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

Modèle dynamique multiniveau et holonique pour la simulation d'un système complexe à grande échelle avec environnement spatial : Application à la simulation du trafic routier

De nos jours, avec l’émergence d’objets et de voitures connectés, les systèmes de trafic routier deviennent de plus en plus complexes et présentent des comportements hiérarchiques à plusieurs niveaux de détail. L'approche de modélisation multiniveaux est une approche appropriée pour représenter le trafic sous plusieurs perspectives. Les modèles multiniveaux constituent également une approche appropriée pour modéliser des systèmes complexes à grande échelle comme le trafic routier. Cependant, la plupart des modèles multiniveaux de trafic proposés dans la littérature sont statiques car ils utilisent un ensemble de niveaux de détail prédéfinis et ces représentations ne peuvent pas commuter pendant la simulation. De plus ces modèles multiniveaux considèrent généralement seulement deux niveaux de détail. Très peu de travaux se sont intéressés à la modélisation dynamique multiniveau de trafic.Cette thèse propose un modèle holonique multiniveau et dynamique du trafic à grande échelle.La commutation dynamique des niveaux de détail lors de l’exécution de la simulation permet d’adapter le modèle aux contraintes liées à la qualité des résultats ou aux ressources de calcul disponibles.La proposition étend l'algorithme DBSCAN dans le contexte des systèmes multi-agents holoniques. De plus, une méthodologie permettant la commutation dynamique entre les différents niveaux de détail est proposée. Des indicateurs multiniveaux basés sur l'écart type sont aussi proposés afin d'évaluer la cohérence des résultats de la simulation.Nowadays, with the emergence of connected objects and cars, road traffic systems become more and more complex and exhibit hierarchical behaviours at several levels of detail. The multilevel modeling approach is an appropriate approach to represent traffic from several perspectives. Multilevel models are also an appropriate approach to model large-scale complex systems such as road traffic. However, most of the multilevel models of traffic proposed in the literature are static because they use a set of predefined levels of detail and these representations cannot change during simulation. Moreover, these multilevel models generally consider only two levels of detail. Few works have been interested on the dynamic multilevel traffic modeling.This thesis proposes a holonic multilevel and dynamic traffic model for large scale traffic systems. The dynamic switching of the levels of detail during the execution of the simulation allows to adapt the model to the constraints related to the quality of the results or to the available computing resources.The proposal extends the DBSCAN algorithm in the context of holonic multi-agent systems. In addition, a methodology allowing a dynamic transition between the different levels of detail is proposed. Multilevel indicators based on standard deviation are also proposed in order to assess the consistency of the simulation results

Modèle dynamique multiniveau et holonique pour la simulation d'un système complexe à grande échelle avec environnement spatial : Application à la simulation du trafic routier

