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XR4DRAMA a knowledge-based system for disaster management and media planning Knowl. Eng. Rev. (IF 2.1) Pub Date : 2024-03-14 Alexandros Vassiliades, Grigorios Stathopoulos-Kampilis, Gerasimos Antzoulatos, Spyridon Symeonidis, Sotiris Diplaris, Stefanos Vrochidis, Nick Bassiliades, Ioannis Kompatsiaris
In the previous two decades, Knowledge Graphs (KGs) have evolved, inspiring developers to build ever-more context-related KGs. Because of this development, Artificial Intelligence (AI) applications can now access open domain-specific information in a format that is both semantically rich and machine comprehensible. In this article, we introduce the XR4DRAMA framework. The KG of the XR4DRAMA framework
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Adaptive learning with artificial barriers yielding Nash equilibria in general games Knowl. Eng. Rev. (IF 2.1) Pub Date : 2023-11-29 Ismail Hassan, B. John Oommen, Anis Yazidi
Artificial barriers in Learning Automata (LA) is a powerful and yet under-explored concept although it was first proposed in the 1980s. Introducing artificial non-absorbing barriers makes the LA schemes resilient to being trapped in absorbing barriers, a phenomenon which is often referred to as lock in probability leading to an exclusive choice of one action after convergence. Within the field of LA
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Reformulation techniques for automated planning: a systematic review Knowl. Eng. Rev. (IF 2.1) Pub Date : 2023-11-08 Diaeddin Alarnaouti, George Baryannis, Mauro Vallati
Automated planning is a prominent area of Artificial Intelligence and an important component for intelligent autonomous agents. A cornerstone of domain-independent planning is the separation between planning logic, that is the automated reasoning side, and the knowledge model, that encodes a formal representation of domain knowledge needed to reason upon a given problem to synthesize a solution plan
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Using active learning and an agent-based system to perform interactive knowledge extraction based on the COVID-19 corpus Knowl. Eng. Rev. (IF 2.1) Pub Date : 2023-11-08 Yao Yao, Junying Liu, Conor Ryan
Efficient knowledge extraction from Big Data is quite a challenging topic. Recognizing relevant concepts from unannotated data while considering both context and domain knowledge is critical to implementing successful knowledge extraction. In this research, we provide a novel platform we call Active Learning Integrated with Knowledge Extraction (ALIKE) that overcomes the challenges of context awareness
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An analysis and review of robot magic shows Knowl. Eng. Rev. (IF 2.1) Pub Date : 2023-06-15 Jeehyun Yang, Jaesik Jeong, Jacky Baltes
The field of humanoid robotics is constantly evolving, with new advances creating exciting opportunities for research and development. Especially in the entertainment area, robotics applications show significant growth potential. To guide and track the progress of robotics research, good benchmark problems are crucial, but especially investigating human–robot interaction capabilities is difficult.
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Selecting and ranking leading cases in Brazilian Supreme Court decisions Knowl. Eng. Rev. (IF 2.1) Pub Date : 2023-06-14 Jackson José De Souza, Marcelo Finger, Jorge Alberto A. de Araújo, Juliano Maranhão
This work studies quantitative measures for ranking judicial decisions by the Brazilian Supreme Court [Supremo Tribunal Federal (STF)] and selecting leading cases, which are understood as those with broadness of influence on different legal fields. The measures are based on a network built over decisions whose cases were finalized in the Brazilian Supreme Court between 01/2001 and 12/2019, which were
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Lightweight mechatronic system for humanoid robot Knowl. Eng. Rev. (IF 2.1) Pub Date : 2023-06-01 Jaesik Jeong, Jeehyun Yang, Guilherme Henrique Galelli Christmann, Jacky Baltes
This paper presents the technical specifications of a lightweight humanoid robot platform named Robinion Sr. including its mechanical and electrical design. We describe a versatile and robust mechatronic system, efficient walking gait, and software architecture of the humanoid robot. The humanoid robot platform is targeted for use in a range of applications, including research and development, competitions
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Adversarial agent-learning for cybersecurity: a comparison of algorithms Knowl. Eng. Rev. (IF 2.1) Pub Date : 2023-03-06 Alexander Shashkov, Erik Hemberg, Miguel Tulla, Una-May O’Reilly
