Some Older Development Projects: CANTI, I_TRACE, CEC-WYS, Agents Intelligents, Représentation Logique des Connaissances pour les Agents Intelligents, Continuous Education Program on Intelligent Agents Technology and Knowledge Processing, INT200…
Some Past Research Grands: AGATE, SCIPA, CATIIS, A-ROADS, ADEPT, MIRA, RLN Negotiation Framework, ICARUS, ARGUMENT, AGENT-FISHBANK, AGCOR, COOP…
Consortium composed of six universities from four countries: France (University of Pierre and Marie Curie Paris 6, University of Lyon Lumière Lyon 2, Polytec’Nantes), Romania (University Polithenica of Bucharest), Italy (University of East Piedmont) and Spain (Technical University of Catalonia).
The Master in DMKM is based on experience in multi-site teaching gained from the Master’s degree in Knowledge Extraction from Data, which has been running since 1999 within three members of the consortium.
Erasmus Mundus Project 2010-2016.
The objective is to develop the Romanian students’ abilities to work as to improve the insertion of specialists from the field of artificial intelligence in the national and European work market.
POSDRU/109/2.1/G/81772, 2012-2013
Member of ICT COST Action TD1202 Mapping and the citizen sensor, 2012-2016.
Member of COST Action IC0801: Agreement Technologies. Agreement Technologies refer to computer systems in which autonomous software agents negotiate with one another, typically on behalf of humans, in order to come to mutually acceptable agreements. 2008-2012.
Member of EU-ROBOTICS
The main goal of ERRIC is to achieve excellence in research in selected areas of Intelligent Information Technologies: agreement technologies, semantic and collaborative technologies for the web, advanced grid technologies, large scale distributed system services, and adaptive intelligent control.
FP7-ICT No. 264207, 2010-2013.
A robotic platform designed to be used in a heterogeneous robotic swarm and that has many hardware and software state of the art features. Internal project, since 2015.
The aim of the AmIciTy initiative is to create a software infrastructure for Ambient Intelligence applications, that handles context at its constructive level, that works naturally with the user’s tasks and activities, and that manifests the robustness and reliability that is required for future ambient systems. Internal project, since 2014.
Mobile@Old extracts a mobility pattern for each user and generates remainders and alerts regarding his/her physical and other daily activities.
In case of low physical activity, the user is advised to increase the level of physical exercise.
The exercises are performed interactively, as an adaptive game, specifically designed for elderly persons.
The intensity level of the recommended physical exercises is generated and adapted based on both vital parameters of the user (analyzed using a medical expertise) and also on the observed behavior by a team of psychologists.
Mobile@Old is financed by PCCA programme, PNII, duration: 2014-2017.
IONIS will offer fully integrated and validated solutions for health monitoring, home automation, personal agenda with reminders, alerts, caregiver administrative tools. The platform will integrate technologies and services that can address dementia specific challenges and offer support to both caretakers and caregivers: location based services for indoor localization, LBS for object localization by integration of own solutions based on embedded Linux and commercial Bluetooth tags, LBS for outdoor localization and geofencing to allow dementia sufferers to enjoy outdoor activities and perform essential task such as shopping or visiting, alerting and easy communication through one-button calls to designated caregivers, sleep quality monitoring through non-wearable sensors, recommending outdoor activities according to the level of indoor effort extracted from mobility patterns and sleep quality. AI-MAS is member of the consortium.
The IONIS project is founded by the EU Active and Assisted Living Programme, duration: 2017-2020.
INCARE product is a software platform compatible with health devices, home automation sensors, robots and dedicated to the elderly and older adults living independently or in elderly care facilities. Our platform aims to help them live longer, more connected and more independent.
INCARE will turn national and European funded projects into viable products by building on two successful solutions developed within previous AAL and European projects. Both NITICS, funded within the AAL 2012 call, and RAPP, funded 2013-2016 through the EC through the 7th Framework Programme FP7, were designed to support elderly people to live independently in their home environment and social circle. Read more about these projects at NITICS and RAPP.
The INCARE project is partially funded by the Active and Assisted Living (AAL) – European Commission.
The objective of this project is the creation of an open source context management middleware solution presenting rich features that alleviate context-aware application development. Based on the existing platform CONSERT, developed in the AIMAS laboratory, the work improves upon CONSERT with: performance improvement and modularisation of the reasoning engine to support applications of variable complexity requirements, development of an IDE for context modeling and deployment to facilitate context-aware application programming based on the CONSERT Middleware, and advanced options for OSGi and Docker based deployments of CONSERT processing units to address dynamic change of context within an application.
The CONSERT project is financed by “Exploratory Grants” programme, PNIII, duration: 2016-2018.
SPARC is developed in cooperation with CITST company that aims at exploiting robotic technology as a service offer for its potential clients. The envisaged applications are product promotion and/or clients’ assistance in public places such as malls, museums, exhibitions by means of an autonomous robot. In this context, the main goal of the project is the design and implementation of a robotic platform to achieve integrated planning of robot tasks and robot interaction with the clients.