Nowadays, with the emergence of connected objects and cars, road traffic systems become more and more complex and exhibit hierarchical behaviours at several levels of detail. The multilevel modeling approach is an appropriate approach to represent traffic from several perspectives. Multilevel models are also an appropriate approach to model large-scale complex systems such as road traffic. However, most of the multilevel models of traffic proposed in the literature are static because they use a set of predefined levels of detail and these representations cannot change during simulation. Moreover, these multilevel models generally consider only two levels of detail. Few works have been interested on the dynamic multilevel traffic modeling.This thesis proposes a holonic multilevel and dynamic traffic model for large scale traffic systems. The dynamic switching of the levels of detail during the execution of the simulation allows to adapt the model to the constraints related to the quality of the results or to the available computing resources.The proposal extends the DBSCAN algorithm in the context of holonic multi-agent systems. In addition, a methodology allowing a dynamic transition between the different levels of detail is proposed. Multilevel indicators based on standard deviation are also proposed in order to assess the consistency of the simulation results.De nos jours, avec l’émergence d’objets et de voitures connectés, les systèmes de trafic routier deviennent de plus en plus complexes et présentent des comportements hiérarchiques à plusieurs niveaux de détail. L'approche de modélisation multiniveaux est une approche appropriée pour représenter le trafic sous plusieurs perspectives. Les modèles multiniveaux constituent également une approche appropriée pour modéliser des systèmes complexes à grande échelle comme le trafic routier. Cependant, la plupart des modèles multiniveaux de trafic proposés dans la littérature sont statiques car ils utilisent un ensemble de niveaux de détail prédéfinis et ces représentations ne peuvent pas commuter pendant la simulation. De plus ces modèles multiniveaux considèrent généralement seulement deux niveaux de détail. Très peu de travaux se sont intéressés à la modélisation dynamique multiniveau de trafic.Cette thèse propose un modèle holonique multiniveau et dynamique du trafic à grande échelle.La commutation dynamique des niveaux de détail lors de l’exécution de la simulation permet d’adapter le modèle aux contraintes liées à la qualité des résultats ou aux ressources de calcul disponibles.La proposition étend l'algorithme DBSCAN dans le contexte des systèmes multi-agents holoniques. De plus, une méthodologie permettant la commutation dynamique entre les différents niveaux de détail est proposée. Des indicateurs multiniveaux basés sur l'écart type sont aussi proposés afin d'évaluer la cohérence des résultats de la simulation

Development of a Human-Agent Interaction System including Norm and Emotion in an Evacuation Situation (Student Abstract)

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

Human-agent Explainability: An Experimental Case Study on the Filtering of Explanations

International audience12th International Conference on Agents and Artificial Intelligence (ICAART 2020), Valletta, Malta, 1970.The communication between robots/agents and humans is a challenge, since humans are not typically capable of understanding the robot's state of mind. To overcome this challenge, this paper relies on the recent advances of the domain of eXplainable Artificial Intelligence (XAI) to trace the decisions of the agents and increase the human's understandability of the agents behaviour, and hence improve efficiency and user satisfaction. In particular, we propose a Human-Agent EXplainability Architecture (HAEXA) to model human-agent explainability. HAEXA filters the explanations provided by the agents to the human user to reduce the user's cognitive load. To evaluate HAEXA, a human-computer interaction experiment is conducted, where participants watch an agent-based simulation of aerial package delivery and fill in a questionnaire that collects their responses. The questionnaire is built according to XAI metrics as established in the literature. The significance of the results is verified using emph{One-tailed Mann-Whitney U} tests. The results show that explainability increases the understandability of the simulation by human users. However, too many details in the explanations overwhelms them; hence, in many scenarios it is preferable to filter the explanations

A Cyber-Physical System for Semi-autonomous Oil & Gas Drilling Operations

International audienceIn Oil&Gas drilling operations and after reaching deep drilled depths, high temperature increases significantly enough to damage the down-hole drilling tools, and the existing mitigation process is insufficient. In this paper, we propose a Cyber-Physical System (CPS) where agents are used to represent the collaborating entities in Oil\&Gas fields both up-hole and down-hole. With the proposed CPS, down-hole tools respond to high temperature autonomously with a decentralized collective voting based on the tools' internal decision model while waiting for the cooling performed up-hole by the field engineer. This decision model, driven by the tools' specifications, aims to withstand high temperature. The proposed CPS is implemented using a multiagent simulation environment, and the results show that it mitigates high temperature properly with both the voting and the cooling mechanisms

A critical review of the use of holonic paradigm in traffic and transportation systems

International audienceThe paper presents a critical review of the use of holonic paradigm in order to model and simulate traffic and transportation systems. After an introduction presenting the principles of this paradigm as well as its frameworks and concepts, the paper surveys existing works using the holonic paradigm for traffic and transportation applications. This is followed by a detailed analysis of the results of the survey. In particular, the relevance, the design approaches and the holonification orientation methodologies are investigated. Finally, based on this extensive review, open issues of holonic paradigm in modeling and simulation of traffic and transportation models are highlighted.Holonic multi-agent system; Holonic manufacturing system; Multilevel; Traffic; Transportatio