We investigate artificial intelligence and machine learning methods for optimizing the adversarial behavior of agents in cybersecurity simulations. Our cybersecurity simulations integrate the modeling of agents launching Advanced Persistent Threats (APTs) with the modeling of agents using detection and mitigation mechanisms against APTs. This simulates the phenomenon of how attacks and defenses coevolve
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An agent-based model of COVID-19 pandemic and its variants using fuzzy subsets and real data applied in an island environment Knowl. Eng. Rev. (IF 2.1) Pub Date : 2023-03-06 Sébastien Regis, Olivier Manicom, Andrei Doncescu
In this paper, we present a model of the spread of the COVID-19 pandemic simulated by a multi-agent system (MAS) based on demographic data and medical knowledge. Demographic data are linked to the distribution of the population according to age and to an index of socioeconomic fragility with regard to the elderly. Medical knowledge are related to two risk factors: age and obesity. The contributions
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A survey of evolutionary algorithms for supervised ensemble learning Knowl. Eng. Rev. (IF 2.1) Pub Date : 2023-03-01 Henry E. L. Cagnini, Silvia C. N. Das Dôres, Alex A. Freitas, Rodrigo C. Barros
This paper presents a comprehensive review of evolutionary algorithms that learn an ensemble of predictive models for supervised machine learning (classification and regression). We propose a detailed four-level taxonomy of studies in this area. The first level of the taxonomy categorizes studies based on which stage of the ensemble learning process is addressed by the evolutionary algorithm: the generation
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OWL ontology evolution: understanding and unifying the complex changes Knowl. Eng. Rev. (IF 2.1) Pub Date : 2022-11-21 Viviane Torres da Silva, Jéssica Soares dos Santos, Raphael Thiago, Elton Soares, Leonardo Guerreiro Azevedo
Knowledge-based systems and their ontologies evolve due to different reasons. Ontology evolution is the adaptation of an ontology and the propagation of these changes to dependent artifacts such as queries and other ontologies. Besides identifying basic/simple changes, it is imperative to identify complex changes between two versions of the same ontology to make this adaptation possible. There are
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A scalable species-based genetic algorithm for reinforcement learning problems Knowl. Eng. Rev. (IF 2.1) Pub Date : 2022-09-19 Anirudh Seth, Alexandros Nikou, Marios Daoutis
Reinforcement Learning (RL) methods often rely on gradient estimates to learn an optimal policy for control problems. These expensive computations result in long training times, a poor rate of convergence, and sample inefficiency when applied to real-world problems with a large state and action space. Evolutionary Computation (EC)-based techniques offer a gradient-free apparatus to train a deep neural
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A framework for belief revision under restrictions Knowl. Eng. Rev. (IF 2.1) Pub Date : 2022-09-12 Zhiguo Long, Hua Meng, Tianrui Li, Heng-Chao Li, Michael Sioutis
Traditional belief revision usually considers generic logic formulas, whilst in practical applications some formulas might even be inappropriate for beliefs. For instance, the formula $p \wedge q$ is syntactically consistent and is also an acceptable belief when there are no restrictions, but it might become unacceptable under restrictions in some context. If we assume that p represents ‘manufacturing
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An online scalarization multi-objective reinforcement learning algorithm: TOPSIS Q-learning Knowl. Eng. Rev. (IF 2.1) Pub Date : 2022-06-13 Mohammad Mirzanejad, Morteza Ebrahimi, Peter Vamplew, Hadi Veisi
Conventional reinforcement learning focuses on problems with single objective. However, many problems have multiple objectives or criteria that may be independent, related, or contradictory. In such cases, multi-objective reinforcement learning is used to propose a compromise among the solutions to balance the objectives. TOPSIS is a multi-criteria decision method that selects the alternative with
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Special issue on ontologies and standards for intelligent systems: editorial Knowl. Eng. Rev. (IF 2.1) Pub Date : 2022-05-13 Joanna Isabelle Olszewska, Julita Bermejo-Alonso, Ricardo Sanz
Day by day, new intelligent systems and autonomous machines are being developed to help and assist humans in a myriad of activities ranging from smart manufacturing to smart cities. Such new-generation intelligent systems need to work in teams and communicate with humans and other agents/robots to share information and coordinate activities. Furthermore, there is an increasing demand from government
