Activities of Research / Research work by Guillaume GUERARD. Projects, seminars, conferences, conferences, internship supervision, all the latest news on research activities.
CST
2023 – 2024
2022 – 2023
2021 – 2022
2020 – 2021
2019 – 2020
2018 – 2019
2017 – 2018
2016 – 2017
Pablo THOMASSIN: mixed clustering algorithm by pretapology
Guillaume GALLET: smishing detection tools
Benjamin SLOSBERG: multi-agent modeling of human contentment in temporary work
Quentin GABOT: profiling tourist behavior
Sumayya CHABANE (with Supergrid-Institute): optimization by multi-agents of a hydraulic power plant with energy market
Lilia BEN BACCAR: pattern mining for predicting tourist behavior
Hugo POUSSEUR: multi-agent model of an eco-district
Manon RIVOIRE: modeling of home automation through learning
Marc RUAULT: game networks applied to an eco-neighborhood
Zeinab NEHAI: request-response and home automation
Bastien PICHON: demand-side management and demand-response
Since the 1950s, it has become clear to scientists that a new way of thinking about and analyzing a system is essential to understanding many of the complex challenges facing humanity. The new paradigm involves inferring rules about how the dynamic behavior of a complex system depends on the combined properties of individual elements, the nature of interactions between elements, as well as the topology of interactions between elements, in order to understand and predict these systems and control them to have desirable properties.
For example, it is important to know which characteristics of complex systems generate resilience against disturbances versus properties that improve the sensitivity of the system and allow it to move to a different equilibrium state for a wide range of questions, for example on climate change and the collapse of ecosystems, the evolution of species and microorganisms, energy systems or even human displacement/migration.
Complex systems are studied from four complementary angles: emergence, resilience, phase transitions and predictability/control. These abstract unifying concepts are linked across various disciplines; mathematical models using numerous fields of research as presented in Figure 1.1.
Before talking about the two complex systems studied (electricity network and tourist flow), this introduction will allow readers to better familiarize themselves with the different concepts related to complex systems.
It is not possible to talk about complex systems without talking about Shannon entropy and chaos theory. The first is a mathematical function which, intuitively, corresponds to the amount of information contained or delivered by an information source. The entropy of a complex system has a particular behavior. Indeed, the entropy of the system as a whole is greater than the sum of the entropies of each of its subsystems. From a mathematical point of view, the overall system has more degrees of freedom (emergent properties) than all of its subsystems.
A system falls under chaos theory if it is extremely sensitive to small causes and if its behavior has a cyclical aspect. For example, weather systems are very sensitive to small causes and their dynamics are cyclical: the seasons. Each behavior of the system is a point defined in “n”-dimensional spaces, called phase space.
Trajectories in phase space converge towards areas called attractors. The same system can have one or more attractors. Attractors are important because they represent stable zones in the sense that the system tends to join these zones or to migrate to another stable zone. A basin of attraction is a space of points which tend to join irremediably towards an attractor.
The systems that fall under chaos theory come in two forms: formal systems and empirical systems. The former are defined mathematically, often in the form of nonlinear differential equations, for example the Lorenz equations.
Empirical systems are unpredictable because of their sensitivity to initial conditions that it is impossible to rigorously define, or even count. These systems evolve towards basins of attraction, but their dynamics within these basins are unpredictable.
Empirical systems are observational and cannot be modeled mathematically in an accurate manner. To better understand this notion, they are often compared to an elephant that blind people try to recognize by touch (figure 1.2); note that in this example the global system is known, which is rarely the case.
The study of complex systems has been concretized and formalized by the work of the Santa Fe Institute since 1984. This institute's mission is to research laws common to complex systems of varied natures; to define analysis and forecasting tools. A complex system is defined as follows:
A complex system is made up of agents that interact with each other, with their environment and with the emergent phenomena created by these interactions. The agents can be of varied nature: an animal, a person, a group of people, an institution, an organ, a cell, an enzyme. An agent's behavioral rules define the stimuli it emits to other agents based on the stimuli it receives from other agents and its environment. These rules evolve according to the agent's experience: stimuli that it has received and that it has emitted. Emergence is a process of creating phenomena through the interactions of agents: among themselves and with their environment. Emerging phenomena are of varied nature, for example the appearance of a new agent, a modification of the environment, a law of distribution of events. Emergences are not planned or managed by an authority that would have an overall view of the system.
Complex systems can be of different types. My work focuses on artificial systems (artifacts). These are systems built by humans mainly using computers, such as: electricity distribution networks, fuloscopy, swarm robotics. These systems have the potential for functionalities of complex adaptive systems.
