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Controlled query evaluation in description logics through consistent query answering Artif. Intell. (IF 5.1) Pub Date : 2024-07-02 Gianluca Cima, Domenico Lembo, Riccardo Rosati, Domenico Fabio Savo
Controlled Query Evaluation (CQE) is a framework for the protection of confidential data, where a given in terms of logic formulae indicates which information must be kept private. Functions called filter query answering so that no answers are returned that may lead a user to infer data protected by the policy. The preferred censors, called censors, are the ones that conceal only what is necessary
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Incremental measurement of structural entropy for dynamic graphs Artif. Intell. (IF 5.1) Pub Date : 2024-07-02 Runze Yang, Hao Peng, Chunyang Liu, Angsheng Li
Structural entropy is a metric that measures the amount of information embedded in graph structure data under a strategy of hierarchical abstracting. To measure the structural entropy of a dynamic graph, we need to decode the optimal encoding tree corresponding to the best community partitioning for each snapshot. However, the current methods do not support dynamic encoding tree updating and incremental
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Hyper-heuristics for personnel scheduling domains Artif. Intell. (IF 5.1) Pub Date : 2024-06-25 Lucas Kletzander, Nysret Musliu
In real-life applications problems can frequently change or require small adaptations. Manually creating and tuning algorithms for different problem domains or different versions of a problem can be cumbersome and time-consuming. In this paper we consider several important problems with high practical relevance, which are Rotating Workforce Scheduling, Minimum Shift Design, and Bus Driver Scheduling
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Boosting optimal symbolic planning: Operator-potential heuristics Artif. Intell. (IF 5.1) Pub Date : 2024-06-21 Daniel Fišer, Álvaro Torralba, Jörg Hoffmann
Heuristic search guides the exploration of states via heuristic functions estimating remaining cost. Symbolic search instead replaces the exploration of individual states with that of state sets, compactly represented using binary decision diagrams (BDDs). In cost-optimal planning, heuristic explicit search performs best overall, but symbolic search performs best in many individual domains, so both
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Delegated online search Artif. Intell. (IF 5.1) Pub Date : 2024-06-20 Pirmin Braun, Niklas Hahn, Martin Hoefer, Conrad Schecker
In a delegation problem, a with commitment power tries to pick one out of options. Each option is drawn independently from a known distribution. Instead of inspecting the options herself, delegates the information acquisition to a rational and self-interested . After inspection, proposes one of the options, and can accept or reject.
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An extensive study of security games with strategic informants Artif. Intell. (IF 5.1) Pub Date : 2024-06-12 Weiran Shen, Minbiao Han, Weizhe Chen, Taoan Huang, Rohit Singh, Haifeng Xu, Fei Fang
Over the past years, game-theoretic modeling for security and public safety issues (also known as ) have attracted intensive research attention and have been successfully deployed in many real-world applications for fighting, e.g., illegal poaching, fishing and urban crimes. However, few existing works consider how information from local communities would affect the structure of these games. In this
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A domain-independent agent architecture for adaptive operation in evolving open worlds Artif. Intell. (IF 5.1) Pub Date : 2024-06-06 Shiwali Mohan, Wiktor Piotrowski, Roni Stern, Sachin Grover, Sookyung Kim, Jacob Le, Yoni Sher, Johan de Kleer
Model-based reasoning agents are ill-equipped to act in novel situations in which their model of the environment no longer sufficiently represents the world. We propose HYDRA, a framework for designing model-based agents operating in mixed discrete-continuous worlds that can autonomously detect when the environment has evolved from its canonical setup, understand how it has evolved, and adapt the agents'
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Functional Relation Field: A Model-Agnostic Framework for Multivariate Time Series Forecasting Artif. Intell. (IF 5.1) Pub Date : 2024-06-05 Ting Li, Bing Yu, Jianguo Li, Zhanxing Zhu
