Learning Relational Dynamics of Stochastic Domains for Planning. Mart�nez, D., Ribeiro, T., Inoue, K., Alenya, G., & Torras, C. 2016.
abstract   bibtex   
Probabilistic planners are very flexible tools that can provide good solutions for difficult tasks. However, they rely on a model of the domain and action, which may be costly to either hand code or automatically 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 exogenous effects that are not related to any action, e.g., moving objects, endogenous growth and natural development. The proposed learning approach combines a multi-valued variant of inductive logic programming for the generation of candidate models, with an optimization method to select the best set of planning operators to model a problem. Finally, experimental validation is provided that shows improvements over previous work.
@inproceeduings {icaps16-40,
    track    = {Main Track},
    title    = {Learning Relational Dynamics of Stochastic Domains for Planning},
    author   = {David Mart�nez and  Tony Ribeiro and  Katsumi Inoue and  Guillem Alenya and  Carme Torras},
    year     = {2016},
    abstract = {Probabilistic planners are very flexible tools that can provide good solutions for difficult tasks. However, they rely on a model of the domain and action, which may be costly to either hand code or automatically 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 exogenous effects that are not related to any action, e.g., moving objects, endogenous growth and natural development. The proposed learning approach combines a multi-valued variant of inductive logic programming for the generation of candidate models, with an optimization method to select the best set of planning operators to model a problem. Finally, experimental validation is provided that shows improvements over previous work.},
    keywords = {Learning in planning and scheduling}
}
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