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Using Pareto simulated annealing to address algorithmic bias in machine learning Knowl. Eng. Rev. (IF 2.1) Pub Date : 2022-05-04 William Blanzeisky, Pádraig Cunningham
Algorithmic bias arises in machine learning when models that may have reasonable overall accuracy are biased in favor of ‘good’ outcomes for one side of a sensitive category, for example gender or race. The bias will manifest as an underestimation of good outcomes for the under-represented minority. In a sense, we should not be surprised that a model might be biased when it has not been ‘asked’ not
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Merging pruning and neuroevolution: towards robust and efficient controllers for modular soft robots Knowl. Eng. Rev. (IF 2.1) Pub Date : 2022-02-03 Giorgia Nadizar, Eric Medvet, Hola Huse Ramstad, Stefano Nichele, Felice Andrea Pellegrino, Marco Zullich
Artificial neural networks (ANNs) can be employed as controllers for robotic agents. Their structure is often complex, with many neurons and connections, especially when the robots have many sensors and actuators distributed across their bodies and/or when high expressive power is desirable. Pruning (removing neurons or connections) reduces the complexity of the ANN, thus increasing its energy efficiency
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A survey on semantic question answering systems Knowl. Eng. Rev. (IF 2.1) Pub Date : 2022-01-20 Christina Antoniou, Nick Bassiliades
Recently, many question answering systems that derive answers from linked data repositories have been developed. The purpose of this survey is to identify the common features and approaches of the semantic question answering (SQA) systems, although many different and prototype systems have been designed. The SQA systems use a formal query language like SPARQL and knowledge of a specific vocabulary
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Evaluation metrics and dimensional reduction for binary classification algorithms: a case study on bankruptcy prediction Knowl. Eng. Rev. (IF 2.1) Pub Date : 2022-01-14 María E. Pérez-Pons, Javier Parra-Dominguez, Guillermo Hernández, Enrique Herrera-Viedma, Juan M. Corchado
This paper presents a methodology that permits to automate binary classification using the minimum possible number of attributes. In this methodology, the success of the binary prediction does not lie in the accuracy of an algorithm but in the evaluation metrics, which give information about the goodness of fit; which is an important factor when the data batch is unbalanced. The proposed methodology
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Contrastive explanation: a structural-model approach Knowl. Eng. Rev. (IF 2.1) Pub Date : 2021-10-20 Tim Miller
This paper presents a model of contrastive explanation using structural casual models. The topic of causal explanation in artificial intelligence has gathered interest in recent years as researchers and practitioners aim to increase trust and understanding of intelligent decision-making. While different sub-fields of artificial intelligence have looked into this problem with a sub-field-specific view
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A 3-phase approach based on sequential mining and dependency parsing for enhancing hypernym patterns performance Knowl. Eng. Rev. (IF 2.1) Pub Date : 2021-09-22 Ahmad Issa Alaa Aldine, Mounira Harzallah, Giuseppe Berio, Nicolas Béchet, Ahmad Faour
Patterns have been extensively used to extract hypernym relations from texts. The most popular patterns are Hearst’s patterns, formulated as regular expressions mainly based on lexical information. Experiences have reported good precision and low recall for such patterns. Thus, several approaches have been developed for improving recall. While these approaches perform better in terms of recall, it
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Agent mining approaches: an ontological view Knowl. Eng. Rev. (IF 2.1) Pub Date : 2021-08-31 Emmanuelle Grislin-Le Strugeon, Kathia Marcal de Oliveira, Dorsaf Zekri, Marie Thilliez
Introduced as an interdisciplinary area that combines multi-agent systems, data mining and knowledge discovery, agent mining is currently in practice. To develop agent mining applications involves a combination of different approaches (model, architecture, technique and so on) from software agent and data mining (DM) areas. This paper presents an investigation of the approaches used in the agent mining
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Enhancing RFID system configuration through semantic modelling Knowl. Eng. Rev. (IF 2.1) Pub Date : 2021-07-27 Eleni Tsalapati, James Tribe, Paul A. Goodall, Robert I. Young, Thomas W. Jackson, Andrew A. West