In multivariate time series forecasting, the most popular strategy for modeling the relationship between multiple time series is the construction of graph, where each time series is represented as a node and related nodes are connected by edges. However, the relationship between multiple time series is typically complicated, e.g. the sum of outflows from upstream nodes may be equal to the inflows of
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Stability based on single-agent deviations in additively separable hedonic games Artif. Intell. (IF 5.1) Pub Date : 2024-05-31 Felix Brandt, Martin Bullinger, Leo Tappe
Coalition formation is a central concern in multiagent systems. A common desideratum for coalition structures is stability, defined by the absence of beneficial deviations of single agents. Such deviations require an agent to improve her utility by joining another coalition. On top of that, the feasibility of deviations may also be restricted by demanding consent of agents in the welcoming and/or the
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Joint learning of reward machines and policies in environments with partially known semantics Artif. Intell. (IF 5.1) Pub Date : 2024-05-23 Christos K. Verginis, Cevahir Koprulu, Sandeep Chinchali, Ufuk Topcu
We study the problem of reinforcement learning for a task encoded by a reward machine. The task is defined over a set of properties in the environment, called atomic propositions, and represented by Boolean variables. One unrealistic assumption commonly used in the literature is that the truth values of these propositions are accurately known. In real situations, however, these truth values are uncertain
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Credulous acceptance in high-order argumentation frameworks with necessities: An incremental approach Artif. Intell. (IF 5.1) Pub Date : 2024-05-22 Gianvincenzo Alfano, Andrea Cohen, Sebastian Gottifredi, Sergio Greco, Francesco Parisi, Guillermo R. Simari
Argumentation is an important research area in the field of AI. There is a substantial amount of work on different aspects of Dung's abstract Argumentation Framework (AF). Two relevant aspects considered separately so far are: ) extending the framework to account for recursive attacks and supports, and considering dynamics, , AFs evolving over time. In this paper, we jointly deal with these two aspects
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Optimizing pathfinding for goal legibility and recognition in cooperative partially observable environments Artif. Intell. (IF 5.1) Pub Date : 2024-05-21 Sara Bernardini, Fabio Fagnani, Alexandra Neacsu, Santiago Franco
In this paper, we perform a joint design of goal legibility and recognition in a cooperative, multi-agent pathfinding setting with partial observability. More specifically, we consider a set of identical agents (the actors) that move in an environment only partially observable to an observer in the loop. The actors are tasked with reaching a set of locations that need to be serviced in a timely fashion
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Acquiring and modeling abstract commonsense knowledge via conceptualization Artif. Intell. (IF 5.1) Pub Date : 2024-05-17 Mutian He, Tianqing Fang, Weiqi Wang, Yangqiu Song
Conceptualization, or viewing entities and situations as instances of abstract concepts in mind and making inferences based on that, is a vital component in human intelligence for commonsense reasoning. Despite recent progress in artificial intelligence to acquire and model commonsense attributed to neural language models and commonsense knowledge graphs (CKGs), conceptualization is yet to be introduced
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Knowledge is power: Open-world knowledge representation learning for knowledge-based visual reasoning Artif. Intell. (IF 5.1) Pub Date : 2024-05-13 Wenbo Zheng, Lan Yan, Fei-Yue Wang
Knowledge-based visual reasoning requires the ability to associate outside knowledge that is not present in a given image for cross-modal visual understanding. Two deficiencies of the existing approaches are that (1) they only employ or construct elementary and but superficial knowledge graphs while lacking complex and but indispensable cross-modal knowledge for visual reasoning, and (2) they also
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Exploring the psychology of LLMs’ moral and legal reasoning Artif. Intell. (IF 5.1) Pub Date : 2024-05-03 Guilherme F.C.F. Almeida, José Luiz Nunes, Neele Engelmann, Alex Wiegmann, Marcelo de Araújo
