Blockchain has become a pervasive technology in a wide number of sectors like industry, research, and academy. In the last decade a large number of tailored-domain problems have been solved thanks to the blockchain. Due to this reason, researchers expressed their interest in combining the blockchain with other well-known technologies, like Semantic Web. Unfortunately, as far as we known, in the literature no one has presented the different scenarios in which Semantic Web and blockchain can be combined, and the further benefits for both. In this paper, we aim a providing an in-depth view of the beneficial symbiotic relation that these technologies may reach together and report the different scenarios that we have identified in the literature to combine Semantic Web and blockchain.

Blockchain has become a pervasive technology in a wide number of sectors like industry, research, and academy. With the emergence of blockchain, new solutions with this technology to existing problems were devised, leading to the introduction of smart contracts. Smart contracts are similar to traditional contracts with the benefits provided by blockchain, such as immutability, privacy, and decentralisation. These contracts are usually defined based on a specific domain, and this domain knowledge can be represented through an ontology. Researches have explored the benefits of using domain ontologies with smart contracts, such as code generation, discovering other contracts in the network, or interaction with other contracts. Notwithstanding, the representation of smart contract languages themselves has not been studied. In this paper, we present an ontology for a well-known smart contract language, Solidity, defining all entities needed to cover the whole language and aligning it to other standardised ontologies such as EthOn, in a way to improve the knowledge of the ontology developed. Furthermore, the ontology has also been validated with already deployed contracts in the Ethereum blockchain. Thus, Solidity will be able to benefit from the advantages provided by ontologies, such as interoperability and the use of semantic web technologies.

Context : Games are a well-established scenario to test AI and Multi-Agent Systems (MAS) proposals due to their popularity and defiance. However, there is no big picture of the application of this technology to games, the evolution of the kind of problem tackled, or the game scenarios in which agents have been experimented. Objective : To perform a systematic mapping to characterise the state of the art in the field of MAS applied to virtual games and to identify trends, strengths, and gaps for further research. Method: A Systematic Mapping Study has been conducted to find primary studies in the field. A search was performed on title, abstracts, and keywords, whilst classification, data extraction, and further analysis were performed according to specific criteria focused on MAS papers with experimentation and evidence in a game scenario. Results : 78 studies published between 1998 and 2021 were found. Studies have been classified according to the MAS problem faced and the agent reasoning strategy. We detect that Machine Learning is the most common AI technique for MAS in games, considering both reinforcement learning and evolutionary techniques. MAS are used in a variety of gaming genres, especially in Real-Time Strategy (RTS), Sports and Simulation. Conclusions : RTS and Sports games are well-suited for concrete MAS problems such as multi-agent planning and task allocation. Expanding evidence and experimentation on other aspects related to scalability and usability issues is discussed. Those MAS problems and experiments that remain slightly modelled on games or are not thoroughly studied yet have been also identified.

The privacy of the data provided by available sources is one of the major concerns of our era. In order to address this challenge, the W3C has promoted recommendations to allow expressing privacy policies. One of these recommendations is the Open Digital Rights Language (ODRL) vocabulary. Although this standard has wide adoption, it is not suitable in domains such as IoT, Ubiquitous and Mobile Computing, or discovery. The reason behind is the fact that ODRL privacy policies are not able to cope with dynamic information that may come from external sources of data and, therefore, these policies can not define privacy restrictions upon data that is not already written in the policy beforehand. In this demo paper, a solution to this challenge is presented. It is shown how ODRL policies can overcome the aforementioned limitation by being combined with a mapping language for RDF materialisation. The article shows how ODRL policies are able to consider data coming from an external data source when they are solved, in particular, a weather forecast API that provides temperature values. The demonstration defines an ODRL policy that grants access to a resource only when the temperature of the API is above a certain value.