Abstract Human interaction has changed considerably with the emergence of the Internet. Today, a large percentage of daily communication takes place on instant messaging applications and social networks. In fact, there has been a considerable increase in the use of social networks because new social networks are being created for specific purposes, such as the search for employment or tourism. When the amount of content on a social network is large, it is necessary to help users find content of their interest. In this regard, artificial intelligence techniques can greatly facilitate the task of searching for relevant content. This paper presents a recommender system for a business and employment oriented social network, on which users are recommended job offers and other user profiles to follow. The presented system is based on virtual organizations of agents, and uses artificial neural networks to determine whether job offers and users should be recommended or not. The system has been evaluated on a real social network; its recommendations regarding job offers and user profiles have had a high acceptance rate.

Decentralized autonomous organizations (DAOs) are relatively a newly emerging type of online entity related to governance or business models where all their members work together and participate in the decision-making processes affecting the DAO in a decentralized, collective, fair, and democratic manner. In a DAO, members interaction is mediated by software agents running on a blockchain that encode the governance of the specific entity in terms of rules that optimize their business and goals. In this context, most popular DAO software frameworks provide decision-making models aiming to facilitate digital governance and the collaboration among their members intertwining social and economic concerns. However, these models are complex, not interoperable among them and lack a common understanding and shared knowledge concerning DAOs, as well as the computational semantics needed to enable automated validation, simulation or execution. Thus, this paper presents an ontology (Web3-DAO), which can support machine-readable digital governance of DAOs adding semantics to their decision-making models. The proposed ontology captures the domain logic that allows the sharing of updated information and decisions for all the members that interact with a DAO by the interoperability of their own assessment and decision tools. Furthermore, the ontology detects semantic ambiguities, uncertainties and contradictions. The Web3-DAO ontology is available in open access at https://github.com/Grasia/semantic-web3-dao.

Population growth implies the need to produce more food. This increases pressure on the planet's resources, which drives climate change and challenges environmental sustainability. Hence, it is important to promote sustainable agriculture strategies that contribute to global food and nutrition security while protecting natural resources. These strategies should be guided from an educational perspective that motivates people to develop positive bonds with nature. In this regard, augmented reality has emerged as a technology with the potential to improve environmental education programs. This paper presents the development and evaluation of an augmented reality-based educational application, whose purpose is to foster sustainable agriculture in the context of aquaponics. The application was developed using Unity and Vuforia following the principles of instructional design. To evaluate the effectiveness of the application, we designed a pilot study with 10 volunteers. The results indicate that the application has the potential to motivate users to learn, which suggests that it is appropriate to foster sustainable agriculture strategies in the context of aquaponics.

Ethics by Design concerns the methods, algorithms and tools needed to endow autonomous agents with the capability to reason about the ethical aspects of their decisions, and the methods, tools and formalisms to guarantee that an agent's behavior remains within given moral bounds. In this context some questions arise: How and to what extent can agents understand the social reality in which they operate, and the other intelligences (AI, animals and humans) with which they co-exist? What are the ethical concerns in the emerging new forms of society, and how do we ensure the human dimension is upheld in interactions and decisions by autonomous agents?. But overall, the central question is: "Can we, and should we, build ethically-aware agents?" This paper presents initial conclusions from the thematic day of the same name held at PRIMA2017, on October 2017.

This paper presents a flexible agent decision model for the simulation of indoor scenarios. There are different kind of applications with varying requirements, from the typical emergency evacuation where the physical interaction of crowds of agents are more relevant, to those that demand more sophisticated agent decision models as when testing smart environment applications. Existing tools usually focus on one of these issues, looking for efficiency in the solutions. The agent decision model in this paper tries to get a balance between efficiency and flexibility in the specification of the agent behavior in simple and complex situations. This is applied in a simulation framework for indoor scenarios, although it could be extended to other settings.

In some areas of software engineering research, there are several metamodels claiming to capture the main issues. Though it is profitable to have variety at the beginning of a research field, after some time, the diversity of metamodels becomes an obstacle, for instance to the sharing of results between research groups. To reach consensus and unification of existing metamodels, metamodel-driven software language engineering can be applied. This paper illustrates an application of software language engineering in the agent-oriented software engineering research domain. Here, we introduce a relatively generic agent-oriented metamodel whose suitability for supporting modeling language development is demonstrated by evaluating it with respect to several existing methodology-specific metamodels. First, the metamodel is constructed by a combination of bottom-up and top-down analysis and best practice. The concepts thus obtained and their relationships are then evaluated by mapping to two agent-oriented metamodels: TAO and Islander. We then refine the metamodel by extending the comparisons with the metamodels implicit or explicit within five more extant agent-oriented approaches: Adelfe, PASSI, Gaia, INGENIAS, and Tropos. The resultant FAML metamodel is a potential candidate for future standardization as an important component for engineering an agent modeling language.

Requirements elicitation and analysis is intended to gain knowledge about customers' needs and the environment of a software system. Requirements not only commonly deal with business processes and their data but also with the motivation behind these activities, the social structures that forge them, and previous design decisions. Recent studies show that the intentional and social concepts of agent-oriented software engineering (AOSE) could be used for the analysis of these aspects. Nevertheless, even having specialized modeling primitives for these requirements is not sufficient for their elicitation. Specialized analysis techniques are also required, but this is commonly overlooked by agent-oriented methodologies. This paper aims to provide the needed modeling primitives and support by means of the theoretical and methodological foundation of a social sciences framework, the activity theory, and its activity checklist. They inspire our requirements elicitation guide (REG) for AOSE. The REG contains the expert knowledge that developers need to grasp information about their multiagent systems, human environments, and their mutual influences. This knowledge takes the form of requirements described as diagrams in a proper modeling language. The REG is applied in a process with the corresponding support tool. In this way, the REG guides requirements elicitation and increases the productivity with the use of templates for a wide range of requirements. These elements have been validated with several case studies. Two of them appear as part of this paper.