Large language models (LLMs) exhibit expert-level performance in tasks across a wide range of different domains. Ethical issues raised by LLMs and the need to align future versions makes it important to know how state of the art models reason about moral and legal issues. In this paper, we employ the methods of experimental psychology to probe into this question. We replicate eight studies from the
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A multi-graph representation for event extraction Artif. Intell. (IF 5.1) Pub Date : 2024-05-03 Hui Huang, Yanping Chen, Chuan Lin, Ruizhang Huang, Qinghua Zheng, Yongbin Qin
Event extraction has a trend in identifying event triggers and arguments in a unified framework, which has the advantage of avoiding the cascading failure in pipeline methods. The main problem is that joint models usually assume a one-to-one relationship between event triggers and arguments. It leads to the argument multiplexing problem, in which an argument mention can serve different roles in an
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Mitigating social biases of pre-trained language models via contrastive self-debiasing with double data augmentation Artif. Intell. (IF 5.1) Pub Date : 2024-04-26 Yingji Li, Mengnan Du, Rui Song, Xin Wang, Mingchen Sun, Ying Wang
Pre-trained Language Models (PLMs) have been shown to inherit and even amplify the social biases contained in the training corpus, leading to undesired stereotype in real-world applications. Existing techniques for mitigating the social biases of PLMs mainly rely on data augmentation with manually designed prior knowledge or fine-tuning with abundant external corpora to debias. However, these methods
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Iterative voting with partial preferences Artif. Intell. (IF 5.1) Pub Date : 2024-04-21 Zoi Terzopoulou, Panagiotis Terzopoulos, Ulle Endriss
Voting platforms can offer participants the option to sequentially modify their preferences, whenever they have a reason to do so. But such iterative voting may never converge, meaning that a state where all agents are happy with their submitted preferences may never be reached. This problem has received increasing attention within the area of computational social choice. Yet, the relevant literature
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Probabilistic reach-avoid for Bayesian neural networks Artif. Intell. (IF 5.1) Pub Date : 2024-04-17 Matthew Wicker, Luca Laurenti, Andrea Patane, Nicola Paoletti, Alessandro Abate, Marta Kwiatkowska
Model-based reinforcement learning seeks to simultaneously learn the dynamics of an unknown stochastic environment and synthesise an optimal policy for acting in it. Ensuring the safety and robustness of sequential decisions made through a policy in such an environment is a key challenge for policies intended for safety-critical scenarios. In this work, we investigate two complementary problems: first
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A unified momentum-based paradigm of decentralized SGD for non-convex models and heterogeneous data Artif. Intell. (IF 5.1) Pub Date : 2024-04-17 Haizhou Du, Chaoqian Cheng, Chengdong Ni
Emerging distributed applications recently boosted the development of decentralized machine learning, especially in IoT and edge computing fields. In real-world scenarios, the common problems of non-convexity and data heterogeneity result in inefficiency, performance degradation, and development stagnation. The bulk of studies concentrate on one of the issues mentioned above without having a more general
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Discrete preference games with logic-based agents: Formal framework, complexity, and islands of tractability Artif. Intell. (IF 5.1) Pub Date : 2024-04-08 Gianluigi Greco, Marco Manna
Analyzing and predicting the dynamics of opinion formation in the context of social environments are problems that attracted much attention in literature. While grounded in social psychology, these problems are nowadays popular within the artificial intelligence community, where opinion dynamics are often studied via models in which individuals/agents hold opinions taken from a fixed set of alternatives
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Critical observations in model-based diagnosis Artif. Intell. (IF 5.1) Pub Date : 2024-03-29 Cody James Christopher, Alban Grastien
In this paper, we address the problem of finding the part of the observations that is useful for the diagnosis. We define a as an abstraction of the observations. We then argue that a sub-observation is if it allows a diagnoser to derive the same minimal diagnosis as the original observations; and we define as a maximally abstracted sufficient sub-observation. We show how to compute a critical observation
