Browsing by Author "Segovia-Aguas, Javier"

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  • Lotinac, Damir; Segovia-Aguas, Javier; Jiménez, Sergio; Jonsson, Anders, 1973- (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 ...
  • Segovia-Aguas, Javier; Jiménez, Sergio; Jonsson, Anders, 1973- (AI Access Foundation, 2018)
    Finite State Controllers (FSCs) are an effective way to compactly represent sequential plans. By imposing appropriate conditions on transitions, FSCs can also represent generalized plans (plans that solve a range of planning ...
  • Segovia-Aguas, Javier; Jiménez, Sergio; Jonsson, Anders, 1973- (Association for the Advancement of Artificial Intelligence (AAAI), 2021)
    Although heuristic search is one of the most successful approaches to classical planning, this planning paradigm does not apply straightforwardly to Generalized Planning (GP). Planning as heuristic search traditionally ...
  • Segovia-Aguas, Javier; Jiménez, Sergio; Jonsson, Anders, 1973- (IJCAI, 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 problem ...
  • Segovia-Aguas, Javier; Jiménez, Sergio; Jonsson, Anders, 1973- (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 ...
  • 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, ...

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