The role of intelligent agents in research on AI is becoming increasingly significant. The study of embodied intelligent systems, such as social and companion robots, is growing steadily in proportion to the previsions that they will become increasingly involved in the everyday lives of individuals. Non-embodied intelligent systems, such as chatbots or virtual voice assistants, are already part of many people’s everyday experience, being constantly at their disposal. This necessitates a re-evaluation of society as we know it, which must be conceived as a ‘hybrid society’. The objective of this paper is to demonstrate how, in such a scenario, a truly human-centred development of intelligent agents necessitates a design that does not capitalise or exploit the natural inclination towards human attachment and empathy, but rather incorporates this into a more responsible framework for human-agent interaction (henceforth HAI). In order to achieve this objective, the topic of human dependency is presented, analysed, and interpreted in the light of one of its main theorisation, formulated in the socio-legal domain. The merit of such a theoretical framework is that it is flexible enough to be applied to all those elements that, once introduced into society, can affect it and alter its equilibrium. This is undoubtedly the case with the artificial agents designated for HAI. Building on this, the paper demonstrates how such an approach could be employed to analyse the dynamics of HAI in a truly human-centred manner, thereby providing a foundation for the future design of artificial agents and the dynamics of interaction with end-users.

Traditional helpdesk ticketing systems (TS) often fail to manage huge amounts of support tickets efficiently, resulting in longer response times and lower user satisfaction. This paper proposes a new TS approach by integrating a Discord-based community with a TS and chatbot. The suggested system takes advantage of the open-source features provided by the Discord platform to create and handle support requests from within Discord channels, taking advantage of the platform’s real-time communication features and the support of a large community. The system’s initial review shows that 88% of the users are delighted through the employment of Discord, providing scalable solutions that can be applied to various community-driven support situations [9].

The awareness about healthy lifestyles is increasing, opening to personalized intelligent health coaching applications. A demand for more than mere suggestions and mechanistic interactions has driven attention to nutrition virtual coaching systems (NVC) as a bridge between human-machine interaction and recommender, informative, persuasive, and argumentation systems. NVC can rely on data-driven opaque mechanisms. Therefore, it is crucial to enable NVC to explain their doing (i.e., engaging the user in discussions (via arguments) about dietary solutions/alternatives). By doing so, transparency, user acceptance, and engagement are expected to be boosted. This study focuses on NVC agents generating personalized food recommendations based on user-specific factors such as allergies, eating habits, lifestyles, and ingredient preferences. In particular, we propose a user-agent negotiation process entailing run-time feedback mechanisms to react to both recommendations and related explanations. Lastly, the study presents the findings obtained by the experiments conducted with multi-background participants to evaluate the acceptability and effectiveness of the proposed system. The results indicate that most participants value the opportunity to provide feedback and receive explanations for recommendations. Additionally, the users are fond of receiving information tailored to their needs. Furthermore, our interactive recommendation system performed better than the corresponding traditional recommendation system in terms of effectiveness regarding the number of agreements and rounds.

The literature on deception in human-robot interaction (henceforth HRI) could be divided between: (i) those who consider it essential to maximise users' end utility and robotic performance; (ii) those who consider it unethical, because it is potentially dangerous for individuals' psychological integrity. However, it has now been proven that humans are naturally prone to anthropomorphism and emotional attachment to inanimate objects. Consequently, despite ethical concerns, the argument for the total elimination of deception could reveal to be a pointless exercise. Rather, it is suggested here to conceive deception in HRI as a dynamic to be modulated and graded, in order to both promote innovation and protect fundamental human rights. To this end, the concept of vulnerability could serve as an objective balancing criterion.

Agent-based modeling and simulation can provide a powerful test environment for crisis management scenarios. Human agent interaction has limitations in representing norms issued by an agent to a human agent that has emotions. In this study, we present an approach to the interaction between a virtual normative agent and a human agent in an evacuation scenario. Through simulation comparisons, it is shown that the method used in this study can more fully simulate the real-life out come of an emergency situation and also improves the au thenticity of the agent interaction.