Radio-Frequency Identification (RFID) system technology is a key element for the realization of the Industry 4.0 vision, as it is vital for tasks such as entity tracking, identification and asset management. However, the plethora of RFID systems’ elements in combination with the wide range of factors that need to be taken under consideration along with the interrelations amongst them, make the problem
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A comprehensive overview of RDF for spatial and spatiotemporal data management Knowl. Eng. Rev. (IF 2.1) Pub Date : 2021-06-22 Fu Zhang, Qingzhe Lu, Zhenjun Du, Xu Chen, Chunhong Cao
Currently, a large amount of spatial and spatiotemporal RDF data has been shared and exchanged on the Internet and various applications. Resource Description Framework (RDF) is widely accepted for representing and processing data in different (including spatiotemporal) application domains. The effective management of spatial and spatiotemporal RDF data are becoming more and more important. A lot of
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Effective grounding for hybrid planning problems represented in PDDL+ Knowl. Eng. Rev. (IF 2.1) Pub Date : 2021-06-10 Enrico Scala, Mauro Vallati
Automated planning is the field of Artificial Intelligence (AI) that focuses on identifying sequences of actions allowing to reach a goal state from a given initial state. The need of using such techniques in real-world applications has brought popular languages for expressing automated planning problems to provide direct support for continuous and discrete state variables, along with changes that
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Learning multiple concepts in description logic through three perspectives Knowl. Eng. Rev. (IF 2.1) Pub Date : 2021-05-19 Raphael Melo, Kate Revoredo, Aline Paes
An ontology formalises a number of dependent and related concepts in a domain, encapsulated as a terminology. Manually defining such terminologies is a complex, time-consuming and error-prone task. Thus, there is great interest for strategies to learn terminologies automatically. However, most of the existing approaches induce a single concept definition at a time, disregarding dependencies that may
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Fully distributed actor-critic architecture for multitask deep reinforcement learning Knowl. Eng. Rev. (IF 2.1) Pub Date : 2021-04-16 Sergio Valcarcel Macua, Ian Davies, Aleksi Tukiainen, Enrique Munoz de Cote
We propose a fully distributed actor-critic architecture, named diffusion-distributed-actor-critic Diff-DAC, with application to multitask reinforcement learning (MRL). During the learning process, agents communicate their value and policy parameters to their neighbours, diffusing the information across a network of agents with no need for a central station. Each agent can only access data from its
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Safe option-critic: learning safety in the option-critic architecture Knowl. Eng. Rev. (IF 2.1) Pub Date : 2021-04-07 Arushi Jain, Khimya Khetarpal, Doina Precup
Designing hierarchical reinforcement learning algorithms that exhibit safe behaviour is not only vital for practical applications but also facilitates a better understanding of an agent’s decisions. We tackle this problem in the options framework (Sutton, Precup & Singh, 1999), a particular way to specify temporally abstract actions which allow an agent to use sub-policies with start and end conditions
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Argumentation and explainable artificial intelligence: a survey Knowl. Eng. Rev. (IF 2.1) Pub Date : 2021-04-05 Alexandros Vassiliades, Nick Bassiliades, Theodore Patkos
Argumentation and eXplainable Artificial Intelligence (XAI) are closely related, as in the recent years, Argumentation has been used for providing Explainability to AI. Argumentation can show step by step how an AI System reaches a decision; it can provide reasoning over uncertainty and can find solutions when conflicting information is faced. In this survey, we elaborate over the topics of Argumentation
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A generic model for representing openness in multi-agent systems Knowl. Eng. Rev. (IF 2.1) Pub Date : 2021-01-27 Sondes Hattab, Wided Lejouad Chaari
Openness is a challenging property that may characterize multi-agent systems (MAS). It refers to their ability to deal with entities leaving and joining agent society over time. This property makes the MAS behaviour complex and difficult to study and analyze, hence the need for a representative model allowing its understanding. In this context, many models were defined in the literature and we propose