Table 1.1 compares the Cartesian approach with a systems approach. The different characteristics of the complex systems [20] present in figure 1.3 will be detailed later.
Complex systems work from the bottom up. It is the agents at the bottom of the hierarchy who make the systems work, who produce emergent phenomena through interactions between them and with their environment.
The role of the top of the hierarchy is limited to creating conditions favorable to the emergence of the desired phenomena (basin of attraction). The capacities for adaptation and innovation of an organization thus decentralized are notably greater than those of a centralized structure.
Complex systems cannot be divided, remember that entropy tells us that “the whole is more than the sum of the parts”. To study a complex system, all of its components must be considered simultaneously: agents, interactions and the environment. It is also necessary to take into account the interactions and the temporal evolution of the system.
It is necessary to bring together multidisciplinary skills that cover all facets of the system studied. This arises from holism which does not allow properties linked to different disciplines to be studied in isolation. As a reminder, Figure 1.1 presents various theories related to all complex systems.
Agents have different properties, laws, rules, behaviors and actions. Agents can however be grouped by class. Very often the specification of agents is of the order of UML diagrams in 4+1 views. The diversity of agents reinforces the properties of complex adaptive systems: emergence, innovation, self-organization.
Medium and long-term developments in complex systems are unpredictable because it is impossible to define all the variables with the precision required for a forecast. Accuracy requirements grow exponentially with the scope of forecasts.
It can be futile to try to identify the cause of a situation observed in a system because this situation is often due to multiple causes. It is impossible to identify a main cause among these multiple causes which have been the subject of a succession of amplifications and attenuations due to feedbacks.
For example, the causes of the rise or weakening of a civilization are often uncertain with controversies fueled by numerous theses. It is necessary to distinguish the accidental cause which triggered an event from all the causes which created the latent state which allowed this triggering. This accidental cause is easily identifiable and is sometimes wrongly considered as the main cause of this event.
Complex systems are subject to shifts which are sudden developments in terms of their scale and speed. For example, a collective enthusiasm, an economic crisis, a revolution, or the initial conditions. A tipping point is a state of a system where a small cause can cause a profound and abrupt change in the state of the system.
Like any chaotic system, a complex system has cycles of evolution between the different basins of attractions. A shift can be conceptualized as a jump from one basin of attraction to another. We often do not know how to identify the attractors of complex systems.
Feedback occurs when an agent receives stimuli that are influenced by the stimuli it emitted. Feedbacks generate the fundamental properties of complex systems: holism, convergence towards basins of attraction, cyclical dynamics, bifurcations, shifts, evolution, adaptation, emergence, self-organization, etc. Feedbacks can have a stabilizing effect, for example regulating supply by demand in a market, or on the contrary an amplifying effect, for example creating booms whose archetype is a speculative bubble. The adaptive capacities of complex systems are due to feedback which causes the rules of behavior of their agents to evolve based on their experiences.
Combinations of these characteristics lead to the main characteristic of all complex systems: emergence. Emergence is the creation (of a holistic view) or self-organization (of a micro view) of new characters or phenomena in the system. Emergence is necessary for adaptation, evolution, coevolution, reproduction.
This occurs in various forms:
1. an adaptation/evolution of the characteristics of agents in space and time;
2. an adaptation/evolution of behavior and rules of agents in space and time;
3. self-organization with specialization of agents;
4. self-organization with hierarchy of agents;
5. coevolution between a pair or more agents;
6. a creation of new agents by evolution and specialization of existing agents.
The study of complex systems has different objectives:
• Determine the agents;
• Understand their functioning, their laws;
• Understand their evolution;
• Predict their developments;
• Define interventions to make them evolve in the desired direction.
Systemic study has a wide range of methods and tools:
1. agent-based simulation
2. graph theory and networks, collective intelligence, analogy with other complex systems
3. chaos edge theory, morphogenesis, chaos theory, catastrophe theory, fuzzy variable logic, memetics
4. operational research and metaheuristic algorithms (see figure 1.4).
These tools work iteratively. They calculate the state of a system at time ti+1 based on its entire state at the previous time ti. They take into account the entire state of the system considered: states of the agents and the environment, stimuli emitted by the agents and the environment.
Agent-based simulation is a response to the excessive complexity of solving through mathematics in the study of complex systems [10]. It is often even impossible to put them into an equation because of the variety of agents and their rules of behavior, the evolution of the rules of behavior of agents according to their experiences. At any time all agents can have different rules of behavior. Complete modeling of cognitive agents, people or groups of people, is impossible. We must therefore simplify while retaining what is relevant for the phenomena studied. The modeling of complex systems are decision support tools.
