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Concept

Rule-based machine learning

Résumé

Rule-based machine learning (RBML) is a term in computer science intended to encompass any machine learning method that identifies, learns, or evolves 'rules' to store, manipulate or apply. The defining characteristic of a rule-based machine learner is the identification and utilization of a set of relational rules that collectively represent the knowledge captured by the system. This is in contrast to other machine learners that commonly identify a singular model that can be universally applied to any instance in order to make a prediction. Rule-based machine learning approaches include learning classifier systems, association rule learning, artificial immune systems, and any other method that relies on a set of rules, each covering contextual knowledge. While rule-based machine learning is conceptually a type of rule-based system, it is distinct from traditional rule-based systems, which are often hand-crafted, and other rule-based decision makers. This is because rule-based mac

Source officielle

https://en.wikipedia.org/wiki/Rule-based_machine_learning

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Cours associés (5)

This course introduces the key concepts and algorithms from the areas of information retrieval, data mining and knowledge bases, which constitute the foundations of today's Web-based distributed information systems.

This advanced course will provide students with the knowledge to tackle the design of privacy-preserving ICT systems. Students will learn about existing technologies to prect privacy, and how to evaluate the protection they provide.

Computer environments such as educational games, interactive simulations, and web services provide large amounts of data, which can be analyzed and serve as a basis for adaptation. This course will cover the core methods of user modeling and personalization, with a focus on educational data.

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Séances de cours associées (10)

Séances de cours associées

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Vulnérabilité sismique à grande échelle de la ville de Lausanne

Loris Silva Brites

The assessment of the risk maps for the seismic vulnerability at large scale is based on the vulnerability of each building. In order to determine these vulnerabilities, it is first required to assign to each building its construction class. The construction class is needed to define the seismic behavior of the building. Since the structures have been built in different times, this construction classes are different between them, and their vulnerability values have been pre-defined in order to simplify the analysis. If, for a small city the task of assign to each building its construction class can be done by hand, in a big city, with thousands of buildings, this process can result long and hard. In this Master project the possibility of assigning the construction classes by using a machine learning method will be studied. This method use as a data set of the city of Lausanne from previous works which contains around 1000 buildings analyzed by hand with their construction classes. The goal of this method is to find the relationships between the attributes of the buildings to their construction classes. Thanks to the statistical offices, it is possible to obtain the attributes in an automatic way for every building in a city. Based on the relations that the machine learning method finds (and using the statistical attributes) the construction class for every building can finally be determined, in a quick and efficient manner. Once obtained the vulnerability’s values for each building, the risk maps can be drawn. In this project there will be studied three maps, two with the Europeans’ typologies (LM1, LM2), and one with the Swiss’ typologies (UniGE). Some comparisons will be made between the maps, in order to highlight the differences. Some other comparisons will be made to show the impact of the new micro zonation of Lausanne and of the new optimized method for the determination of the seismic displacement demand (N2 optimized method) on the maps. This is done for the mechanical methods (LM2 and UniGE) only. Finally, there will be shown the relationships between the soil’s characteristics (micro zonation), the building’s attributes and the city’s historical evolution with the risk maps.

2020

Chargement

In order to evaluate the seismic vulnerability at large scale, it is necessary to gain awareness of the soil properties, types of materials used in the construction of buildings during different periods, the construction standards of each period, different techniques of construction, the epicentre of the earthquake, etc. An ideal evaluation would take into account all the mentioned variables above; however, in this study only the building typologies are considered (Classification according to EMS-98). More precisely, the typologies are identified for a small data set manually. Each building possesses four attributes such as: 1) construction period, 2) number of floors, 3) surface of the building, and 4) Roof shape. In order to obtain the attributes above, data sets from Statistic Federal Office (OFS) and territorial information system in Geneva (SITG) were acquired. In addition, these data which embodied many imprecisions as well as irregularities were examined and treated. Furthermore, machine learning techniques are applied and will result in the acquisition of the building typologies for the whole city of Geneva. These machine learning methods are trained on a learning set collected manually, and then applied to the testing set which corresponds to the whole attribute combinations of Geneva. All in the interest of obtaining the corresponding typologies.

2020

Chargement

Preventing Breaks in Embodiment in Immersive Virtual Reality

Thibault Serge Mario Porssut

Virtual reality (VR) is immersive not only because of visual integration, but because we can act and perform in a virtual environment (VE). Beyond the mere fact that a VR system provides images computed directly from the users' viewpoint in stereoscopy, it can also provide a natural way to interact within the VE using the entire body. Full-body motion capture allows the user's movement to be mapped to a virtual body. Such a mapping may let users feel in control of (sense of agency), own (sense of body ownership) and locate themselves inside this virtual body (sense of self-location), thus leading them to feel embodied in this virtual body whenever these conditions are met (sense of embodiment (SoE)). Hence the integration of a full body avatar in VR is essential, as it brings even more; experiencing being someone else, train to recover mobility or having superhuman abilities. Most importantly, the avatar mediates the virtual nature of the VE to make it seamless for the user. Thus, this thesis focuses on helping the users execute complex movements in VR by applying a distortion to their movements. However, such a distortion is efficient only if users, when assisted, do not experience a break in embodiment (BiE), i.e., do not abruptly lose their sense of embodiment. Moreover, the way the virtual body is perceived is not consistent between users and some users might accept or reject a virtual body more easily than others. Therefore, this thesis explores how to prevent a BiE while distorting users' movement and adapting the VR experience to each individual. To this end, we designed a system combining brain-computer interface and machine learning to detect when users experience a BiE, compute users' preferences, and adapt the VR application's parameters. We first identified the user`s threshold for perceiving a movement distortion in some critical contexts, such as articular limits. We designed a new distortion to help users execute a complex movement. We introduced a method based on machine learning to find users' preferences when using our new distortion. We also established a link between the brain's error monitoring mechanism and the cognitive process of embodiment. This link exposed a new implicit EEG marker indicating when users experience a BiE. Finally, we designed a system to calibrate the distortion implicitly based on users' preferences with all these elements. This system first consists of detecting a BiE during a continuous movement thanks to state-of-the-art brain-computer interface algorithms. Our machine learning method then computes users' preferences, and the distortion is adapted dynamically. This original approach demonstrates the use of an implicit feedback loop to help users while preventing any BiE. Our framework opens an exciting perspective for personalized and self-adapting embodiment systems.

EPFL2021

Chargement

Vulnérabilité sismique à grande échelle de la ville de Lausanne

Loris Silva Brites

2020

Preventing Breaks in Embodiment in Immersive Virtual Reality

Thibault Serge Mario Porssut

EPFL2021