The SPARC project is financed by “Bridge Grant” programme, PNIII, duration: 2016-2018.
AI4GENDARED (Imbunătățirea Capacităților Funcționale ale aplicației GENDARED prin utilizarea Algoritmilor de Inteligență Artificială) aims to improve the Functional Capabilities of the GENDARED Application by using Artificial Intelligence Algorithms.
The Intelligent Agent for Recipe Recommendation Based on Visual Identification of Available Ingredients (IntAli) project has as objectives the development of a software agent capable of recommending recipes to users based on what ingredients they have available in their fridge, to research, propose and validate methods for image-based recognition of ingredients placed on shelves of a fridge. In addition, IntAli has as objectives to create semantic models of the knowledge in the project for information querying and extraction, to build recommender systems for recipes, to analyze user interaction and feedback, as well as to design and build a prototype smart-fridge which integrates the sub-systems proposed in the project.
Subsidiary Project: 20175/30.10.2019, part of NETIO P_40_270 53/05.09.2016.
The scope of the project is to monitor daily activities and to recognize possible critical and dangerous situations (falls of the elderly).
Subsidiary project: 1268/22.01.2018, Deployment period: 22.01.2018 – 31.12.2020.
The scope of the project is to create an integrated and easily configurable solution for customizing social and assistive robots, using artificial intelligence methods that can provide a cognitive and autonomous character to robots.
Subsidiary project: 1226/22.01.2018, Deployment period: 22.01.2018 – 31.12.2020.
The employability of IT graduates is a challenge in the context of a rapidly evolving environment featuring novel and unexpected ideas and technologies.
Our target group are young, local and international IT master students and future IT leaders who wish to benefit from the experience of researchers and industry specialists, during seminars in which industry and academia collaborate for a shared curriculum.
The consortium is formed of 8 partners from 4 European countries: 4 leading Universities and 4 Corporate partners.
The project will organize 4 seminars with international students.
The project is Co-funded by the Erasmus+ Programme of the European Union.
Nemodrive brings together a group of motivated researchers with the required resources and support to build a self-driving car that will be tested on the UPB campus streets. The prototype will be developed to tackle the particularities of campus and other local roads. In this context, some of the most recurring challenges of self-driving cars will be empirically put to test, in a small scale, un-controlled environment.
Nemodrive is founded on a desire that drives both its theoretical as well as empirical aspirations: to continuously drive interest and understanding of machine learning, duration: 2018-2020.
CAMI is offering a fully integrated Ambient Assisted Living solution by offering services for health management, home management and wellbeing (including socialization, and reduced mobility support). CAMI builds an artificial intelligence ecosystem, which allows seamless integration of any number of ambient and wearable sensors with a mobile telepresence system endowed with multimodal interaction (touch, voice, person detection), including a telepresence robot. This will allow older adults to self-manage their daily life and prolong their involvement in the society while allowing their informal caregivers to continue working whilst caring for their loved ones.
The consortium comprises 8 partners from Romania, Sweden, Switzerland, Denmark, and Polland. AI-MAS is the consortium coordinator.
The CAMI project is founded by the EU Active and Assisted Living Programme (Call 2014 – “Care for the Future”), duration: 2015-2018.
NETIO Objectives: development of innovative products and services in the domain of IoT and Big Data, empowering innovation activities of companies in the domains of IoT and Big Data through knowledge transfer and common research activities with UPB, implication of UPB staff in all stages of technological transfer for IoT and Big Data based products and services. Duration: 2017-2020
Project co-financed by the European Fond for Regional Development in the framework of the Operational Programme for Competitiveness 2014-2020.
ROBIN is a user centered project which develops systems and services for the use of robots in an interconnected digital society. It comprises 5 sub-projects. ROBIN-Social develops integrated and configurable solutions for the personalization of assistive and social autonomous robots. ROBIN-Car develops computer vision methods devoted to autonomous driving and a prototype systems to be tested on an electric car. ROBIN-Context creates a support platform for the semantic representation and management of data that becomes context in scenarios of personalized robotic assistance and AADS. ROBIN-Dialog develops a set of scenarios for micro-worlds and the technology for the Romanian language processing to achieve situational dialogs in these micro-worlds. ROBIN-Cloud builds a support platform for collecting data coming from the sensors of robotic systems and IoT devices that offers Cloud Edge and Cloud Robotics computing.
The ROBIN project is financed by UEFISCDI “Complex Projects” programme, PNIII, duration: 2018-2020.
Detecting, tracking, and recognizing people is a valuable capability for machines. However, these tasks are quite difficult to be achieved autonomously and, although significant results have been obtained, they are still a major technological challenge. People tracking, unlike other recognition and interpretation tasks, is difficult both from the point of view of the recognition and prediction of trajectory, and from the one of the identifications of the ground truth.
