Learning probabilistic action models from interpretation transitions
Tipo de documentoTexto en actas de congreso
Fecha de publicación2015
Condiciones de accesoAcceso abierto
Probabilistic planners are very flexible tools that provide good solutions for difficult tasks. However, they rely on a model of the domain and actions, which they have difficulties to learn for complex tasks. We propose a new learning approach that (a) requires only a set of state transitions to learn the model; (b) can cope with uncertainty in the effects; (c) uses a relational representation to generalize over different objects; and (d) in addition to action effects, it can also learn
CitaciónMartínez, D., Ribeiro, T., Inoue, K., Alenyà, G., Torras, C. Learning probabilistic action models from interpretation transitions. A: Technical Communications of the International Conference on Logic Programming. "Proceedings of the Technical Communications of the 31st International Conference on Logic Programming (ICLP 2015)". Cork: 2015, p. 1-14.
Versión del editorhttp://ceur-ws.org/Vol-1433/tc_30.pdf