The quest of parsimonious XAI ::a human-agent architecture for explanation formulation

With the widespread use of Artificial Intelligence (AI), understanding the behavior of intelligent agents and robots is crucial to guarantee successful human-agent collaboration since it is not straightforward for humans to understand an agent's state of mind. Recent empirical studies have confirmed that explaining a system's behavior to human users fosters the latter's acceptance of the system. However, providing overwhelming or unnecessary information may also confuse the users and cause failure. For these reasons, parsimony has been outlined as one of the key features allowing successful human-agent interaction with parsimonious explanation defined as the simplest explanation (i.e. least complex) that describes the situation adequately (i.e. descriptive adequacy). While parsimony is receiving growing attention in the literature, most of the works are carried out on the conceptual front. This paper proposes a mechanism for parsimonious eXplainable AI (XAI). In particular, it introduces the process of explanation formulation and proposes HAExA, a human-agent explainability architecture allowing to make it operational for remote robots. To provide parsimonious explanations, HAExA relies on both contrastive explanations and explanation filtering. To evaluate the proposed architecture, several research hypotheses are investigated in an empirical user study that relies on well-established XAI metrics to estimate how trustworthy and satisfactory the explanations provided by HAExA are. The results are analyzed using parametric and non-parametric statistical testing

Towards a multi-agent model to prevent damage caused by cocoa mirids to cocoa pods

Co-localisées avec la Plate-Forme Intelligence Artificielle (PFIA 2019)International audienceAgriculture in general and cocoa farm in particular is one of the primary sources of income for several African countries. Although quite useful for the development of Africa, cocoa production is faced with several challenges like the diseases of cocoa trees. Mirid (Sahlbergella singularis) is the worst pest affecting cocoa production across west Africa causing 30% to 40% of damage on cocoa production. It is difficult to control the mirids’ population and its impact on the cacao production. Related works on mirids studied the mirids population as a macroscopic entity without considering local interactions of individual mirids. These models did not consider the behaviours and the environment of mirids which are essential due to their complexity. To overcome these drawbacks, this paper proposes an agent-based model of mirids considering the individual interactions of mirids within the environment. The proposed model consists of agents representing the mirids through their life cycle in cocoa farm environments. It is based on the biological and ecological partial knowledge found in the literature, and is built according to the GAIA methodology. The main assets of this model are the reproduction of the dynamics of the mirids on a cocoa pod and damage caused by them at the micro level for human users (entomologists, agricultural engineers, farmers and decision makers) to thoroughly understand the phenomena and the emergent properties outcomes.L’agriculture en général et la culture du cacao en particulier est l’une des principales sources de revenus de plusieurs pays africains. Cependant, la production du cacao est confrontée à plusieurs défis tels que les maladies et l’attaque des insectes nuisibles. Les mirides (Sahlbergella singularis) sont les insectes les plus préjudiciables à la culture du cacao en Afrique. Avec 30 à 40 % de dommages sur la production, il est difficile de contrôler la population des mirides ainsi que son impact sur la production de cacao. Les travaux précédents sur les mirides, étudient la population de cet insecte selon le point de vue macroscopique sans tenir compte des interactions locales entre les mirides. Ces modèles ne prennent pas en compte les comportements individuels des mirides et leurs interactions avec l’environnement qui sont essentiels en raison de leurs complexités. Pour remédier à ces insuffisances, le présent article propose un modèle de mirides à base d’agents prenant en compte les interactions individuelles des mirides dans leur environnement. Le modéle proposé est constitué d’agents représentant le cycle de vie du miride sur une cabosse de cacao. Il est basé sur les connaissances biologiques et écologiques du miride, et est construit selon la méthodologie GAIA. Ces modèles ainsi que leurs implémentations sont destinés à des utilisateurs tels que les entomologistes, les ingénieurs agronomes, les agriculteurs et les décideurs. Cela leur permettra de mieux comprendre les phénomènes émergents dues aux mirides ainsi que leurs propriétés