XAI: Using Smart Photobooth for Explaining History of Art

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

Expliquer le comportement de robots distants à des utilisateurs humains : une approche orientée-agent

With the widespread use of Artificial Intelligence (AI) systems, understanding the behavior of intelligent agents and robots is crucial to guarantee smooth human-agent collaboration since it is not straightforward for humans to understand the agent’s state of mind. Recent studies in the goal-driven Explainable AI (XAI) domain have confirmed that explaining the agent’s behavior to humans fosters the latter’s understandability of the agent and increases its acceptability. However, providing overwhelming or unnecessary information may also confuse human users and cause misunderstandings. For these reasons, the parsimony of explanations has been outlined as one of the key features facilitating successful human-agent interaction with a parsimonious explanation defined as the simplest explanation that describes the situation adequately. While the parsimony of explanations is receiving growing attention in the literature, most of the works are carried out only conceptually.This thesis proposes, using a rigorous research methodology, a mechanism for parsimonious XAI that strikes a balance between simplicity and adequacy. In particular, it introduces a context-aware and adaptive process of explanation formulation and proposes a Human-Agent Explainability Architecture (HAExA) allowing to make this process operational for remote robots represented as Belief-Desire-Intention agents. To provide parsimonious explanations, HAExA relies first on generating normal and contrastive explanations and second on updating and filtering them before communicating them to the human.To evaluate the proposed architecture, we design and conduct empirical human-computer interaction studies employing agent-based simulation. The studies rely on well-established XAI metrics to estimate how understood and satisfactory the explanations provided by HAExA are. The results are properly analyzed and validated using parametric and non-parametric statistical testing.Avec l’émergence et la généralisation des systèmes d'intelligence artificielle, comprendre le comportement des agents artificiels, ou robots intelligents, devient essentiel pour garantir une collaboration fluide entre l'homme et ces agents. En effet, il n'est pas simple pour les humains de comprendre les processus qui ont amenés aux décisions des agents. De récentes études dans le domaine l’intelligence artificielle explicable, particulièrement sur les modèles utilisant des objectifs, ont confirmé qu'expliquer le comportement d’un agent à un humain favorise la compréhensibilité de l'agent par ce dernier et augmente son acceptabilité. Cependant, fournir des informations trop nombreuses ou inutiles peut également semer la confusion chez les utilisateurs humains et provoquer des malentendus. Pour ces raisons, la parcimonie des explications a été présentée comme l'une des principales caractéristiques facilitant une interaction réussie entre l’homme et l’agent. Une explication parcimonieuse est définie comme l'explication la plus simple et décrivant la situation de manière adéquate. Si la parcimonie des explications fait l'objet d'une attention croissante dans la littérature, la plupart des travaux ne sont réalisés que de manière conceptuelle.Dans le cadre d'une méthodologie de recherche rigoureuse, cette thèse propose un mécanisme permettant d’expliquer le comportement d’une intelligence artificielle de manière parcimonieuse afin de trouver un équilibre entre simplicité et adéquation. En particulier, il introduit un processus de formulation des explications, sensible au contexte et adaptatif, et propose une architecture permettant d’expliquer les comportements des agents à des humains (HAExA). Cette architecture permet de rendre ce processus opérationnel pour des robots distants représentés comme des agents utilisant une architecture de type Croyance-Désir-Intention.Pour fournir des explications parcimonieuses, HAExA s'appuie d'abord sur la génération d'explications normales et contrastées, et ensuite sur leur mise à jour et leur filtrage avant de les communiquer à l'humain. Nous validons nos propositions en concevant et menant des études empiriques d'interaction homme-machine utilisant la simulation orientée-agent. Nos études reposent sur des mesures bien établies pour estimer la compréhension et la satisfaction des explications fournies par HAExA. Les résultats sont analysés et validés à l'aide de tests statistiques paramétriques et non paramétriques

Human-Computer Interaction and Explainability: Intersection and Terminology

International audienceHuman-computer interaction (HCI) is generally considered the broader domain encompassing the study of the relationships between humans and types of technological artifacts or systems. Explainable AI (xAI) is involved in HCI to have humans better understand computers or AI systems which fosters, as a consequence, better interaction. The term “explainability” is sometimes used interchangeably with other closely related terms such as interpretability or understandability. The same can be said for the term “interaction”. It is a very broad way to describe the relationship between humans and technologies, which is why it is often replaced or completed by more precise terms like cooperation, collaboration, teaming, symbiosis, and integration. In the same vein, the technologies are represented by several terms like computer, machine, AI, agent, and robot. However, each of these terms (technologies and relationships) has its specificity and properties which need to be clearly defined. Currently, the definitions of these various terms are not well established in the literature, and their usage in various contexts is ambiguous. The goals of this paper are threefold: First, clarify the terminology in the HCI domain representing the technologies and their relationships with humans. A few concepts specific to xAI are also clarified. Second, highlight the role that xAI plays or can play in the HCI domain. Third, study the evolution and tendency of the usage of explainability and interpretability with the HCI terminology throughout the years and highlight the observations in the last three years

