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. These ‘complex’ alignments pose a challenge for existing evaluation approaches, which hinders the development of new systems capable of finding such correspondences. This position paper surveys and analyzes the requirements for effective evaluation of complex ontology alignments and assesses the degree to which these requirements are met by existing approaches. It also provides a roadmap for future work on this topic taking into consideration emerging community initiatives and major challenges that need to be addressed.

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 humanoid robot are not just on playing archery. The humanoid robots for HuroCup must make use of the same hardware for the 10 events. In this paper, the design and implementation of the humanoid robot for archery are proposed under the trade off with other nine events. Therefore, the humanoid robot must have some special design and development on software. More specially, the humanoid robot must use professional bow to challenge human for archery competition. Therefore, in this paper, special shooting posture under constrained arm structure and motion planning of both arms for more torque to play professional bow are proposed. In addition, the further development of humanoid robot to improve archery shooting is summarized.

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 operate only within their own networks. Even though the underlying technology is similar, it relies on centralized third-party mediators to exchange or retrieve information from other blockchain networks. The current third-party intermediaries establish trust and security by preserving a centralized ledger to track ‘account balances’ and vouch for a transaction’s authenticity. The inability for independent blockchains to communicate with one another is an inherent problem in the decentralized systems. Lack of appropriate inter-blockchain communication puts a strain on the mainstream adoption of blockchain. It is evident that blockchain technology has the potential to become a suitable solution for some systems if it can scale and is able to cross communicate with other systems. For the multisystem blockchain concept to become a reality, a mechanism is required that would connect and communicate with multiple entities’ blockchain systems in a distributed fashion (without any intermediary), while maintaining the property of trust and integrity built by individual blockchains. In this article, we propose a mechanism that provides cross-chain interoperability using transactions.

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 the outcome of certain ADDs when developing architectures for such systems. In the following, we propose a simulation-based approach for blockchain architectures in which the impact of certain ADDs on certain quality attributes can be simulated. To this end, we first implemented a simulation environment for blockchain architectures. The simulation environment was then used to execute a series of experiments from which we derived a set of hypotheses about the impact of certain ADDs on quality attributes for blockchain architectures. Finally, we tested the hypotheses using statistical analyses and derived an empirical model for blockchain architectures based on the outcome of the analysis. The model can be used by architects to predict the effect of certain decisions in the design of blockchain architectures before implementing them.

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 communications and efficient agent-to-agent collaboration within these CRS, there is a need to formalize the knowledge representation in CR. Hence, the use of ontologies provides a mean to define formal concepts and their relations in an interoperable way. This paper presents standard robotic ontologies and their extension in the CR domain as well as their possible implementations in the case of a real-world CR scenario.

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 agents to patrolling a hierarchically defined set of areas. Results show near-ideal performance across all areas, while minimizing wasteful task switching through the development of specializations and subsequent respecializations when area demands change. A genetic algorithm is then used to evolve even more adaptable and stable task allocation behavior, by incorporating weight and power coefficients into agents’ response threshold reinforcement action probability calculations.

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 scholarship, donation, or loan. The fund will be arranged and managed by a group of competitive fundraisers who will hold the distributed ledgers and act as the miners in the blockchain network.

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 patterns, we investigated the range of dependency models between combinations of random variables (RVs) that represent unary attributes, functional relationships, and binary predicate relationships. We found eight major patterns, grouped into three categories, that cover a significant number of first-order BN situations. Selection behavior occurs in six patterns, where a relationship/attribute identifies which entities in a second relationship/attribute are applicable. In other cases, certain kinds of embedded dependencies based on semantic meaning are exploited. A significant contribution of our patterns is that they describe various behaviors used to establish the RV’s local probability distribution. Taken together, the patterns form a modeling framework that provides significant insight into first-order expressive BNs and can reduce efforts in developing such models. To the best of our knowledge, there are no comprehensive published accounts of such patterns.

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, P2P networks, pervasive computing and the Smart Grid can be viewed as MAS. Dynamic agent behaviour in such MAS can arise from seasonal changes, cheaters, supply chain faults, network traffic and many other reasons. However, existing trust and reputation models use limited techniques, such as forgetting factors and sliding windows, to account for dynamic behaviour.