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Special issue on adaptive and learning agents 2018 Knowl. Eng. Rev. (IF 2.1) Pub Date : 2021-01-01 Patrick Mannion,Anna Harutyunyan,Bei Peng,Kaushik Subramanian
1 School of Computer Science, National University of Ireland Galway, University Road, Galway H91 TK33, Ireland e-mail: patrick.mannion@nuigalway.ie 2 DeepMind, 6 Pancras Square, London N1C 4AG, UK e-mail: harutyunyan@google.com 3 Department of Computer Science, University of Oxford, Parks Road, Oxford OX1 3QD, UK e-mail: bei.peng@cs.ox.ac.uk 4 College of Computing, Georgia Institute of Technology,
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Is p-value 0.05 enough? A study on the statistical evaluation of classifiers Knowl. Eng. Rev. (IF 2.1) Pub Date : 2020-11-27 Nadine M. Neumann, Alexandre Plastino, Jony A. Pinto Junior, Alex A. Freitas
Statistical significance analysis, based on hypothesis tests, is a common approach for comparing classifiers. However, many studies oversimplify this analysis by simply checking the condition p-value < 0.05, ignoring important concepts such as the effect size and the statistical power of the test. This problem is so worrying that the American Statistical Association has taken a strong stand on the
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Human–agent transfer from observations Knowl. Eng. Rev. (IF 2.1) Pub Date : 2020-11-27 Bikramjit Banerjee, Sneha Racharla
Learning from human demonstration (LfD), among many speedup techniques for reinforcement learning (RL), has seen many successful applications. We consider one LfD technique called human–agent transfer (HAT), where a model of the human demonstrator’s decision function is induced via supervised learning and used as an initial bias for RL. Some recent work in LfD has investigated learning from observations
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Measuring the strength of threats, rewards, and appeals in persuasive negotiation dialogues Knowl. Eng. Rev. (IF 2.1) Pub Date : 2020-11-12 Mariela Morveli-Espinoza, Juan Carlos Nieves, Cesar Augusto Tacla
The aim of this article is to propose a model for the measurement of the strength of rhetorical arguments (i.e., threats, rewards, and appeals), which are used in persuasive negotiation dialogues when a proponent agent tries to convince his opponent to accept a proposal. Related articles propose a calculation based on the components of the rhetorical arguments, that is, the importance of the goal of
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Jargon of Hadoop MapReduce scheduling techniques: a scientific categorization Knowl. Eng. Rev. (IF 2.1) Pub Date : 2020-09-02 Muhammad Hanif, Choonhwa Lee
Recently, valuable knowledge that can be retrieved from a huge volume of datasets (called Big Data) set in motion the development of frameworks to process data based on parallel and distributed computing, including Apache Hadoop, Facebook Corona, and Microsoft Dryad. Apache Hadoop is an open source implementation of Google MapReduce that attracted strong attention from the research community both in
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Toward an interdisciplinary integration between multi-agents systems and multi-robots systems: a case study Knowl. Eng. Rev. (IF 2.1) Pub Date : 2020-08-13 Wagner Tanaka Botelho, Maria Das Graças Bruno Marietto, Eduardo De Lima Mendes, Daniel Rodrigues De Sousa, Edson Pinheiro Pimentel, Vera Lúcia da Silva, Tamires dos Santos
Multi-Robot System (MRS) is composed of a group of robots that work cooperatively. However, Multi-Agent System (MAS) is computational systems consisting of a group of agents that interact with each other to solve a problem. The central difference between MRS and MAS is that in the first case, the agent is a robot, and in the second, it is a software. Analyzing the scientific literature, it is possible
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A consensual dataset for complex ontology matching evaluation Knowl. Eng. Rev. (IF 2.1) Pub Date : 2020-07-07 Elodie Thiéblin, Michelle Cheatham, Cassia Trojahn, Ondrej Zamazal
Simple ontology alignments, largely studied in the literature, link one single entity of a source ontology to one single entity of a target ontology. One of the limitations of these alignments is, however, their lack of expressiveness, which can be overcome by complex alignments, which are composed of correspondences involving logical constructors or transformation functions. While most work on complex
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Crowd-assessing quality in uncertain data linking datasets Knowl. Eng. Rev. (IF 2.1) Pub Date : 2020-07-02 Daniel Faria, Alfio Ferrara, Ernesto Jiménez-ruiz, Stefano Montanelli, Catia Pesquita
