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  • Learning self-play agents for combinatorial optimization problems
    Knowl. Eng. Rev. (IF 0.814) 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

  • Legal smart contracts for derivative trading in mining
    Knowl. Eng. Rev. (IF 0.814) Pub Date : 2020-03-19
    Julian Adam Wise; Meng Chak Chan; Dihon Tadic; Stephanie Miles; Jack Cornish; Ewan Sellers; David Brenecki; Isaac Dzakpata; Barti Murugesan

    This research demonstrates financial derivative trade of unprocessed materials, for the mining industry through legal smart contracts. Within the mining supply chain, a stock of mined resources can reside in a mineral stockpile for over twenty years without gaining financial interest and without undergoing the mineral extraction process to derive value from the asset. This research elaborates on a

  • Data preprocessing in predictive data mining
    Knowl. Eng. Rev. (IF 0.814) Pub Date : 2019-01-09
    Stamatios-Aggelos N. Alexandropoulos; Sotiris B. Kotsiantis; Michael N. Vrahatis

    A large variety of issues influence the success of data mining on a given problem. Two primary and important issues are the representation and the quality of the dataset. Specifically, if much redundant and unrelated or noisy and unreliable information is presented, then knowledge discovery becomes a very difficult problem. It is well-known that data preparation steps require significant processing

  • Dimensions in programming multi-agent systems
    Knowl. Eng. Rev. (IF 0.814) Pub Date : 2019-01-14
    Olivier Boissier; Rafael H. Bordini; Jomi F. Hübner; Alessandro Ricci

    Research on Multi-Agent Systems (MAS) has led to the development of several models, languages, and technologies for programming not only agents, but also their interaction, the application environment where they are situated, as well as the organization in which they participate. Research on those topics moved from agent-oriented programming towards multi-agent-oriented programming (MAOP). A MAS program

  • Principles and practice of multi-agent systems
    Knowl. Eng. Rev. (IF 0.814) Pub Date : 2019-02-05
    Qingliang Chen; Paolo Torroni; Serena Villata


  • Jargon of Hadoop MapReduce scheduling techniques: a scientific categorization
    Knowl. Eng. Rev. (IF 0.814) Pub Date : 2019-03-15
    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

  • A review of generalized planning
    Knowl. Eng. Rev. (IF 0.814) Pub Date : 2019-03-12
    Sergio Jiménez; Javier Segovia-Aguas; Anders Jonsson

    Generalized planning studies the representation, computation and evaluation of solutions that are valid for multiple planning instances. These are topics studied since the early days of AI. However, in recent years, we are experiencing the appearance of novel formalisms to compactly represent generalized planning tasks, the solutions to these tasks (called generalized plans) and efficient algorithms

  • Predictive models and abstract argumentation: the case of high-complexity semantics
    Knowl. Eng. Rev. (IF 0.814) Pub Date : 2019-04-18
    Mauro Vallati; Federico Cerutti; Massimiliano Giacomin

    In this paper, we describe how predictive models can be positively exploited in abstract argumentation. In particular, we present two main sets of results. On one side, we show that predictive models are effective for performing algorithm selection in order to determine which approach is better to enumerate the preferred extensions of a given argumentation framework. On the other side, we show that

  • Short-text learning in social media: a review
    Knowl. Eng. Rev. (IF 0.814) Pub Date : 2019-06-06
    Antonela Tommasel; Daniela Godoy

    Social networks occupy a ubiquitous and pervasive place in the life of their users. The substantial amount of content generated and shared by social networking users offers new research opportunities across a wide variety of disciplines, including media and communication studies, linguistics, sociology, psychology, information and computer sciences, or education. This situation, in combination with

  • Introspective Q-learning and learning from demonstration
    Knowl. Eng. Rev. (IF 0.814) Pub Date : 2019-01-01
    Mao Li; Tim Brys; Daniel Kudenko

    One challenge faced by reinforcement learning (RL) agents is that in many environments the reward signal is sparse, leading to slow improvement of the agent’s performance in early learning episodes. Potential-based reward shaping can help to resolve the aforementioned issue of sparse reward by incorporating an expert’s domain knowledge into the learning through a potential function. Past work on reinforcement

