Dialogue Games in Defeasible Logic

In this paper we show how to capture dialogue games in Defeasible Logic. We argue that Defeasible Logic is a natural candidate and general representation formalism to capture dialogue games even with requirements more complex than existing formalisms for this kind of games. We parse the dialogue into defeasible rules with time of the dialogue as time of the rule. As the dialogue evolves we allow an agent to upgrade the strength of unchallenged rules. The proof procedures of (Antoniou, Billington, Governatori, Maher 2001) are used to determine the winner of a dialogue game

Design of an open-source laboratory demonstrator for peer-to-peer trading in local energy markets

There is significant research interest in new energy trading mechanisms based on peer-to-peer or community-based markets, which are more consumer-centric and direct compared to traditional electricity retail markets. Such trading mechanisms require an electricity trading platform that can manage and settle energy transactions from vast numbers of small distributed energy resources, and therefore scalability and interoperability are major challenges. The hardware, communications and software required for implementing local energy trading platforms needs to be designed, tested and demonstrated in a real-time environment. Accordingly, this paper presents a design for an open-source laboratory demonstrator, which allows testing of the hardware and software required for peer-to-peer local energy trading using distributed ledger technology

Distributed double auction for peer to peer energy trade using blockchains

In this paper we use the blockchain technology to develop a peer to peer energy trade platform without a trusted third party. Our main contribution is a novel distributed double auction mechanism which allows any peer to act as an auctioneer and the blockchain mechanism ensures that a peer behaves lawfully while acting as an auctioneer. Using experimental evaluation we show that (1) the distributed auction converges quickly, (2) it minimizes energy loss due to long transmission, (3) computational overhead due to employing a blockchain is negligible, (4) it is efficient and (5) it can implement trade restrictions imposed by the energy distribution network

Cooperative game theory based peer to peer energy trading algorithm

The energy sector is undergoing a paradigm shift to integrate the increasing volume of embedded renewable energy generation and creating local energy communities or LECs that have been an essential component in increasing the same. Peer to Peer (P2P) energy trading is one of the alternatives to curb the surplus energy flow and would also help in maintaining a dynamic balance between supply and demand in the power grid. In this paper, we propose a P2P energy trading mechanism with distributed solar photovoltaic, community battery storage, and electric vehicle charging points. Game theory is the most widely used approach for P2P energy trading because of its characteristic of solving complicated interactions between provider and receiver. In the present work, we have considered a coalition based cooperative game theory framework whose objective is to maximize the total profit of the coalition. The simulation framework of this mechanism has been tested on a local energy community with 100 households having 50 consumers and 50 prosumers creating a win-win approach for both consumers and prosumers (users able to generate and consume simultaneously). Various trading scenarios have been proposed in this paper depending on geographical location, maximum energy demand, and maximum energy generated. These trading scenarios have been tested on a low voltage model to check their feasibility for a real network. The best operational performance priority at each timeslot with solar PV and community storage has also been analysed

Block Chain and Internet of Nano-Things for Optimizing Chemical Sensing in Smart Farming

preprintThe use of Internet of Things (IoT) with the Internet of Nano Things (IoNT) can further expand decision making systems (DMS) to improve reliability as it provides a new spectrum of more granular level data to make decisions. However, growing concerns such as data security, transparency and processing capability challenge their use in real-world applications. DMS integrated with Block Chain (BC) technology can contribute immensely to overcome such challenges. The use of IoNT and IoT along with BC for making DMS has not yet been investigated. This study proposes a BC-powered IoNT (BC-IoNT) system for sensing chemicals level in the context of farm management. This is a critical application for smart farming, which aims to improve sustainable farm practices through controlled delivery of chemicals. BC-IoNT system includes a novel machine learning model formed by using the Langmuir molecular binding model and the Bayesian theory, and is used as a smart contract for sensing the level of the chemicals. A credit model is used to quantify the traceability and credibility of farms to determine if they are compliant with the chemical standards. The accuracy of detecting the chemicals of the distributed BC-IoNT approach was ≥ 90% and the centralized approach was ≤ 80%. Also, the efficiency of sensing the level of chemicals depends on the sampling frequency and variability in chemical level among farms