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Polarized message-passing in graph neural networks Artif. Intell. (IF 5.1) Pub Date : 2024-03-27 Tiantian He, Yang Liu, Yew-Soon Ong, Xiaohu Wu, Xin Luo
In this paper, we present Polarized message-passing (PMP), a novel paradigm to revolutionize the design of message-passing graph neural networks (GNNs). In contrast to existing methods, PMP captures the power of node-node similarity and dissimilarity to acquire dual sources of messages from neighbors. The messages are then coalesced to enable GNNs to learn expressive representations from sparse but
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Matching papers and reviewers at large conferences Artif. Intell. (IF 5.1) Pub Date : 2024-03-25 Kevin Leyton-Brown, Mausam, Yatin Nandwani, Hedayat Zarkoob, Chris Cameron, Neil Newman, Dinesh Raghu
Peer-reviewed conferences, the main publication venues in CS, rely critically on matching highly qualified reviewers for each paper. Because of the growing scale of these conferences, the tight timelines on which they operate, and a recent surge in explicitly dishonest behavior, there is now no alternative to performing this matching in an automated way. This paper introduces , a novel reviewer–paper
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Almost proportional allocations of indivisible chores: Computation, approximation and efficiency Artif. Intell. (IF 5.1) Pub Date : 2024-03-24 Haris Aziz, Bo Li, Hervé Moulin, Xiaowei Wu, Xinran Zhu
Proportionality (PROP) is one of the simplest and most intuitive fairness criteria used for allocating items among agents with additive utilities. However, when the items are indivisible, ensuring PROP becomes unattainable, leading to increased focus on its relaxations. In this paper, we focus on the relaxation of proportionality up to any item (PROPX), where proportionality is satisfied if an arbitrary
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Knowledge-driven profile dynamics Artif. Intell. (IF 5.1) Pub Date : 2024-03-21 Eduardo Fermé, Marco Garapa, Maurício D.L. Reis, Yuri Almeida, Teresa Paulino, Mariana Rodrigues
In the last decades, user profiles have been used in several areas of information technology. In the literature, most research works, and systems focus on the creation of profiles (using Data Mining techniques based on user's navigation or interaction history). In general, the dynamics of profiles are made by means of a systematic recreation of the profiles, without using the previous profiles. In
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Regular decision processes Artif. Intell. (IF 5.1) Pub Date : 2024-03-21 Ronen I. Brafman, Giuseppe De Giacomo
We introduce and study Regular Decision Processes (RDPs), a new, compact model for domains with non-Markovian dynamics and rewards, in which the dependence on the past is regular, in the language theoretic sense. RDPs are an intermediate model between MDPs and POMDPs. They generalize -order MDPs and can be viewed as a POMDP in which the hidden state is a regular function of the entire history. In factored
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Lifted algorithms for symmetric weighted first-order model sampling Artif. Intell. (IF 5.1) Pub Date : 2024-03-19 Yuanhong Wang, Juhua Pu, Yuyi Wang, Ondřej Kuželka
Weighted model counting (WMC) is the task of computing the weighted sum of all satisfying assignments (i.e., models) of a propositional formula. Similarly, weighted model sampling (WMS) aims to randomly generate models with probability proportional to their respective weights. Both WMC and WMS are hard to solve exactly, falling under the #-hard complexity class. However, it is known that the counting
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Embedding justification theory in approximation fixpoint theory Artif. Intell. (IF 5.1) Pub Date : 2024-03-16 Simon Marynissen, Bart Bogaerts, Marc Denecker
Approximation Fixpoint Theory (AFT) and Justification Theory (JT) are two frameworks to unify logical formalisms. AFT studies semantics in terms of fixpoints of lattice operators, and JT in terms of so-called justifications, which are explanations of why certain facts do or do not hold in a model. While the approaches differ, the frameworks were designed with similar goals in mind, namely to study
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A differentiable first-order rule learner for inductive logic programming Artif. Intell. (IF 5.1) Pub Date : 2024-03-15 Kun Gao, Katsumi Inoue, Yongzhi Cao, Hanpin Wang
