Browsing by Author "Jonsson, Anders, 1973-"

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  • Intelligent group formation in computer supported collaborative learning scripts
    Amarasinghe, Ishari; Hernández Leo, Davinia; Jonsson, Anders, 1973- (IEEE, 2017)
    Well-structured collaborative learning groups scripted based on Collaborative Learning Flow Patterns (CLFPs) often result in successful collaborative learning outcomes. Formulation of such learner groups based on instructor ...
  • Learner group formation using constraint optimization
    Amarasinghe, Ishari; Hernández Leo, Davinia; Jonsson, Anders, 1973- (2017)
    Learner group formation manually based on multiple criteria often fails due to its complexity and time limitations of practitioners. This paper presents a novel binary integer programming approach which models learner ...
  • Lifelong control of off-grid microgrid with model-based reinforcement learning
    Totaro, Simone; Boukas, Ioannis; Jonsson, Anders, 1973-; Cornélusse, Bertrand (Elsevier, 2021)
    Off-grid microgrids are receiving a growing interest for rural electrification purposes in developing countries due to their ability to ensure affordable, sustainable and reliable energy services. Off-grid microgrids rely ...
  • Limitations of acyclic causal graphs for planning
    Jonsson, Anders, 1973-; Jonsson, Peter; Lööw, Tomas (Elsevier, 2014)
    Causal graphs are widely used in planning to capture the internal structure of planning instances. Researchers have paid special attention to the subclass of planning instances with acyclic causal graphs, which in the past ...
  • Potential and pitfalls of multi-armed bandits for decentralized spatial reuse in WLANs
    Wilhelmi Roca, Francesc; Barrachina Muñoz, Sergio; Bellalta, Boris; Cano Bastidas, Cristina; Jonsson, Anders, 1973-; Neu, Gergely (Elsevier, 2019)
    Spatial Reuse (SR) has recently gained attention to maximize the performance of IEEE 802.11 Wireless Local Area Networks (WLANs). Decentralized mechanisms are expected to be key in the development of SR solutions for ...
  • Probabilistic activity recognition in navigation
    Font Sola, Octavi; Francès, Guillem; Jonsson, Anders, 1973-; Bartie, Phil; Mackaness, William (Institute of Electrical and Electronics Engineers (IEEE), 2014)
    In this paper we present a novel probabilistic approach to activity recognition. Our approach is to estimate posterior probabilities of different activities using Bayes' rule. The approach can handle any type of activities ...
  • Re-producing public space: the changing everyday production of outdoor retail markets
    MMP; Breines, Markus; Dahinden, Janine; Jonsson, Anders, 1973-; Lindmäe, Maria; Madella, Marco; Menet, Joanna; Schapendonk, Joris; van Eck, Emil; Melik, Rianne van; Watson, Sophie (Taylor & Francis, 2022)
    In 2020, nation states across Europe restricted access to, and use of, public space to prevent the spread of COVID-19. As almost all public spaces in Europe were consequently affected by restrictive measures, so too did ...
  • Reinforcement learning for active modality selection during diagnosis
    Bernardino Perez, Gabriel; Jonsson, Anders, 1973-; Loncaric, Filip; Martí Castellote, Pablo Miki; Sitges, Marta; Clarysse, Patrick; Duchateau, Nicolas (Springer, 2022)
    Diagnosis through imaging generally requires the combination of several modalities. Algorithms for data fusion allow merging information from different sources, mostly combining all images in a single step. In contrast, ...
  • Solving concurrent multiagent planning using classical planning
    Furelos Blanco, Daniel; Jonsson, Anders, 1973- (Association for the Advancement of Artificial Intelligence (AAAI) - Congrés ICAPS17, 2018)
    In this work we present a novel approach to solving concurrent multiagent planning problems in which several agents act in parallel. Our approach relies on a compilation from concurrent multiagent planning to classical ...
  • Solving multiagent planning problems with concurrent conditional effects
    Furelos Blanco, Daniel; Jonsson, Anders, 1973- (Association for the Advancement of Artificial Intelligence (AAAI), 2019)
    In this work we present a novel approach to solving concurrent multiagent planning problems in which several agents act in parallel. Our approach relies on a compilation from concurrent multiagent planning to classical ...
