© Copyright 2015 ACM. Current research in planning focuses mainly on so called domain independent models using the Planning Domain Description Language (PDDL) as the domain modeling language. This declarative modeling approach embraces the idea of a physics-only model describing how actions change the world. However, PDDL omits information about why and when the actions should be applied to reach the goal, which significantly decreases the practical applicability of PDDL. There exist approaches such as Hierarchical Task Networks (HTN) and control rules that add this type of information to the model with the pay-off of increased efficiency but also with the downside of increased complexity and code sizes. In this paper we propose a modeling framework for planning problems based on tabled logic programming that exploits a planner module in the Picat language. This modeling framework supports structured description of world states as well as inclusion of control knowledge and heuristics in the action model. By using problems from the International Planning Competition, we experimentally demonstrate that this modeling framework achieves results comparable to planners with control rules and HTN while keeping the size of the domain model much smaller. We also show that it gives much better solving efficiency than the state-of-the-art domain-independent PDDL planners.