Learning first-order logic programs from relational facts yields intuitive insights into the data. Inductive logic programming (ILP) models are effective in learning first-order logic programs from observed relational data. Symbolic ILP models support rule learning in a data-efficient manner. However, symbolic ILP models are not robust to learn from noisy data. Neuro-symbolic ILP models utilize neural
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A neurosymbolic cognitive architecture framework for handling novelties in open worlds Artif. Intell. (IF 5.1) Pub Date : 2024-03-15 Shivam Goel, Panagiotis Lymperopoulos, Ravenna Thielstrom, Evan Krause, Patrick Feeney, Pierrick Lorang, Sarah Schneider, Yichen Wei, Eric Kildebeck, Stephen Goss, Michael C. Hughes, Liping Liu, Jivko Sinapov, Matthias Scheutz
“Open world” environments are those in which novel objects, agents, events, and more can appear and contradict previous understandings of the environment. This runs counter to the “closed world” assumption used in most AI research, where the environment is assumed to be fully understood and unchanging. The types of environments AI agents can be deployed in are limited by the inability to handle the
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Hyperbolic Secant representation of the logistic function: Application to probabilistic Multiple Instance Learning for CT intracranial hemorrhage detection Artif. Intell. (IF 5.1) Pub Date : 2024-03-15 Francisco M. Castro-Macías, Pablo Morales-Álvarez, Yunan Wu, Rafael Molina, Aggelos K. Katsaggelos
Multiple Instance Learning (MIL) is a weakly supervised paradigm that has been successfully applied to many different scientific areas and is particularly well suited to medical imaging. Probabilistic MIL methods, and more specifically Gaussian Processes (GPs), have achieved excellent results due to their high expressiveness and uncertainty quantification capabilities. One of the most successful GP-based
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Non-deterministic approximation fixpoint theory and its application in disjunctive logic programming Artif. Intell. (IF 5.1) Pub Date : 2024-03-08 Jesse Heyninck, Ofer Arieli, Bart Bogaerts
Approximation fixpoint theory (AFT) is an abstract and general algebraic framework for studying the semantics of nonmonotonic logics. It provides a unifying study of the semantics of different formalisms for nonmonotonic reasoning, such as logic programming, default logic and autoepistemic logic. In this paper, we extend AFT to dealing with that allow to handle indefinite information, represented e
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“Guess what I'm doing”: Extending legibility to sequential decision tasks Artif. Intell. (IF 5.1) Pub Date : 2024-03-07 Miguel Faria, Francisco S. Melo, Ana Paiva
In this paper we investigate the notion of in sequential decision tasks under uncertainty. Previous works that extend legibility to scenarios beyond robot motion either focus on deterministic settings or are computationally too expensive. Our proposed approach, dubbed PoLMDP, is able to handle uncertainty while remaining computationally tractable. We establish the advantages of our approach against
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aspmc: New frontiers of algebraic answer set counting Artif. Intell. (IF 5.1) Pub Date : 2024-03-06 Thomas Eiter, Markus Hecher, Rafael Kiesel
In the last decade, there has been increasing interest in extensions of answer set programming (ASP) that cater for quantitative information such as weights or probabilities. A wide range of quantitative reasoning tasks for ASP and logic programming, among them probabilistic inference and parameter learning in the neuro-symbolic setting, can be expressed as algebraic answer set counting (AASC) tasks
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Investigating the properties of neural network representations in reinforcement learning Artif. Intell. (IF 5.1) Pub Date : 2024-03-01 Han Wang, Erfan Miahi, Martha White, Marlos C. Machado, Zaheer Abbas, Raksha Kumaraswamy, Vincent Liu, Adam White
In this paper we investigate the properties of representations learned by deep reinforcement learning systems. Much of the early work on representations for reinforcement learning focused on designing fixed-basis architectures to achieve properties thought to be desirable, such as orthogonality and sparsity. In contrast, the idea behind deep reinforcement learning methods is that the agent designer
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Crossover can guarantee exponential speed-ups in evolutionary multi-objective optimisation Artif. Intell. (IF 5.1) Pub Date : 2024-02-27 Duc-Cuong Dang, Andre Opris, Dirk Sudholt