The quality of a dataset used for evaluating data linking methods, techniques, and tools depends on the availability of a set of mappings, called reference alignment, that is known to be correct. In particular, it is crucial that mappings effectively represent relations between pairs of entities that are indeed similar due to the fact that they denote the same object. Since the reliability of mappings
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A utility-based analysis of equilibria in multi-objective normal-form games Knowl. Eng. Rev. (IF 2.1) Pub Date : 2020-06-30 Roxana Rădulescu, Patrick Mannion, Yijie Zhang, Diederik M. Roijers, Ann Nowé
In multi-objective multi-agent systems (MOMASs), agents explicitly consider the possible trade-offs between conflicting objective functions. We argue that compromises between competing objectives in MOMAS should be analyzed on the basis of the utility that these compromises have for the users of a system, where an agent’s utility function maps their payoff vectors to scalar utility values. This utility-based
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Effects of parity, sympathy and reciprocity in increasing social welfare Knowl. Eng. Rev. (IF 2.1) Pub Date : 2020-06-23 Sandip Sen, Chad Crawford, Adam Dees, Rachna Nanda Kumar, James Hale
We are interested in understanding how socially desirable traits like sympathy, reciprocity, and fairness can survive in environments that include aggressive and exploitative agents. Social scientists have long theorized about ingrained motivational factors as explanations for departures from self-seeking behaviors by human subjects. Some of these factors, namely reciprocity, have also been studied
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The merits of using Ethereum MainNet as a Coordination Blockchain for Ethereum Private Sidechains Knowl. Eng. Rev. (IF 2.1) Pub Date : 2020-06-22 Peter Robinson
A Coordination Blockchain is a blockchain that coordinates activities of multiple private blockchains. This paper discusses the pros and cons of using Ethereum MainNet, the public Ethereum blockchain, as a Coordination Blockchain. The requirements Ethereum MainNet needs to fulfil to perform this role are analyzed within the context of Ethereum Private Sidechains, a private blockchain technology which
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Design patterns for modeling first-order expressive Bayesian networks Knowl. Eng. Rev. (IF 2.1) Pub Date : 2020-06-17 Mark Locher, Kathryn B. Laskey, Paulo C. G. Costa
First-order expressive capabilities allow Bayesian networks (BNs) to model problem domains where the number of entities, their attributes, and their relationships can vary significantly between model instantiations. First-order BNs are well-suited for capturing knowledge representation dependencies, but literature on design patterns specific to first-order BNs is few and scattered. To identify useful
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Improving trust and reputation assessment with dynamic behaviour Knowl. Eng. Rev. (IF 2.1) Pub Date : 2020-06-17 Caroline Player, Nathan Griffiths
Trust between agents in multi-agent systems (MASs) is critical to encourage high levels of cooperation. Existing methods to assess trust and reputation use direct and indirect past experiences about an agent to estimate their future performance; however, these will not always be representative if agents change their behaviour over time. Real-world distributed networks such as online market places,
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TEduChain: a blockchain-based platform for crowdfunding tertiary education Knowl. Eng. Rev. (IF 2.1) Pub Date : 2020-06-09 Mahmood A. Rashid, Krishneel Deo, Divnesh Prasad, Kunal Singh, Sarvesh Chand, Mansour Assaf
Blockchain is an emerging technology framework for creating and storing transaction in distributed ledgers with a high degree of security and reliability. In this paper, we present a blockchain-based platform to create and store contracts in between students and their higher education sponsors facilitated by intermediary brokers denoted as fundraisers. The sponsorship might be in any form, such as
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Adaptable and stable decentralized task allocation for hierarchical domains Knowl. Eng. Rev. (IF 2.1) Pub Date : 2020-06-04 Vera A. Kazakova, Gita R. Sukthankar
Many real-world domains can benefit from adaptable decentralized task allocation through emergent specialization, especially in large teams of non-communicating agents. We begin with an existing bio-inspired response threshold reinforcement approach for decentralized task allocation and extend it to handle hierarchical task domains. We test the extension on self-deployment of a large team of non-communicating
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Ontologies for cloud robotics Knowl. Eng. Rev. (IF 2.1) Pub Date : 2020-06-02 Edison Pignaton de Freitas, Julita Bermejo-Alonso, Alaa Khamis, Howard Li, Joanna Isabelle Olszewska
