Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence Generalized Planning: Synthesizing Plans that Work for Multiple Environments

Citations in the corpus

• Neural Logic Machines (2019, methodology): “General planning methods solve these tasks via planning based on rules (Hu & De Giacomo, 2011; Srivastava et al., 2011; Jiménez et al., 2019).”
• A review of generalized planning (2019, background): “Conformant, contingent or POMDP plans belong also to this class if we consider that, the possible initial states represent different classical planning instances all sharing the same state variables, actions and goals [42].”
• Model-free, Model-based, and General Intelligence (2018, background): “A and B where the goal is A on B, is called generalized planning [Srivastava et al., 2011; Hu and De Giacomo, 2011; Bonet et al., 2017].”
• Learning Generalized Reactive Policies using Deep Neural Networks (2017, unknown): “On the other hand, the literature on generalized planning (Srivastava, Immerman, and Zilberstein 2011; Hu and De Giacomo 2011) has focused on computing iterative generalized plans that solve broad classes of problem instances, with strong formal guarantees of correctness.”