Evolutionary algorithms are popular algorithms for multi-objective optimisation (also called Pareto optimisation) as they use a population to store trade-offs between different objectives. Despite their popularity, the theoretical foundation of multi-objective evolutionary optimisation (EMO) is still in its early development. Fundamental questions such as the benefits of the crossover operator are
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Datalog rewritability and data complexity of [formula omitted] with closed predicates Artif. Intell. (IF 5.1) Pub Date : 2024-02-23 Sanja Lukumbuzya, Magdalena Ortiz, Mantas Šimkus
We study the relative expressiveness of ontology-mediated queries (OMQs) formulated in the expressive Description Logic extended with closed predicates. In particular, we present a polynomial time translation from OMQs into Datalog with negation under the stable model semantics, the formalism that underlies Answer Set Programming. This is a novel and non-trivial result: the considered OMQs are not
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Finding the optimal exploration-exploitation trade-off online through Bayesian risk estimation and minimization Artif. Intell. (IF 5.1) Pub Date : 2024-02-21 Stewart Jamieson, Jonathan P. How, Yogesh Girdhar
We propose (EBRM) over policy sets as an approach to online learning across a wide range of settings. Many real-world online learning problems have complexities such as action- and belief-dependent rewards, time-discounting of reward, and heterogeneous costs for actions and feedback; we find that existing online learning heuristics cannot leverage most problem-specific information, to the detriment
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Generalized planning as heuristic search: A new planning search-space that leverages pointers over objects Artif. Intell. (IF 5.1) Pub Date : 2024-02-15 Javier Segovia-Aguas, Sergio Jiménez, Anders Jonsson
is one of the most successful approaches to but unfortunately, it does not trivially extend to (GP); GP aims to compute algorithmic solutions that are valid for a set of classical planning instances from a given domain, even if these instances differ in their number of objects, the initial and goal configuration of these objects and hence, in the number (and possible values) of the state variables
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Decentralized fused-learner architectures for Bayesian reinforcement learning Artif. Intell. (IF 5.1) Pub Date : 2024-02-13 Augustin A. Saucan, Subhro Das, Moe Z. Win
Decentralized training is a robust solution for learning over an extensive network of distributed agents. Many existing solutions involve the averaging of locally inferred parameters which constrain the architecture to independent agents with identical learning algorithms. Here, we propose decentralized fused-learner architectures for Bayesian reinforcement learning, named fused Bayesian-learner architectures
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Temporal segmentation in multi agent path finding with applications to explainability Artif. Intell. (IF 5.1) Pub Date : 2024-02-07 Shaull Almagor, Justin Kottinger, Morteza Lahijanian
Multi-Agent Path Finding (MAPF) is the problem of planning paths for agents to reach their targets from their start locations, such that the agents do not collide while executing the plan. In many settings, the plan (or a digest thereof) is conveyed to a supervising entity, e.g., for confirmation before execution, for a report, etc. In such cases, we wish to convey that the plan is collision-free with
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Primarily about primaries Artif. Intell. (IF 5.1) Pub Date : 2024-02-07 Allan Borodin, Omer Lev, Nisarg Shah, Tyrone Strangway
Much of the social choice literature examines voting systems, in which voters submit their ranked preferences over candidates and a voting rule picks a winner. Real-world elections and decision-making processes are often more complex and involve multiple stages. For instance, one popular voting system filters candidates through : first, voters affiliated with each political party vote over candidates
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An extended view on lifting Gaussian Bayesian networks Artif. Intell. (IF 5.1) Pub Date : 2024-02-06 Mattis Hartwig, Ralf Möller, Tanya Braun
Lifting probabilistic graphical models and developing lifted inference algorithms aim to use higher level groups of random variables instead of individual instances. In the past, many inference algorithms for discrete probabilistic graphical models have been lifted. Lifting continuous probabilistic graphical models has played a minor role. Since many real-world applications involve continuous random