A Heuristic Charging Cost Optimization Algorithm for Residential Charging of Electric Vehicles

The charging loads of electric vehicles (EVs) at residential premises are controlled through a tariff system based on fixed timing. The conventional tariff system presents the herding issue, such as with many connected EVs, all of them are directed to charge during the same off-peak period, which results in overloading the power grid and high charging costs. Besides, the random nature of EV users restricts them from following fixed charging times. Consequently, the real-time pricing scenarios are natural and can support optimizing the charging load and cost for EV users. This paper aims to develop charging cost optimization algorithm (CCOA) for residential charging of EVs. The proposed CCOA coordinates the charging of EVs by heuristically learning the real-time price pattern and the EV’s information, such as the battery size, current state-of-charge, and arrival departure times. In contrast to the holistic price, the CCOA determines a threshold price value for each arrival and departure sequence of EVs and accordingly coordinates the charging process with optimizing the cost at each scheduling period. The charging cost is captured at the end of each charging activity and the cumulative cost is calculated until the battery’s desired capacity. Various charging scenarios for individual and aggregated EVs with random arrival sequences of EVs against the real-time price pattern are simulated through MATLAB. The simulation results show that the proposed algorithm outperforms with a low charging cost while avoiding the overloading of the grid compared to the conventional uncoordinated, flat-rate, and time-of-use systems

Charting Past, Present, and Future Research in the Semantic Web and Interoperability

Huge advances in peer-to-peer systems and attempts to develop the semantic web have revealed a critical issue in information systems across multiple domains: the absence of semantic interoperability. Today, businesses operating in a digital environment require increased supply-chain automation, interoperability, and data governance. While research on the semantic web and interoperability has recently received much attention, a dearth of studies investigates the relationship between these two concepts in depth. To address this knowledge gap, the objective of this study is to conduct a review and bibliometric analysis of 3511 Scopus-registered papers on the semantic web and interoperability published over the past two decades. In addition, the publications were analyzed using a variety of bibliometric indicators, such as publication year, journal, authors, countries, and institutions. Keyword co-occurrence and co-citation networks were utilized to identify the primary research hotspots and group the relevant literature. The findings of the review and bibliometric analysis indicate the dominance of conference papers as a means of disseminating knowledge and the substantial contribution of developed nations to the semantic web field. In addition, the keyword co-occurrence network analysis reveals a significant emphasis on semantic web languages, sensors and computing, graphs and models, and linking and integration techniques. Based on the co-citation clustering, the Internet of Things, semantic web services, ontology mapping, building information modeling, bioinformatics, education and e-learning, and semantic web languages were identified as the primary themes contributing to the flow of knowledge and the growth of the semantic web and interoperability field. Overall, this review substantially contributes to the literature and increases scholars’ and practitioners’ awareness of the current knowledge composition and future research directions of the semantic web field. View Full-Tex

Production of He-4 and (4) in Pb-Pb collisions at root(NN)-N-S=2.76 TeV at the LHC

Results on the production of He-4 and (4) nuclei in Pb-Pb collisions at root(NN)-N-S = 2.76 TeV in the rapidity range vertical bar y vertical bar <1, using the ALICE detector, are presented in this paper. The rapidity densities corresponding to 0-10% central events are found to be dN/dy4(He) = (0.8 +/- 0.4 (stat) +/- 0.3 (syst)) x 10(-6) and dN/dy4 = (1.1 +/- 0.4 (stat) +/- 0.2 (syst)) x 10(-6), respectively. This is in agreement with the statistical thermal model expectation assuming the same chemical freeze-out temperature (T-chem = 156 MeV) as for light hadrons. The measured ratio of (4)/He-4 is 1.4 +/- 0.8 (stat) +/- 0.5 (syst). (C) 2018 Published by Elsevier B.V.Peer reviewe