The effect of domain modeling on efficiency of planning: Lessons from the Nomystery domain

Nomystery is a simple logistic planning domain proposed for the International Planning Competition. The task is to plan loading, driving, and unloading actions for a single truck with unlimited load capacity but with limited fuel to transport packages between various locations. In this paper we show how different modeling techniques influence efficiency of planning for the Nomystery domain. In particular, we compare factored and structured representations of states enhanced with heuristics and control knowledge. We use the Picat planner module that exploits tabling to memorize visited states and that uses iterative deepening or branch-and-bound to search for optimal plans.

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