Cloud robotics (CR) is currently a growing area in the robotic community. Indeed, the use of cloud computing to share data and resources of distributed robotic systems leads to the design and development of cloud robotic systems (CRS) which constitute useful technologies for a wide range of applications such as smart manufacturing, aid and rescue missions. However, in order to get coherent agent-to-cloud
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On the impact of architecture design decisions on the quality of blockchain-based applications Knowl. Eng. Rev. (IF 2.1) Pub Date : 2020-06-02 Diego Marmsoler, Leo Eichhorn
In software architectures, architectural design decisions (ADDs) strongly influence the quality of the resulting software system. Wrong decisions lead to low-quality systems and are difficult to repair later on in the development process. As of today, little is known about the impact of certain ADDs for the development of architectures for blockchain-based systems. Thus, it is difficult to predict
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Cross-chain interoperability among blockchain-based systems using transactions Knowl. Eng. Rev. (IF 2.1) Pub Date : 2020-06-01 Babu Pillai, Kamanashis Biswas, Vallipuram Muthukkumarasamy
The blockchain is an emerging technology which has the potential to improve many information systems. In this regard, the applications and the platform they are built on must be able to connect and communicate with each other. However, the current blockchain platforms have several limitations, such as lack of interoperability among different systems. The existing platforms of blockchain applications
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Towards evaluating complex ontology alignments Knowl. Eng. Rev. (IF 2.1) Pub Date : 2020-05-29 Lu Zhou, Elodie Thiéblin, Michelle Cheatham, Daniel Faria, Catia Pesquita, Cassia Trojahn, Ondřej Zamazal
The development of semi-automated and automated ontology alignment techniques is an important part of realizing the potential of the Semantic Web. Until very recently, most existing work in this area was focused on finding simple (1:1) equivalence correspondences between two ontologies. However, many real-world ontology pairs involve correspondences that contain multiple entities from each ontology
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First human–robot archery competition: a new humanoid robot challenge Knowl. Eng. Rev. (IF 2.1) Pub Date : 2020-05-28 Kuo-Yang Tu, Hong-Yu Lin, You-Ru Li, Che-Ping Hung, Jacky Baltes
A humanoid robot developed to play multievent athletes like human has paved a way for interesting and popular robotics research. One of the great dreams is to develop a humanoid robot being able to challenge human athletes. Therefore, the challenge of humanoid robots to play archery against human is organized at Taichung, Taiwan, in HuroCup, FIRA 2018, on August 7th. The difficulties of developing
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A blockchain-based database management system Knowl. Eng. Rev. (IF 2.1) Pub Date : 2020-05-18 Jeyakumar Samantha Tharani, Mukunthan Tharmakulasingam, Vallipuram Muthukkumarasamy
The software and hardware applications are clearly on the way of becoming an integral tool of business, communication and popular culture in many parts of the world. People are interacting with the environment via the Internet to perform physical activities remotely. These applications are hosted in the public or private servers under the control of the server admin. The users’ online usage data can
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A survey on blockchain-based platforms for IoT use-cases Knowl. Eng. Rev. (IF 2.1) Pub Date : 2020-05-08 Mohammad Jabed Morshed Chowdhury, Md Sadek Ferdous, Kamanashis Biswas, Niaz Chowdhury, Vallipuram Muthukkumarasamy
The Internet of Things (IoT) has recently emerged as an innovative technology capable of empowering various areas such as healthcare, agriculture, smart cities, smart homes and supply chain with real-time and state-of-the-art sensing capabilities. Due to the underlying potential of this technology, it already saw exponential growth in a wide variety of use-cases in multiple application domains. As
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A blockchain-based decentralized booking system Knowl. Eng. Rev. (IF 2.1) Pub Date : 2020-05-04 Naipeng Dong, Guangdong Bai, Lung-Chen Huang, Edmund Kok Heng Lim, Jin Song Dong
Blockchain technology has rapidly emerged as a decentralized trusted network to replace the traditional centralized intermediator. Especially, the smart contracts that are based on blockchain allow users to define the agreed behaviour among them, the execution of which will be enforced by the smart contracts. Based on this, we propose a decentralized booking system that uses the blockchain as the intermediator