  • Time-sensitive resource re-allocation strategy for interdependent continuous tasks
    Knowl. Eng. Rev. (IF 0.814) Pub Date : 2019-07-22
    Valeriia Haberland; Simon Miles; Michael Luck

    An increase in volumes of data and a shift towards live data enabled a stronger focus on resource-intensive tasks which run continuously over long periods. A Grid has potential to offer the required resources for these tasks, while considering a fair and balanced allocation of resources among multiple client agents. Taking this into account, a Grid might be unwilling to allocate its resources for long

  • Pre-training with non-expert human demonstration for deep reinforcement learning
    Knowl. Eng. Rev. (IF 0.814) Pub Date : 2019-07-26
    Gabriel V. de la Cruz; Yunshu Du; Matthew E. Taylor

    Deep reinforcement learning (deep RL) has achieved superior performance in complex sequential tasks by using deep neural networks as function approximators to learn directly from raw input images. However, learning directly from raw images is data inefficient. The agent must learn feature representation of complex states in addition to learning a policy. As a result, deep RL typically suffers from

  • Automatic landmark discovery for learning agents under partial observability
    Knowl. Eng. Rev. (IF 0.814) Pub Date : 2019-08-02
    Alper Demіr; Erkіn Çіlden; Faruk Polat

    In the reinforcement learning context, a landmark is a compact information which uniquely couples a state, for problems with hidden states. Landmarks are shown to support finding good memoryless policies for Partially Observable Markov Decision Processes (POMDP) which contain at least one landmark. SarsaLandmark, as an adaptation of Sarsa(λ), is known to promise a better learning performance with the

  • Team learning from human demonstration with coordination confidence
    Knowl. Eng. Rev. (IF 0.814) Pub Date : 2019-11-05
    Bikramjit Banerjee; Syamala Vittanala; Matthew Edmund Taylor

    Among an array of techniques proposed to speed-up reinforcement learning (RL), learning from human demonstration has a proven record of success. A related technique, called Human-Agent Transfer, and its confidence-based derivatives have been successfully applied to single-agent RL. This article investigates their application to collaborative multi-agent RL problems. We show that a first-cut extension

  • Action learning and grounding in simulated human–robot interactions
    Knowl. Eng. Rev. (IF 0.814) Pub Date : 2019-11-12
    Oliver Roesler; Ann Nowé

    In order to enable robots to interact with humans in a natural way, they need to be able to autonomously learn new tasks. The most natural way for humans to tell another agent, which can be a human or robot, to perform a task is via natural language. Thus, natural human–robot interactions also require robots to understand natural language, i.e. extract the meaning of words and phrases. To do this,

  • Two-level Q-learning: learning from conflict demonstrations
    Knowl. Eng. Rev. (IF 0.814) Pub Date : 2019-11-12
    Mao Li; Yi Wei; Daniel Kudenko

    One way to address this low sample efficiency of reinforcement learning (RL) is to employ human expert demonstrations to speed up the RL process (RL from demonstration or RLfD). The research so far has focused on demonstrations from a single expert. However, little attention has been given to the case where demonstrations are collected from multiple experts, whose expertise may vary on different aspects

  • User validation in ontology alignment: functional assessment and impact
    Knowl. Eng. Rev. (IF 0.814) Pub Date : 2019-11-14
    Huanyu Li; Zlatan Dragisic; Daniel Faria; Valentina Ivanova; Ernesto Jiménez-Ruiz; Patrick Lambrix; Catia Pesquita

    User validation is one of the challenges facing the ontology alignment community, as there are limits to the quality of the alignments produced by automated alignment algorithms. In this paper, we present a broad study on user validation of ontology alignments that encompasses three distinct but inter-related aspects: the profile of the user, the services of the alignment system, and its user interface

  • Localization and obstacle avoidance in soccer competition of humanoid robot by gait and vision system
    Knowl. Eng. Rev. (IF 0.814) Pub Date : 2019-11-20
    Shu-Yin Chiang; Jia-Huei Lu

