Planning as satisfiability: Heuristics

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How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Towards Deep Symbolic Reinforcement Learning (2016, positive): “methods are capable of efficiently finding large plans in complex domains (eg:(Rintanen, 2012)), and it would be rash not to exploit the potential of these techniques.”
A review of generalized planning (2019, methodology): “The state-of-the-art algorithms for classical planning are heuristic search in the state space [37, 30] or compilation to other forms of problem solving such as SAT [78].”
Tell me Dave: Context-sensitive grounding of natural language to manipulation instructions (2014, methodology): “These constraints along with the environment are passed onto a symbolic-planner [41] which outputs the missing instructions Ij` (line 9).”