Combining Heuristic Search and Linear Programming to Compute Realistic Financial Plans - Pozanco, Alberto; Papasotiriou, Kassiani; Borrajo, Daniel; Veloso, Manuela (Proceedings of the International Conference on Automated Planning and Scheduling, 2023)
Defining financial goals and formulating actionable plans to achieve them are essential components for ensuring financial health. This task is computationally challenging, given the abundance of factors that can influence one’s financial situation. In this paper, we present the Personal Finance Planner (PFP), which can generate personalized financial plans that consider a person’s context and the likelihood of taking financially related actions to help them achieve their goals. PFP solves the problem in two stages. First, it uses heuristic search to find a high-level sequence of actions that increase the income and reduce spending to help users achieve their financial goals. Next, it uses integer linear programming to determine the best low-level actions to implement the high-level plan. Results show that PFP is able to scale on generating realistic financial plans for complex tasks involving many low level actions and long planning horizons.