“…• Critical path heuristics (Haslum & Geffner, 2000;Haslum, Bonet, & Geffner, 2005) estimate the goal distance by computing lower bound estimates on the cost of achieving sets of facts of a predefined size, • Delete relaxation heuristics (Nguyen, Kambhampati, & Nigenda, 2002;Hoffmann & Nebel, 2001;Domshlak, Hoffmann, & Katz, 2015) estimate the cost of reaching a goal state by ignoring the negative effects of actions, • Abstraction heuristics (Ededlkamp, 2001;Helmert, Haslum, Hoffmann, & Nissim, 2014;Katz & Domshlak, 2008) try to collapse several states into one depending on their properties in order to reduce the size of the search space. When the abstraction is small enough it is feasible to quickly find an optimal solution for the abstracted problem that is used as a heuristics value during the search, • Landmarks heuristics (Hoffmann, Porteous, & Sebastia, 2004;Richter, Helmet, & Westphal, 2008) are based on the observation that some propositions are true at some point in every plan to reduce and decompose the search space.…”