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

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

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

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

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

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

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

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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

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

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

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

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

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

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

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

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

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,

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 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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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