    In this study, we designed a localization and obstacle avoidance system for humanoid robots in the Federation of International Robot-soccer Association (FIRA) HuroCup united soccer competition event. The localization is implemented by using grid points, gait, and steps to determine the positions of each robot. To increase the localization accuracy and eliminate the accumulated distance errors resulting

  • Ontologies for Industry 4.0
    Knowl. Eng. Rev. (IF 0.814) Pub Date : 2019-11-22
    Veera Ragavan Sampath Kumar; Alaa Khamis; Sandro Fiorini; Joel Luís Carbonera; Alberto Olivares Alarcos; Maki Habib; Paulo Goncalves; Howard Li; Joanna Isabelle Olszewska

    The current fourth industrial revolution, or ‘Industry 4.0’ (I4.0), is driven by digital data, connectivity, and cyber systems, and it has the potential to create impressive/new business opportunities. With the arrival of I4.0, the scenario of various intelligent systems interacting reliably and securely with each other becomes a reality which technical systems need to address. One major aspect of

  • A sketch drawing humanoid robot using image-based visual servoing
    Knowl. Eng. Rev. (IF 0.814) Pub Date : 2019-11-25
    Meng-Cheng Lau; John Anderson; Jacky Baltes

    This paper presents our sketch drawing artist humanoid robot research. One of the limitations of the existing artist humanoid robot is the lack of feedback on the error that occurs during the drawing process. The contribution of this research is the development of a humanoid robot artist with drawing error correction capability. Based on our previous work with open-loop control pen-and-ink humanoid

  • A multi-objective evolutionary hyper-heuristic algorithm for team-orienteering problem with time windows regarding rescue applications
    Knowl. Eng. Rev. (IF 0.814) Pub Date : 2019-12-02
    Hadi S. Aghdasi; Saeed Saeedvand; Jacky Baltes

    The team-orienteering problem (TOP) has broad applicability. Examples of possible uses are in factory and automation settings, robot sports teams, and urban search and rescue applications. We chose the rescue domain as a guiding example throughout this paper. Hence, this paper explores a practical variant of TOP with time window (TOPTW) for rescue applications by humanoid robots called TOPTWR. Due

  • A comprehensive survey on humanoid robot development
    Knowl. Eng. Rev. (IF 0.814) Pub Date : 2019-12-03
    Saeed Saeedvand; Masoumeh Jafari; Hadi S. Aghdasi; Jacky Baltes

    The development of a versatile, fully-capable humanoid robot as envisioned in science fiction books is one of the most challenging but interesting issues in the robotic field. Currently, existing humanoid robots are designed with different purposes and applications in mind. In humanoid robot development process, each robot is designed with various characteristics, abilities, and equipment, which influence

  • Coupling the normative regulation with the constitutive state management in Situated Artificial Institutions
    Knowl. Eng. Rev. (IF 0.814) Pub Date : 2019-12-04
    Maiquel De Brito; Jomi Fred Hübner; Olivier Boissier

    Artificial Institutions are systems where the regulation defined through norms is based on an interpretation of the concrete world where the agents are situated and interact. Such interpretation can be defined through constitutive rules. The literature proposes independent approaches for the definition and management of both norms and constitutive rules. However, they are usually either not coupled

  • Imitation of human motion for humanoid robot in lift and carry event
    Knowl. Eng. Rev. (IF 0.814) Pub Date : 2019-12-03
    Shu-Yin Chiang; Hao-Ge Jiang

    This study proposed a method to enable a humanoid robot to step up onto a stair by imitating the step-up motion of a human and to accomplish a lift and carry event in HuroCup of Federation of International RoboSports Association. The step-up motion, divided into five states, was captured by a Kinect sensor, and the human joints corresponded to the humanoid robot joints. Selected servomotors and their

  • Adaptive computational SLAM incorporating strategies of exploration and path planning
    Knowl. Eng. Rev. (IF 0.814) Pub Date : 2019-12-02
    Jacky Baltes; Da-Wei Kung; Wei-Yen Wang; Chen-Chien Hsu

    Simultaneous localization and mapping (SLAM) is a well-known and fundamental topic for autonomous robot navigation. Existing solutions include the FastSLAM family-based approaches which are based on Rao–Blackwellized particle filter. The FastSLAM methods slow down greatly when the number of landmarks becomes large. Furthermore, the FastSLAM methods use a fixed number of particles, which may result

