Unifying the Causal Graph and Additive Heuristics

Many current heuristics for domain-independent planning, such as Bonet and Geffner's additive heuristic and Hoffmann and Nebel's FF heuristic, are based on delete relaxations. They estimate the goal distance of a search state by approximating the solution cost in a relaxed task where negative consequences of operator applications are ignored. Helmert's causal graph heuristic, on the other hand, approximates goal distances by solving a hierarchy of "local" planning problems that only involve a single state variable and the variables it depends on directly. Superficially, the causal graph heuristic appears quite unrelated to heuristics based on delete relaxation. In this contribution, we show that the opposite is true. Using a novel, declarative formulation of the causal graph heuristic, we show that the causal graph heuristic is the additive heuristic plus context. Unlike the original heuristic, our formulation does not require the causal graph to be acyclic, a...

• Type: Conference
• Year: 2008
• Where: AIPS
• Authors: Malte Helmert, Hector Geffner
• Keywords: Additive Heuristic, AIPS 2008, Artificial Intelligence, Causal Graph, Heuristic