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Pre-training and diagnosing knowledge base completion models Artif. Intell. (IF 5.1) Pub Date : 2024-02-02 Vid Kocijan, Myeongjun Jang, Thomas Lukasiewicz
In this work, we introduce and analyze an approach to knowledge transfer from one collection of facts to another without the need for entity or relation matching. The method works for both knowledge bases and or , i.e., knowledge bases where more than one copy of a real-world entity or relation may exist. The main contribution is a method that can make use of large-scale pre-training on facts, which
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Revision operators with compact representations Artif. Intell. (IF 5.1) Pub Date : 2024-02-02 Pavlos Peppas, Mary-Anne Williams, Grigoris Antoniou
Despite the great theoretical advancements in the area of Belief Revision, there has been limited success in terms of implementations. One of the hurdles in implementing revision operators is that their specification (let alone their computation), requires substantial resources. On the other hand, implementing a specific revision operator, like Dalal's operator, would be of limited use. In this paper
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Efficient optimal Kolmogorov approximation of random variables Artif. Intell. (IF 5.1) Pub Date : 2024-02-01 Liat Cohen, Tal Grinshpoun, Gera Weiss
Discrete random variables are essential ingredients in various artificial intelligence problems. These include the estimation of the probability of missing the deadline in a series-parallel schedule and the assignment of suppliers to tasks in a project in a manner that maximizes the probability of meeting the overall project deadline. The solving of such problems involves repetitive operations, such
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A stochastic process approach for multi-agent path finding with non-asymptotic performance guarantees Artif. Intell. (IF 5.1) Pub Date : 2024-02-01 Xiaoyu He, Xueyan Tang, Wentong Cai, Jingning Li
Multi-agent path finding (MAPF) is a classical NP-hard problem that considers planning collision-free paths for multiple agents simultaneously. A MAPF problem is typically solved via addressing a sequence of single-agent path finding subproblems in which well-studied algorithms such as are applicable. Existing methods based on this idea, however, rely on an exhaustive search and therefore only have
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Temporal inductive path neural network for temporal knowledge graph reasoning Artif. Intell. (IF 5.1) Pub Date : 2024-02-01 Hao Dong, Pengyang Wang, Meng Xiao, Zhiyuan Ning, Pengfei Wang, Yuanchun Zhou
Temporal Knowledge Graph (TKG) is an extension of traditional Knowledge Graph (KG) that incorporates the dimension of time. Reasoning on TKGs is a crucial task that aims to predict future facts based on historical occurrences. The key challenge lies in uncovering structural dependencies within historical subgraphs and temporal patterns. Most existing approaches model TKGs relying on entity modeling
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Transferable dynamics models for efficient object-oriented reinforcement learning Artif. Intell. (IF 5.1) Pub Date : 2024-01-26 Ofir Marom, Benjamin Rosman
The Reinforcement Learning (RL) framework offers a general paradigm for constructing autonomous agents that can make effective decisions when solving tasks. An important area of study within the field of RL is transfer learning, where an agent utilizes knowledge gained from solving previous tasks to solve a new task more efficiently. While the notion of transfer learning is conceptually appealing,
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Emotion Selectable End-to-End Text-based Speech Editing Artif. Intell. (IF 5.1) Pub Date : 2024-01-23 Tao Wang, Jiangyan Yi, Ruibo Fu, Jianhua Tao, Zhengqi Wen, Chu Yuan Zhang
Text-based speech editing is a convenient way for users to edit speech by intuitively cutting, copying, and pasting text. Previous work introduced CampNet, a context-aware mask prediction network that significantly improved the quality of edited speech. However, this paper proposes a new task: adding emotional effects to the edited speech during text-based speech editing to enhance the expressiveness
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Saliency-aware regularized graph neural network Artif. Intell. (IF 5.1) Pub Date : 2024-01-19 Wenjie Pei, WeiNa Xu, Zongze Wu, Weichao Li, Jinfan Wang, Guangming Lu, Xiangrong Wang
