Visualitza per autor "Jonsson, Anders"

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  • Lotinac, Damir; Segovia-Aguas, Javier; Jiménez, Sergio; Jonsson, Anders (Association for the Advancement of Artificial Intelligence (AAAI), 2016)
    In many domains generalized plans can only/nbe computed if certain high-level state features,/ni.e. features that capture key concepts to accurately/ndistinguish between states and make good decisions,/nare available. In ...
  • Jonsson, Anders; Lotinac, Damir (IOS Press, 2016)
    Hierarchical Task Networks (HTNs) are a common model for encoding knowledge about planning domains in the form of task decompositions. We present a novel algorithm that uses invariant analysis to construct an HTN from the ...
  • Segovia-Aguas, Javier; Jiménez, Sergio; Jonsson, Anders (2017)
    This paper presents a novel approach for generating Context-Free Grammars (CFGs) from small sets of input strings (a single input string in some cases). Our approach is to compile this task into a classical planning ...
  • Segovia-Aguas, Javier; Jiménez, Sergio; Jonsson, Anders (Association for the Advancement of Artificial Intelligence (AAAI), 2016)
    Finite State Controllers (FSCs) are an effective way/nto represent sequential plans compactly. By imposing/nappropriate conditions on transitions, FSCs/ncan also represent generalized plans that solve a/nrange of planning ...
  • Amarasinghe, Ishari; Hernández Leo, Davinia; Jonsson, Anders (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 ...
  • Amarasinghe, Ishari; Hernández Leo, Davinia; Jonsson, Anders (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 ...
  • Segovia-Aguas, Javier; Jiménez, Sergio; Jonsson, Anders (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, ...