  • TempCourt: evaluation of temporal taggers on a new corpus of court decisions
    Knowl. Eng. Rev. (IF 0.814) Pub Date : 2019-12-17
    María Navas-Loro; Erwin Filtz; Víctor Rodríguez-Doncel; Axel Polleres; Sabrina Kirrane

    The extraction and processing of temporal expressions (TEs) in textual documents have been extensively studied in several domains; however, for the legal domain it remains an open challenge. This is possibly due to the scarcity of corpora in the domain and the particularities found in legal documents that are highlighted in this paper. Considering the pivotal role played by temporal information when

  • The average speed of motion and optimal power consumption in biped robots
    Knowl. Eng. Rev. (IF 0.814) Pub Date : 2019-12-19
    Vida Shams Esfanabadi; Mostafa Rostami; Seyed Mohammadali Rahmati; Jacky Baltes; Soroush Sadeghnejad

    One of the issues that have garnered little attention, but that is nevertheless important for developing practical robots, is optimal walking conditions like power consumption during walking. The main contribution of this research is to prepare a correct walking pattern for humans who have a problem with their walking and also study the effect of average motion speed on optimal power consumption. In

  • Designing emotional BDI agents: good practices and open questions
    Knowl. Eng. Rev. (IF 0.814) Pub Date : 2019-12-17
    Yanet Sánchez-López; Eva Cerezo

    Intelligent agents built on the basis of the BDI (belief–desire–intention) architecture are known as BDI agents. Currently, due to the increasing importance given to the affective capacities, they have evolved giving way to the BDI emotional agents. These agents are generally characterized by affective states such as emotions, mood or personality but sometimes also by affective capacities such as empathy

  • Dr. Eureka: a humanoid robot manipulation case study
    Knowl. Eng. Rev. (IF 0.814) Pub Date : 2019-12-19
    Lin Yu-Ren; Guilherme Henrique Galelli Christmann; Ricardo Bedin Grando; Rodrigo Da Silva Guerra; Jacky Baltes

    To this day, manipulation still stands as one of the hardest challenges in robotics. In this work, we examine the board game Dr. Eureka as a benchmark to encourage further development in the field. The game consists of a race to solve a manipulation puzzle: reordering colored balls in transparent tubes, in which the solution requires planning, dexterity and agility. In this work, we present a robot

  • Artificial intelligence for team sports: a survey
    Knowl. Eng. Rev. (IF 0.814) Pub Date : 2019-12-20
    Ryan Beal; Timothy J. Norman; Sarvapali D. Ramchurn

    The sports domain presents a number of significant computational challenges for artificial intelligence (AI) and machine learning (ML). In this paper, we explore the techniques that have been applied to the challenges within team sports thus far. We focus on a number of different areas, namely match outcome prediction, tactical decision making, player investments, fantasy sports, and injury prediction

  • A review and comparison of ontology-based approaches to robot autonomy
    Knowl. Eng. Rev. (IF 0.814) Pub Date : 2019-12-27
    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

  • Alin: improving interactive ontology matching by interactively revising mapping suggestions
    Knowl. Eng. Rev. (IF 0.814) Pub Date : 2020-01-20
    Jomar Da Silva; Kate Revoredo; Fernanda Baião; Jérôme Euzenat

    Ontology matching aims at discovering mappings between the entities of two ontologies. It plays an important role in the integration of heterogeneous data sources that are described by ontologies. Interactive ontology matching involves domain experts in the matching process. In some approaches, the expert provides feedback about mappings between ontology entities, that is, these approaches select mappings

  • Blockchain’s future: can the decentralized blockchain community succeed in creating standards?
    Knowl. Eng. Rev. (IF 0.814) Pub Date : 2020-01-28
    John Flood; Adrian McCullagh

    Nakamoto proposed a new solution to transact value via the internet. And since 2009, blockchain technology has expanded and diversified. It has, however, proven to be inefficient in the way it achieves its outcomes, especially through the proof of work protocol. Other developers are promoting alternative methods but, as yet, none has superseded proof of work. The competing protocols illuminate a key