The paper presents Pedalcoin, a decentralized application that incentivizes sustainable transportation and promotes cycling through a blockchain-based reward system. It investigates how Pedalcoin leverages concepts from blockchain, multi-agent systems, and reinforcement learning to drive large-scale sustainable behavior changes through gamified incentives and decentralized optimization of agent policies towards greater rewards. The dynamics of this system lead to increased cycling and reduced automobile usage organically through positive reinforcement, benefiting the environment.

Studies on the phenomenon of anthropomorphism underpin the strategies by which social robots are designed. This allows for greater acceptance and trust in the machine, but may lead to manipulative drifts and to the distortion of the perception of reality. This paper argues that anthropomorphism cannot be fully controlled and surely not eliminated, because it represents a characteristic and irreducible feature of humanity. In this sense, it represents the materialisation in the context of human robot interaction (henceforth HRI) of the more general concept of human vulnerability. Nevertheless, it is affirmed the necessity to evaluate how and to what extent the design features implemented in different robots may elicit, target, support such a phenomenon (and thus human vulnerability), and how they can be handled for the protection of the fundamental rights of individuals, first of all dignity and psychological integrity. To this end, a Vulnerability-oriented Impact Assessment is suggested.

Primary data collection is key to achieve successful humanitarian governance in conflict-zones. Recent years witness a surge in works undertaking such surveys of data collection both of fieldwork and academia. Despite the proliferation of online and cloud-based survey services, several challenges should be addressed in order to unlock the full potential of primary data collection in conflict zones and in the global south. This paper presents BlueData, an AI-assisted Primary data collection system. BlueData provides high-quality data collection for monitoring and evaluation for humanitarian and non-humanitarian projects. The paper presents the architecture of the BlueData system, discuss its merits, outlines its limitations and identify future research perspectives.

Food recommender systems (FRS) provides suggestions of recipes to human users. These systems are more and more spreading like in EU and US. However in sub-Saharan Africa, the application of these systems is less explored. This paper aims to shed light on the challenges faced by food recommender systems when applied in Sub-Saharan Africa. By identifying these limitations, we can pave the way for more inclusive and culturally sensitive human-computer interaction designs.

Explainable AI (XAI) has emerged in recent years as a set of techniques to build systems that enable humans to understand the outcomes produced by artificial intelligent entities. Although these initiatives have advanced over the past few years, most approaches focus on explanations that are meant for literate or even skilled end users such as engineers, researchers etc. Few works available in the literature address the needs of illiterate end-users in XAI (illiterate centered design). This paper proposes a generic model to extract the contents of explanations from a given explainable AI system, and translate them into a representation format that illiterate end users may understand. The usefulness of the model is shown by reference to an application of a food recommender system.

As automated decision-making systems proliferate, accountability becomes crucial. Developers must ensure adherence to regulations and fairness. Explainable AI offers a remedy by crafting algorithms that provide precise outcomes and understandable explanations. This paper focuses on food recommender system interpretability for better health. Integrating explainable AI empowers users to make informed dietary decisions. The proposed framework generates natural language explanations for recommendations using the prompting technique, demonstrating superior performance and broad applicability across domains.

In the context of human-social robot interaction, it has been proven that an affable design and the ability to exhibit emotional and social skills are central to fostering acceptance and more efficient system performance. Nevertheless, these features may result in manipulative dynamics, able to impact the psychological sphere of the users, affecting their ability to make decisions and to exercise free, conscious will. This highlights the need to identify a legal framework that balances the interests at stake. To this end, the principle of human dignity is proposed here as a criterion to ensure (i) the protection of users’ fundamental rights, and (ii) an effective and truly human-friendly technological development.

The rise of Artificial Intelligence has led to advancements in daily life, including applications in industries, telemedicine, farming, and smart cities. It is necessary to have human-AI synergies to guarantee user engagement and provide interactive expert knowledge, despite AI’s success in "less technical" fields. In this article, the possible synergies between humans and AI to explain the development of art history and artistic style transfer are discussed. This study is part of the "Smart Photobooth" project that is able to automatically transform a user’s picture into a well-known artistic style as an interactive approach to introduce the fundamentals of the history of art to the common people and provide them with a concise explanation of the various art painting styles. This study investigates human-AI synergies by combining the explanation produced by an explainable AI mechanism with a human expert’s insights to provide reasons for school students and a larger audience.