The crux of graph classification lies in the effective representation learning for the entire graph. Typical graph neural networks focus on modeling the local dependencies when aggregating features of neighboring nodes, and obtain the representation for the entire graph by aggregating node features. Such methods have two potential limitations: 1) the global node saliency w.r.t. graph classification
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Counterexamples and amendments to the termination and optimality of ADOPT-based algorithms Artif. Intell. (IF 5.1) Pub Date : 2024-01-24 Koji Noshiro, Koji Hasebe
A distributed constraint optimization problem (DCOP) is a framework to model multi-agent coordination problems. Asynchronous distributed optimization (ADOPT) is a well-known complete DCOP algorithm, and many of its variants have been proposed over the last decade. It is considered proven that ADOPT-based algorithms have the key properties of termination and optimality, which guarantee that the algorithms
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Enhancing SMT-based Weighted Model Integration by structure awareness Artif. Intell. (IF 5.1) Pub Date : 2024-01-18 Giuseppe Spallitta, Gabriele Masina, Paolo Morettin, Andrea Passerini, Roberto Sebastiani
The development of efficient exact and approximate algorithms for probabilistic inference is a long-standing goal of artificial intelligence research. Whereas substantial progress has been made in dealing with purely discrete or purely continuous domains, adapting the developed solutions to tackle hybrid domains, characterized by discrete and continuous variables and their relationships, is highly
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On the role of logical separability in knowledge compilation Artif. Intell. (IF 5.1) Pub Date : 2024-01-12 Junming Qiu, Wenqing Li, Liangda Fang, Quanlong Guan, Zhanhao Xiao, Zhao-Rong Lai, Qian Dong
Knowledge compilation is an alternative solution to address demanding reasoning tasks with high complexity via converting knowledge bases into a suitable target language. The notion of logical separability, proposed by Levesque, offers a general explanation for the tractability of clausal entailment for two remarkable languages: decomposable negation normal form and prime implicates. It is interesting
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Dual-track spatio-temporal learning for urban flow prediction with adaptive normalization Artif. Intell. (IF 5.1) Pub Date : 2024-01-15 Xiaoyu Li, Yongshun Gong, Wei Liu, Yilong Yin, Yu Zheng, Liqiang Nie
Robust urban flow prediction is crucial for transportation planning and management in urban areas. Although recent advances in modeling spatio-temporal correlations have shown potential, most models fail to adequately consider the complex spatio-temporal semantic information present in real-world scenarios. We summarize the following three primary limitations in existing models: a) The majority of
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Corrigendum to “Learning constraints through partial queries” [Artificial Intelligence 319 (2023) 103896] Artif. Intell. (IF 5.1) Pub Date : 2024-01-12 Christian Bessiere, Clément Carbonnel, Anton Dries, Emmanuel Hebrard, George Katsirelos, Nadjib Lazaar, Nina Narodytska, Claude-Guy Quimper, Kostas Stergiou, Dimosthenis C. Tsouros, Toby Walsh
Abstract not available
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The computational complexity of multi-agent pathfinding on directed graphs Artif. Intell. (IF 5.1) Pub Date : 2024-01-09 Bernhard Nebel
While the non-optimizing variant of multi-agent pathfinding on undirected graphs is known to be a polynomial-time problem since almost forty years, a similar result has not been established for directed graphs. In this paper, it will be shown that this problem is NP-complete. For strongly connected directed graphs, however, the problem is polynomial. And both of these results hold even if one allows
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The Distortion of Distributed Facility Location Artif. Intell. (IF 5.1) Pub Date : 2024-01-09 Aris Filos-Ratsikas, Panagiotis Kanellopoulos, Alexandros A. Voudouris, Rongsen Zhang
We study the distributed facility location problem, where a set of agents with positions on the line of real numbers are partitioned into disjoint districts, and the goal is to choose a point to satisfy certain criteria, such as optimize an objective function or avoid strategic behavior. A mechanism in our distributed setting works in two steps: For each district it chooses a point that is representative