Learning Planning Rules in Noisy Stochastic Worlds

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Learning First-Order Symbolic Representations for Planning from the Structure of the State Space (2019, neutral): “In contrast to other approaches for learning stochastic action models [38], this method does not require symbolic inputs but the structure of the space in the form of an AND-OR graph.”
A review of learning planning action models (2018, background): “In a series of works by (Pasula et al. (2004, 2007), Zettlemoyer et al. (2005)), the goal is to learn action models in noisy and stochastic domains.”
Bayesian Time Series Models: Expectation maximisation methods for solving (PO)MDPs and optimal control problems (2011, methodology): “For instance, in the eld of planning, they include work on Factored Markov Decision Processes (Boutilier et al., 1995; Koller and Parr, 1999; Guestrin et al., 2003; Kveton and Hauskrecht, 2005), abstractions (Hauskrecht et al., 1998), and relational models of the environment ( Zettlemoyer et al., 20”
Solving Relational and First-Order Logical Markov Decision Processes: A Survey (2012, methodology): “The approach was later extended by Zettlemoyer et al (2005) who added noise outcomes, i.e. outcomes which are difficult to model exactly, but which do happen (like knocking over a blocks tower and scattering all blocks on the floor).”