In this paper, we propose Reacting and Predicting in Trust and Reputation (RaPTaR), a method to extend existing trust and reputation models to give agents the ability to monitor the output of interactions with a group of agents over time to identify any likely changes in behaviour and adapt accordingly. Additionally, RaPTaR can provide an a priori estimate of trust when there is little or no interaction data (either because an agent is new or because a detected behaviour change suggests recent past experiences are no longer representative). Our results show that RaPTaR has improved performance compared to existing trust and reputation methods when dynamic behaviour causes the ranking of the best agents to interact with to change.

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 allows many blockchains to be operated in parallel, and allows atomic crosschain transactions to execute across blockchains. We found that Ethereum MainNet is best suited to storing long-term static data that need to be widely available, such as the Ethereum Registration Authority information. However, due to Ethereum MainNet’s probabilistic finality, it is not well suited to information that needs to be available and acted upon immediately, such as the Sidechain Public Keys and Atomic Crosschain Transaction state information that need to be accessible prior to the first atomic crosschain transaction being issued on a sidechain. Although this paper examined the use of Ethereum MainNet as a Coordination Blockchain within reference to Ethereum Private Sidechains, the discussions and observations of the typical tasks a Coordination Blockchain may be expected to perform are applicable more widely to any multi-blockchain system.

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 extensively in the context of agent systems as tools for promoting cooperation and improving social welfare in stable societies. In this paper, we evaluate how other factors like sympathy and parity can be used by agents to seek out cooperation possibilities while avoiding exploitation traps in more dynamic societies. We evaluate the relative effectiveness of agents influenced by different social considerations when they can change who they interact with in their environment using both an experimental framework and a predictive analysis. Such rewiring of social networks not only allows possibly vulnerable agents to avoid exploitation but also allows them to form gainful coalitions to leverage mutually beneficial cooperation, thereby significantly increasing social welfare.

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 approach naturally leads to two different optimization criteria for agents in a MOMAS: expected scalarized returns (ESRs) and scalarized expected returns (SERs). In this article, we explore the differences between these two criteria using the framework of multi-objective normal-form games (MONFGs). We demonstrate that the choice of optimization criterion (ESR or SER) can radically alter the set of equilibria in a MONFG when nonlinear utility functions are used.

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 is decisive in order to perform a fair evaluation of automatic linking methods and tools, we call this property of mappings as mapping fairness. In this article, we propose a crowd-based approach, called Crowd Quality (CQ), for assessing the quality of data linking datasets by measuring the fairness of the mappings in the reference alignment. Moreover, we present a real experiment, where we evaluate two state-of-the-art data linking tools before and after the refinement of the reference alignment based on the CQ approach, in order to present the benefits deriving from the crowd assessment of mapping fairness.

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 ontology matching has been dedicated to the development of complex matching approaches, there is still a lack of benchmarks on which the complex approaches can be systematically evaluated. The aim of this paper is to present the process of constructing the consensual complex Conference dataset, describing the design choices and the methodology followed for constructing it. We discuss the issues the experts were faced with during the process and discuss the lessons learned and perspectives in the field.

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 to notice that few studies address the integration between MAS and MRS. In order to achieve the interdisciplinary integration, the theoretical background of these areas must be considered in this paper, so that the integration can be applied using a case study of decentralized MRS. The objective of this MRS is to track and surround a stationary target. Also, it has been implemented and validated in the robot simulator called Virtual Robot Experimentation Platform (V-REP). In the validation of the proposed MRS, a scenario with three robots and a stationary target were defined. In the tracking task, the robot can detect the target whose position is not known a priori. When the detection occurs, the V-REP informs the target position to the robot because the environment is discretized into a grid of rectangular cells. After that, all the robots are directed to the target, and the surround task is realized. In this task, a mathematical model with direct communication between the robots was used to keep the robots equidistant therefrom and from each other.

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 the opponent and the certainty level of the beliefs that make up the argument. Our proposed model is based on the pre-conditions of credibility and preferability stated by Guerini and Castelfranchi. Thus, we suggest the use of two new criteria for the strength calculation: the credibility of the proponent and the status of the goal of the opponent in the goal processing cycle. We use three scenarios in order to illustrate our proposal. Besides, the model is empirically evaluated and the results demonstrate that the proposed model is more efficient than previous works of the state of the art in terms of numbers of negotiation cycles, number of exchanged arguments, and number of reached agreements.