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A review and comparison of ontology-based approaches to robot autonomy – ADDENDUM Knowl. Eng. Rev. (IF 2.1) Pub Date : 2020-02-06 Alberto Olivares-Alarcos, Daniel Beßler, Alaa Khamis, Paulo Goncalves, Maki K. Habib, Julita Bermejo-Alonso, Marcos Barreto, Mohammed Diab, Jan Rosell, João Quintas, Joanna Olszewska, Hirenkumar Nakawala, Edison Pignaton, Amelie Gyrard, Stefano Borgo, Guillem Alenyà, Michael Beetz, Howard Li
Within the next decades, robots will need to be able to execute a large variety of tasks autonomously in a large variety of environments. To relax the resulting programming effort, a knowledge-enabled approach to robot programming can be adopted to organize information in re-usable knowledge pieces. However, for the ease of reuse, there needs to be an agreement on the meaning of terms. A common approach
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Orchestrating DDoS mitigation via blockchain-based network provider collaborations Knowl. Eng. Rev. (IF 2.1) Pub Date : 2020-04-14 Adam Pavlidis, Marinos Dimolianis, Kostas Giotis, Loukas Anagnostou, Nikolaos Kostopoulos, Theocharis Tsigkritis, Ilias Kotinas, Dimitrios Kalogeras, Vasilis Maglaris
Network providers either attempt to handle massive distributed denial-of-service attacks themselves or redirect traffic to third-party scrubbing centers. If providers adopt the first option, it is sensible to counter such attacks in their infancy via provider collaborations deploying distributed security mechanisms across multiple domains in an attack path. This motivated our work presented in this
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Robot magic show: human–robot interaction Knowl. Eng. Rev. (IF 2.1) Pub Date : 2020-04-07 Jaesik Jeong, Jeehyun Yang, Jacky Baltes
The use of robots in performance arts is increasing. But, it is hard for robots to cope with unexpected circumstances during a performance, and it is almost impossible for robots to act fully autonomously in such situations. IROS-HAC is a new challenge in robotics research and a new opportunity for cross-disciplinary collaborative research. In this paper, we describe a practical method for generating
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SANOM-HOBBIT: simulated annealing-based ontology matching on HOBBIT platform Knowl. Eng. Rev. (IF 2.1) Pub Date : 2020-03-31 Majid Mohammadi, Wout Hofman, Yao-Hua Tan
Ontology alignment is an important and inescapable problem for the interconnections of two ontologies stating the same concepts. Ontology alignment evaluation initiative (OAEI) has been taken place for more than a decade to monitor and help the progress of the field and to compare systematically existing alignment systems. As of 2018, the evaluation of systems is partly transitioned to the HOBBIT platform
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Regulatory changes for redesigned securities markets with distributed ledger technology Knowl. Eng. Rev. (IF 2.1) Pub Date : 2020-03-31 Muthukkumarasamy Thuvarakan
Distributed ledger technology (DLT) is regarded as a revolutionary solution that offers immutability, transparency, trust, and efficiency while ‘transcending law and regulation’. One of the potential applications of DLT is in the securities market. Share registration, settlement, regulatory compliance, information disclosure, payment systems, and market service requirements can be redesigned with the
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A loyalty program based on Waves blockchain and mobile phone interactions Knowl. Eng. Rev. (IF 2.1) Pub Date : 2020-03-30 Luis J. Dominguez Perez, Luis Ibarra, García-Fernández Alejandro, Agustín Rumayor, Carlos Lara-Alvarez
Loyalty cards programs have been used by retailers to increase customer retention. Loyality cards provide means to identify a particular customer and to collect customer-specific data, thus enabling individualized marketing; however, operating a loyalty program is complicated for retailers since they require to manage balances, collections, and transfers of customers. This is exactly the same problem
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Learning self-play agents for combinatorial optimization problems Knowl. Eng. Rev. (IF 2.1) Pub Date : 2020-03-23 Ruiyang Xu, Karl Lieberherr
Recent progress in reinforcement learning (RL) using self-play has shown remarkable performance with several board games (e.g., Chess and Go) and video games (e.g., Atari games and Dota2). It is plausible to hypothesize that RL, starting from zero knowledge, might be able to gradually approach a winning strategy after a certain amount of training. In this paper, we explore neural Monte Carlo Tree Search