenceAuthor(s): 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 exploiting the interrelations of swarm intelligence, self-organization and emergence, DBS serves as an alternative approach to the optimization of a global objective function in the task of clustering. The swarm omits the usage of a global objective function and is parameter-free because it searches for the Nash equilibrium during its annealing process.To our knowledge, DBS is the first swarm combining these approaches and its clustering can outperform common clustering methods such as K-means, PAM, single linkage, spectral clustering, model-based clustering, and Ward if no prior knowledge about the data is available. A central problem in clustering is the correct estimation of the number of clusters. This is addressed by a DBS visualization called topographic map which allows assessing the number of clusters. It is known that all clustering algorithms construct clusters, no matter if the data set contains clusters or not. In contrast to most other clustering algorithms, the topographic map identifies that clustering of the data is meaningless if the data contains no (natural) clusters. The performance of DBS is demonstrated on a set of benchmark data constructed to pose difficult clustering problems and in two real-world applications.
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Heriot-Watt University
Edinburgh, EH14 4AS, UK
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