The process which leads to deciding on an action implements a set of convergent operations, logical or not, on a more or less important and relevant group of information, based on a set of knowledge, in an environment determined in order to obtain a result. The relevance of the procedure followed to make a decision is rarely evaluated because it is very complex, only the result is in relation to an initially sought objective.
Decision aids are therefore operations that facilitate the decision-making task by simplifying or shortening the cognitive and mathematical path followed by agents. The functions of these aids can be very diverse [23]:
• searches for relevant information;
• organization of information;
• partial or total processing of disjoint sets of information;
• ordered activation of knowledge;
• establishment of scenarios;
• spatial and/or temporal representations;
• proposed decisions.
Agents, cognitive or reactive, all use decision support processes in order to adopt a particular behavior (see Figure 1.5) [26]. All of these decisions cause, disrupt or vary the emergence, resilience, phases and control of the entire system.
The modeling of a complex system can vary depending on the decision sought. A generic model then leads to a decision being made on all aspects of this system.
RESUME THE CV TO MAKE THE FOLLOWING SECTIONS WITH REF
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achraf
LNL and digital twin
Energy development has brought about a paradigm shift in the 21st century. Industrial entities, political bodies, and the scientific community are seeking to make cities and the grid smarter. The French Institute for Demographic Studies (INED) states that the world's population will reach 10 billion by 2050, while the urban population will double (an increase of 63% of GDP). This widespread urbanization necessitates new ways of understanding and managing the complexities of energy production and consumption.
Today, although cities occupy only 2% of the Earth's surface, they are home to approximately 50% of the world's population, consume 75% of the total energy generated, and are responsible for 80% of greenhouse gas emissions (United Nations Environment Programme, Visions for Change: Recommendations for Effective Policies on Sustainable Living, 2011). The development of smart cities depends on the intelligence of electrical grids, from producers to consumers and from consumers back to producers. The most important aspect is coordination between all entities; a microgrid can incentivize consumers to adjust their consumption under critical conditions to avoid damaging the electrical infrastructure.
Around the world, smart grids are being developed to reduce electrical waste and prevent power outages. Simulating a microgrid, an eco-neighborhood, or a virtual power plant is difficult, given their different behaviors and structures. Each varies according to several aspects: social, economic, energy, mobility, and the well-being of its inhabitants.
Smart grids and smart cities need to be understood. They are generally described as complex systems, and the best way to analyze them is through modeling and simulation. In this context, the goal is to create an out-of-context model to provide decision-making tools for social, economic, and algorithmic issues within a smarter microgrid.
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The grid constantly needs to estimate its consumption/production in the near and distant future. Previously based on data from previous days and years, the smart grid must be equipped with predictive systems that analyze data in real time.
To achieve this, many machine learning tools, and more specifically deep learning tools, rely on data from hundreds of sensors to make reliable predictions. However, this system is complex to implement, operate, and maintain.
The aim of the work carried out on this subject is to provide a reliable consumption prediction tool without requiring the conditioning of a building. The data used can be provided by a simple smart meter such as a Linky.
The energy consumption of each device is transformed into consumption sequences. These sequences then provide sufficient data for prediction. The models used so far are derived from data mining and are based on prefix tree learning. The models are either Markovian (with or without grammatical inference) or compact trees.
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Due to the speed at which information is exchanged online by users, many destinations have seen a surge in tourism. Businesses recognized Instagram's enormous potential as an advertising platform early on, and many savvy companies are collaborating with the site's top influencers to act as brand ambassadors. The Lake Wanaka Tourism Board in New Zealand is a prime example: in 2015, they invited Instagram influencers to share their photos of the region. As a result, tourist visits jumped by 14%, representing the fastest growth rate in New Zealand.
Analyzing tourist sites is relatively easy, but analyzing tourist flows between hotspots within the same city is a challenge. The capacity of our current technologies and processing algorithms is limited by three aspects: data volume, data generation speed, and the variety of data types, also known as the 3Vs.
Tourists want a recommendation based on their interests because time is a key element in trip planning and it is a task that takes a lot of time when done by the tourist.
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The network's overload of digital tools renders its entire system insensitive to the threat of computer viruses. The number of attacks by polymorphic viruses or botnets increases as the system migrates from physical to digital systems.
Detecting polymorphic viruses is complex, if not impossible, without knowledge of the network and its behavior. Detecting suspicious activity is then possible by comparing reality with the system's various predictions.