Solving constraint satisfaction planning problems is a challenging area in artificial intelligence due to its inherent complexity. These problems require finding solutions that meet a set of constraints, which can be computationally intensive and mentally taxing for humans. To address this, an automatic inferencing approach can significantly reduce the mental burden on humans by automating the reasoning process. In this study, I present a logic programming example that can automatically reason and find feasible solutions that satisfy multiple constraints in a constraint satisfaction planning problem instance. This approach not only demonstrates the potential of logic programming in handling complex tasks but also highlights its efficiency in solving real-world problems.