The main objective of the PETRA project is the development of a software platform enabling the development of applications requiring people detection and tracking in real environments. The design and implementation of the platform and the set of supported algorithms for people detection and tracking will be such that they can be easily used and integrated in tasks performed by social robots in closed spaces and in tasks in open spaces, such as the case for pedestrian detection and tracking. The scientific and technological challenge of the project is to start from our current developments on people detection and tracking in the contexts of user-robot interaction and autonomous driving to develop and implement novel solutions based on deep learning approaches. One of the project challenges is to extensively test the implementations towards several difficult benchmarks, but also on our own data sets, and strive to achieve results better than current state-of-the-art.
The Digital Twin for Complex Infrastructures and Urban Ecosystems is an advanced and specialized academic offering designed primarily for industry professionals looking to expand their skills in the rapidly evolving landscape of civil engineering technology. The principal objective of the program is to equip professionals with the necessary tools and knowledge to harness the power of digital twin technologies and apply them in their respective fields to enhance the design, operation, and maintenance of civil infrastructures and urban ecosystems.
The digital twin technology, characterized by the creation of a dynamic virtual clone of a physical entity is quickly becoming a game-changer in the field of civil engineering. It adds to increased efficiency and resilience, as well as cost optimization, by facilitating real-time monitoring, predictive analysis, and informed decision-making processes. The program’s focus on this emerging technology guarantees the delivery of cutting-edge knowledge and practical skills, placing our alumni as market leaders.
Implementing a vision in which intelligent agents perceive and act in an environment while searching for, exchanging, annotating, and improving machine learning models, forming a culture based on experience and interaction.
Our research goal is to develop a knowledge model and an interaction protocol which allow a system formed of multiple actors — human, software, or organizational — to find, use and share improvements on machine learning resources. Such resources can be datasets, models, or experiences.
We propose the development of the AI Folk framework and methodology, at the intersection of machine learning, knowledge management, and multi-agent systems. It comprises tools and methods that allow the management and discovery of ML-related resources in a distributed system. Federated learning has yet to achieve maturity as a field of study and this is a novel approach which assumes an open system and a variety of resources. We believe that this approach will lead to an advance in the state of the art and will help in the development of standards for open and distributed artificial intelligence.
The project result will be an ontology for describing machine learning resources, a methodology for creating searches for resources, an interaction protocol through which actors can search, transfer and update machine learning resources, the implementation of two applications using this approach, and a general methodology for applying the proposed approach to other application domains.
In hypermedia-driven Multi-Agent Systems (HyperAgents) the web and its architecture act like a global environment in which different types of agents (whether human or software) perform interactions and compose the functionality of web-enabled things to achieve their goals.
The main objective of this project is the development of a web-based framework facilitating context-aware search and discovery of web-enabled things in hypermedia driven environments. The requirement is to facilitate the detection and understanding of shared context between a producer and a consumer to enable corresponding discovery of and access to relevant web resources to agents operating in the web environment.
To achieve its objective the project aims to:
1) Develop a web-based platform that can expressively model relevant context information of web things and enable a semantic-query and complex-event processing based mechanism to define and detect situations of shared context between web things and agents;
2) Develop a web-based APIs that take the detection of a shared context as input and are able to facilitate the creation of authorization elements that integrate into existing access control and search technologies for web things.
In healthcare, the cost of treating brain disorders accounts for a significant percentage of total treatment costs. In Europe, the budget for treating brain disorders alone far surpasses the combined budget for other diseases. Utilising cutting-edge AI, the EU-funded ALAMEDA project could greatly reduce the cost of treating these conditions by enabling personalised care and improved treatments for major brain disorders. The goal is to demonstrate AI-enabled prediction, prevention and intervention, making the treatment of disorders such as Parkinson’s, multiple sclerosis and strokes more affordable and easing the burden on healthcare systems across Europe.
ALAMEDA is a truly pan-European project and brings together a highly skilled and complementary consortium of 15 expert organisations from across 8 countries. University Politehnica of Bucharest is coordinating the WP3.
The project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 101017558.
Virtual patients are able to represent patients in realistic clinical scenarios and engage learners in doctor-patient conversations about the patient’s health, interpret laboratory results and medical images, and form a diagnosis. Currently, due to the conditions created by the COVID-19 pandemic, the interaction of medical students with patients is limited, therefore virtual doctor-patient interaction environments become a safe and practical solution for training medical skills, including blended learning. With the current advances in artificial intelligence, virtual patients can be equipped with advanced functions, such as the integration of different types of conversational agents or support for the automatic prediction of the patient’s evolution.
In this context, the main objective of the project is to develop a virtual environment to simulate the interactions between the learner (medical student or doctor/resident) and a virtual clinical patient for the diagnosis and treatment of acute and chronic heart diseases. The virtual environment, supported by a software platform and artificial intelligence technologies, simulates real-life clinical scenarios in which the student emulates the role of the doctor who performs an examination of the virtual patient and obtains a history, performs anamnesis, physical examination, paraclinical investigations, establishes a diagnosis and recommends a therapeutic plan.
