Inference and learning with planning models

• Autores: Diego Aineto García
• Directores de la Tesis: Eva Onaindia de la Rivaherrera (dir. tes.), Sergio Jiménez Celorrio (dir. tes.)
• Lectura: En la Universitat Politècnica de València ( España ) en 2022
• Idioma: español
• Tribunal Calificador de la Tesis: Roman Bartak (presid.), Laura Sebastiá Tarín (secret.), Daniel Borrajo Millán (voc.)
• Programa de doctorado: Programa de Doctorado en Informática por la Universitat Politècnica de València
• Materias:
  • Ciencias tecnológicas
    • Tecnología de los ordenadores
      • Dispositivos de control
• Enlaces
  • Tesis en acceso abierto en: RiuNet
• Resumen

  • Inference and learning are the acts of reasoning about some collected evidence in order to reach a logical conclusion regarding the process that originated it. In the context of a state-space model, inference and learning are usually concerned with explaining an agent's past behaviour, predicting its future actions or identifying its model. In this thesis, we present a framework for inference and learning in the state-space model underlying the classical planning model, and formulate a palette of inference and learning problems under this unifying umbrella. We also develop effective planning-based approaches to solve these problems using off-the-shelf, state-of-the-art planning algorithms. We will show that several core inference and learning problems that previous research has treated as disconnected can be formulated in a cohesive way and solved following homogeneous procedures using the proposed framework. Further, our work opens the way for new applications of planning technology as it highlights the features that make the state-space model of classical planning different from other models.