  • State representation learning for goal-conditioned reinforcement learning
    Steccanella, Lorenzo; Jonsson, Anders, 1973- (Springer, 2022)
    This paper presents a novel state representation for reward-free Markov decision processes. The idea is to learn, in a self-supervised manner, an embedding space where distances between pairs of embedded states correspond ...
  • Sustaining continuous collaborative learning flows in MOOCs: orchestration agent approach
    Amarasinghe, Ishari; Hernández Leo, Davinia; Manathunga, Kalpani; Jonsson, Anders, 1973- (Graz University of Technology. Institut für Informationssysteme und Computer Medien (IICM), 2018)
    Collaborative learning spaces deployed in Massive Open Online Courses (MOOCs) provide productive social learning opportunities. However, sustaining collaboration in these spaces is challenging. This paper provides a ...
  • Towards data-informed group formation support across learning spaces
    Amarasinghe, Ishari; Hernández Leo, Davinia; Jonsson, Anders, 1973- (2017)
    Learning via collaboration has gained much success over past few decades given their learning benefits. Group composition has been seen as a relevant design element that contributes to the potential effectiveness of ...
  • Unsupervised classification of planning instances
    Segovia-Aguas, Javier; Jiménez, Sergio; Jonsson, Anders, 1973- (Association for the Advancement of Artificial Intelligence (AAAI), 2017)
    In this paper we introduce a novel approach for unsupervised classification of planning instances based on the recent formalism of planning programs. Our approach is inspired by structured prediction in machine learning, ...
  • Unsupervised learning for C-RAN power control and power allocation
    Nikbakht Silab, Rasoul; Jonsson, Anders, 1973-; Lozano Solsona, Angel (Institute of Electrical and Electronics Engineers (IEEE), 2021)
    This letter applies a feedforward neural network trained in an unsupervised fashion to the problem of optimizing the transmit powers in centralized radio access networks operating on a cell-free basis. Both uplink and ...
  • Unsupervised learning for cellular power control
    Nikbakht Silab, Rasoul; Jonsson, Anders, 1973-; Lozano Solsona, Angel (Institute of Electrical and Electronics Engineers (IEEE), 2021)
    This letter applies a feedforward neural network trained in an unsupervised fashion to the problem of optimizing the transmit powers in cellular wireless systems. Both uplink and downlink are considered, with either ...
  • Unsupervised learning for parametric optimization
    Nikbakht Silab, Rasoul; Jonsson, Anders, 1973-; Lozano Solsona, Angel (Institute of Electrical and Electronics Engineers (IEEE), 2021)
    This letter proposes the unsupervised training of a feedforward neural network to solve parametric optimization problems involving large numbers of parameters. Such unsupervised training, which consists in repeatedly ...
  • Unsupervised-learning power allocation for the cell-free downlink
    Nikbakht Silab, Rasoul; Jonsson, Anders, 1973-; Lozano Solsona, Angel (Institute of Electrical and Electronics Engineers (IEEE), 2020)
    This paper applies feedforward neural networks to the problem of centralized power allocation in the downlink of cell-free wireless systems with conjugate beamforming. The formulation relies only on large-scale channel ...
  • Unsupervised-learning power control for cell-free wireless systems
    Nikbakht Silab, Rasoul; Jonsson, Anders, 1973-; Lozano Solsona, Angel (Institute of Electrical and Electronics Engineers, 2019)
    This paper studies the viability of feedforward neural networks (NNs) for centralized power control in the uplink of cell-free wireless systems with matched-filter reception. The formulation relies only on large-scale ...
  • Usage of network simulators in machine-learning-assisted 5G/6G networks
    Wilhelmi Roca, Francesc; Carrascosa Zamacois, Marc; Cano, Cristina; Jonsson, Anders, 1973-; Ram, Vishnu; Bellalta, Boris (Institute of Electrical and Electronics Engineers (IEEE), 2021)
    Without any doubt, Machine Learning (ML) will be an important driver of future communications due to its foreseen performance when applied to complex problems. However, the application of ML to networking systems raises ...

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