AQUAMan: QoE-driven cost-aware mechanism for SaaS acceptability rate adaptation

International audienceAs 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

One-to-Many Multi-agent Negotiation and Coordination Mechanisms to Manage User Satisfaction

International audienceQuality of Experience (QoE) is defined as the measure of end-user satisfaction with the service. Existing works addressing QoE-management rely on a binary vision of end-user satisfaction. This vision has been criticized by the growing empirical evidence showing that QoE is rather a degree. The aim of this article is to go beyond this binary vision and propose a QoE management mechanism. In particular, we propose a one-to-many negotiation mechanism allowing the provider to undertake satisfaction management : to meet fine-grained user QoE goals, while still minimizing the costs. This problem is formulated as an optimization problem, for which a linear model is proposed. For reference, a generic linear program solver is used to find the optimal solution, and an alternative heuristic algorithm is devised in order to improve the responsiveness, when the system has to scale up with fast growing number of users. Both are implemented and experimentally evaluated against state-of-the-art one-to-many negotiation frameworks

Négociation multi-agent « un-à-plusieurs » et mécanismes de coordination pour la gestion de la satisfaction des utilisateurs d'un service

International audienc

Agent-based model and service-oriented architecture for shifting from consumer to prosumer e-mobility behaviors in flex community

<p>The energy market is rapidly transforming and so is the role of the consumer. As new prosumers, energy markets can benefit<br> from their generation, consumption, and storage capabilities. The H2020 REDREAM project develops a strategy for the<br> creation of a value generation chain based on a revolutionary service-dominant logic in which services are exchanged.<br> Mobility service is one of the non-energy services of REDREAM. It enables to compute key indicators, such as energy consumption and CO2 emission, related to mobility behaviors for each prosumer, using different means of transport, including green ones. This paper presents the general architecture and the agent-based models that are included in the mobility service. A brief comparison of the mobility service to other mapping libraries is provided. Experiments with 400 real prosumers constitute the next step of this on-<br> going work.</p>https://hal.science/hal-0401565

AgentOil: A Multiagent-Based Simulation of the Drilling Process in Oilfields

International audienceOil&Gas have become the world’s most important source of energy since the mid-1950’s. For instance; Britain oilfields produce each year about 76 million tonnes of oil equivalent. This provides 76% of the UK’s total primary energy [5]. In oilfields wells, a drilling rig is used to create a bore-hole in the earth’s sub-surface with a Bottom Hole Assembly (BHA), which is a composition of several drilling tools with various functionalities, searching for natural resources

A tripartite evolutionary game analysis of providing subsidies for pick-up/drop-off strategy in carpooling problem

Abstract Although the pick-up/drop-off (PUDO) strategy in carpooling offers the convenience of short-distance walking for passengers during boarding and disembarking, there is a noticeable hesitancy among commuters to adopt this travel method, despite its numerous benefits. Here, this paper establishes a tripartite evolutionary game theory (EGT) model to verify the evolutionary stability of choosing the PUDO strategy of drivers and passengers and offering subsidies strategy of carpooling platforms in carpooling system. The model presented in this paper serves as a valuable tool for assessing the dissemination and implementation of PUDO strategy and offering subsidies strategy in carpooling applications. Subsequently, an empirical analysis is conducted to examine and compare the sensitivity of the parameters across various scenarios. The findings suggest that: firstly, providing subsidies to passengers and drivers, along with deductions for drivers through carpooling platforms, is an effective way to promote wider adoption of the PUDO strategy. Then, the decision-making process is divided into three stages: initial stage, middle stage, and mature stage. PUDO strategy progresses from initial rejection to widespread acceptance among drivers in the middle stage and, in the mature stage, both passengers and drivers tend to adopt it under carpooling platform subsidies; the factors influencing the costs of waiting and walking times, as well as the subsidies granted to passengers, are essential determinants that require careful consideration by passengers, drivers, and carpooling platforms when choosing the PUDO strategy. Our work provides valuable insight into the PUDO strategy’s applicability and the declared results provide implications for traffic managers and carpooling platforms to offer a suitable incentive

Towards a Real-time Mitigation of High Temperature while Drilling using a Multi-agent System

International audienceIn oilfield wells, while drilling for several kilometers below surface, high temperature damages the drilling tools. This costs money and time for tripping operations to change the damaged tool. Existing temperature mitigation techniques have several drawbacks including a long response time, analogue signal issues and human intervention. In this work, we empower the down-hole tools with a coordination mechanism to mitigate high temperature in soft real time by controlling a down-hole actuator through a voting process. The tools are represented by agents that control the sensors and actuators embedded in these tools. To implement the proposed system properly, a model of the drilling domain is constructed with all drilling mechanics and parameters, along with the well trajectory and temperature equations taken into consideration. The proposed model is implemented and tested using AgentOil, a multi-agent-based simulation tool, and the results are evaluated. Furthermore , the requirements of a real-time temperature mitigation system for Oil&Gas drilling operations are identified and the constraints of such systems are analyzed