  • Immutable autobiography of smart cars leveraging blockchain technology
    Knowl. Eng. Rev. (IF 0.814) Pub Date : 2020-01-27
    MD. Sadek Ferdous; Mohammad Jabed Morshed Chowdhury; Kamanashis Biswas; Niaz Chowdhury; Vallipuram Muthukkumarasamy

    The popularity of smart cars is increasing around the world as they offer a wide range of services and conveniences. These smart cars are equipped with a variety of sensors generating a large amount of data, many of which are critical. Besides, there are multiple parties involved in the lifespan of a smart car, such as manufacturers, car owners, government agencies, and third-party service providers

  • Ontology engineering methodologies for the evolution of living and reused ontologies: status, trends, findings and recommendations
    Knowl. Eng. Rev. (IF 0.814) Pub Date : 2020-01-31
    Konstantinos I. Kotis; George A. Vouros; Dimitris Spiliotopoulos

    The aim of this critical review paper is threefold: (a) to provide an insight on the impact of ontology engineering methodologies (OEMs) to the evolution of living and reused ontologies, (b) to update the ontology engineering (OE) community on the status and trends in OEMs and of their use in practice and (c) to propose a set of recommendations for working ontologists to consider during the life cycle

  • A review and comparison of ontology-based approaches to robot autonomy – ADDENDUM
    Knowl. Eng. Rev. (IF 0.814) 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

  • Enacting policies in digital health: a case for smart legal contracts and distributed ledgers?
    Knowl. Eng. Rev. (IF 0.814) Pub Date : 2020-02-12
    Zoran Milosevic

    This paper presents an approach for the enactment of policies in digital health based on our earlier work on the implementation of digital contracts in distributed systems. A formal policy model and an abstract policy language for the expression of healthcare policies are first proposed, leveraging the semantics of the ISO Reference Model for Open Distributed Processing enterprise language standard

  • Domain adaptation-based transfer learning using adversarial networks
    Knowl. Eng. Rev. (IF 0.814) Pub Date : 2020-02-26
    Farzaneh Shoeleh; Mohammad Mehdi Yadollahi; Masoud Asadpour

    There is an implicit assumption in machine learning techniques that each new task has no relation to the tasks previously learned. Therefore, tasks are often addressed independently. However, in some domains, particularly reinforcement learning (RL), this assumption is often incorrect because tasks in the same or similar domain tend to be related. In other words, even though tasks are quite different

  • Toll-based reinforcement learning for efficient equilibria in route choice
    Knowl. Eng. Rev. (IF 0.814) Pub Date : 2020-03-05
    Gabriel de O. Ramos; Bruno C. Da Silva; Roxana Rădulescu; Ana L. C. Bazzan; Ann Nowé

    The problem of traffic congestion incurs numerous social and economical repercussions and has thus become a central issue in every major city in the world. For this work we look at the transportation domain from a multiagent system perspective, where every driver can be seen as an autonomous decision-making agent. We explore how learning approaches can help achieve an efficient outcome, even when agents

  • Matching biodiversity and ecology ontologies: challenges and evaluation results
    Knowl. Eng. Rev. (IF 0.814) Pub Date : 2020-03-09
    Naouel Karam; Abderrahmane Khiat; Alsayed Algergawy; Melanie Sattler; Claus Weiland; Marco Schmidt

    Biodiversity research studies the variability and diversity of organisms, including variability within and between species with particular focus on the functional diversity of traits and their relationship to environment. Managing biodiversity data implies dealing with its heterogeneous nature using semantics and tailored ontologies. These are themselves differently conceived, and combining them in

  • Learning Qualitative Differential Equation models: a survey of algorithms and applications.
    Knowl. Eng. Rev. (IF 0.814) Pub Date : 2010-03-01
    Wei Pang,George M Coghill

    Over the last two decades, qualitative reasoning (QR) has become an important domain in Artificial Intelligence. QDE (Qualitative Differential Equation) model learning (QML), as a branch of QR, has also received an increasing amount of attention; many systems have been proposed to solve various significant problems in this field. QML has been applied to a wide range of fields, including physics, biology

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