Traffic congestion remains a major issue in the majority of developing countries. Intersections, in particular, are one of the major bottlenecks in road networks, exacerbating congestion. In these countries, policemen are regularly used to control traffic at intersections due to the social behaviors of drivers. However, policemen experience a lot of stress from long working hours and have the risk of accidents. Therefore, effective control of traffic at intersections taking into account the social behavior of drivers is an important strategy for improving traffic flow. To address this, in this paper to control the traffic at the intersection of a road network, a robot model for traffic signal management system using a web-based traffic simulator is presented.

The advent of Distributed Ledger Technologies (DLTs) has paved the way for a new paradigm of traceability in all information systems areas. In the context of decision-making processes, however, DLTs are generally used only to trace the end results. In this work we argue that a reasoning system can be put in place for making these decisions, in order to enhance auditability, transparency, and finally to provide explainability. We propose the Intelligent Human-input-based Blockchain Oracle (IHiBO), a cross-chain oracle that enables the execution and traceability of formal argumentation and negotiation processes, involving the intervention of human experts. We take as reference the decision-making processes of fund managements, as trust is of crucial importance in such ``trust services''. The architecture and implementation of IHiBO are based on leveraging two-layer DLTs, smart contracts, argumentation and negotiation in a multi-agent setup. Finally, we provide some experimental results that support our discussion, namely that in the use-case we have considered our methodology can increase trust from principals to trusted services.

Abstract Since its dawn as a discipline, Artificial Intelligence (AI) has focused on mimicking the human mental processes. As AI applications matured, the interest for employing them into real-world complex systems (i.e., coupling AI with Cyber-Physical Systems—CPS) kept increasing. In the last decades, the multi-agent systems (MAS) paradigm has been among the most relevant approaches fostering the development of intelligent systems. In numerous scenarios, MAS boosted distributed autonomous reasoning and behaviors. However, many real-world applications (e.g., CPS) demand the respect of strict timing constraints. Unfortunately, current AI/MAS theories and applications onlyreason“about time” and are incapable ofacting“in time” guaranteeing any timing predictability. This paper analyzes the MAS compliance with strict timing constraints (real-time compliance)—crucial for safety-critical applications such as healthcare, industry 4.0, and automotive. Moreover, it elicits the main reasons for the lack of real-time satisfiability in MAS (originated from current theories, standards, and implementations). In particular, traditional internal agent schedulers (general-purpose-like), communication middlewares, and negotiation protocols have been identified as co-factors inhibiting real-time compliance. To pave the road towards reliable and predictable MAS, this paper postulates a formal definition and mathematical model of real-time multi-agent systems (RT-MAS). Furthermore, this paper presents the results obtained by testing the dynamics characterizing the RT-MAS model within the simulator MAXIM-GPRT. Thus, it has been possible to analyze the deadline miss ratio between the algorithms employed in the most popular frameworks and the proposed ones. Finally, discussing the obtained results, the ongoing and future steps are outlined.

The recent unprecedented popularity of food recommender applications has raised several issues related to the ethical, societal and legal implications of relying on these applications. In this paper, in order to assess the relevant ethical issues, we rely on the emerging principles across the AI & Ethics community and define them tailored context specifically. Considering the popular Food Recommender Systems (henceforth F-RS) in the European market cannot be regarded as personalised F-RS, we show how merely this lack of feature shifts the relevance of the focal ethical concerns. We identify the major challenges and propose a scheme for how explicit ethical agendas should be explained. We also argue how a multi-stakeholder approach is indispensable to ensure producing long-term benefits for all stakeholders. After proposing eight ethical desiderata points for F-RS, we present a case-study and assess it based on our proposed desiderata points.

International audience

Asynchronous messaging is leading human-machine interaction due to the boom of mobile devices and social networks. The recent release of dedicated APIs from messaging platforms boosted the development of computer programs able to conduct conversations, (i.e., chatbots), which have been adopted in several domain-specific contexts. This paper proposes SMAG: a chatbot framework supporting a smoking cessation program (JDF) deployed on a social network. In particular, it details the single-agent implementation, the campaign results, a multi-agent design for SMAG enabling the modelization of personalized behavior and user profiling, and highlighting of coupling chatbot technology with and multi-agent systems.

Recently, the civilian applications of Unmanned Aerial Vehicles (UAVs) are gaining more interest in several domains. Due to operational costs, safety concerns, and legal regulations, Agent-Based Simulation (ABS) is commonly used to design models and conduct tests. This has resulted in numerous research works addressing ABS in civilian UAV applications. This paper aims to provide a comprehensive overview of the ABS contribution in civilian UAV applications by conducting a Systematic Literature Review (SLR) on the relevant research in the previous ten years. Following the SLR methodology, this objective is broken down into several research questions aiming to (i) understand the evolution of ABS use in civilian UAV applications and identify the related hot research topics, (ii) identify the underlying artificial intelligence systems used in the literature, (iii) understand how and when ABS is integrated in broader and more complex internet of things & ubiquitous computing environments, and (iv) identity the communication technologies, tools, and evaluation techniques used to design, implement, and test the proposed ABS models. From the SLR results, key research directions are highlighted including problems related to autonomy, explainability, security, flight duration, integration within smart cities, regulations, and validation & verification of the UAV behavior.

Executing business processes in a decentralized manner can improve inter-organizational efficacy. For example, blockchain-based process execution allows, at least conceptually, for cross-organizational compatibility, data integration, and integrity assurance without the need for a centralized trusted operator. However, most business processes run in agile and rapidly changing business environments. Updating a decentralized process requires continuous and extensive consensus-building efforts. Reflecting all organizations' business requirements is hardly practicable. Hence, in many real-life scenarios, to support cases with initially unforeseen properties, organizations can allow to bypass the decentralized process and fall-back to local variants. Yet, the decision to bypass or update a given process can have significant social implications since it may encourage a social dynamic that encourages collective avoidance of the decentralized process. This paper proposes a multi-agent simulation system to assess the social consequences of approving a bypass under given conditions. The proposed simulation is intended to inform the decision-maker (human or machine) on whether to allow to bypass a process or not. Moreover, we present an architecture for the integration of multi-agent simulation system, local process engine, and decentralized process execution environment, and describe a possible implementation with a particular tool chain.

As more interactive and multimedia-rich applications are migrating to the cloud, end-user satisfaction and her Quality of Experience (QoE) will become a determinant factor to secure success for any Software as a Service (SaaS) provider. Yet, in order to survive in this competitive market, SaaS providers also need to maximize their Quality of Business (QoBiz) and minimize costs paid to cloud providers. However, most of the existing works in the literature adopt a provider-centric approach where the end-user preferences are overlooked. In this article, we propose the AQUAMan mechanism that gives the provider a fine-grained QoE-driven control over the service acceptability rate while taking into account both end-users' satisfaction and provider's QoBiz. The proposed solution is implemented using a multi-agent simulation environment. The results show that the SaaS provider is capable of attaining the predefined acceptability rate while respecting the imposed average cost per user. Furthermore, the results help the SaaS provider identify the limits of the adaptation mechanism and estimate the best average cost to be invested per user.

As more personal and interactive applications are moving to the cloud, modeling the end-user expectations and satisfaction is becoming necessary for any SaaS provider to survive and thrive in today's competitive market. However, most of existing works addressing cloud elasticity management adopt a centralized approach where user preferences are mostly overlooked. Based on evidence from the fields of customer expectation management and psychophysics, in this article we propose a personal user model to represent end-user satisfaction and her expectations. To integrate the end-user into the decision loop we develop multi-agent negotiation architecture in which the end-user model is embodied by a personal agent who negotiates on her behalf. The results of the evaluation process show that automated negotiation provides a useful platform to empower the user choices, fulfill her expectations, and maximize her satisfaction hereby outperforming centralized approaches where the provider acts in a unilateral manner.