Beliefs We Can Believe in: Replacing Assumptions with Data in Real-Time Search


  • Maximilian Fickert Saarland University
  • Tianyi Gu University of New Hampshire
  • Leonhard Staut Saarland University
  • Wheeler Ruml University of New Hampshire
  • Joerg Hoffmann Saarland University
  • Marek Petrik University of New Hampshire



Suboptimal heuristic search algorithms can benefit from reasoning about heuristic error, especially in a real-time setting where there is not enough time to search all the way to a goal. However, current reasoning methods implicitly or explicitly incorporate assumptions about the cost-to-go function. We consider a recent real-time search algorithm, called Nancy, that manipulates explicit beliefs about the cost-to-go. The original presentation of Nancy assumed that these beliefs are Gaussian, with parameters following a certain form. In this paper, we explore how to replace these assumptions with actual data. We develop a data-driven variant of Nancy, DDNancy, that bases its beliefs on heuristic performance statistics from the same domain. We extend Nancy and DDNancy with the notion of persistence and prove their completeness. Experimental results show that DDNancy can perform well in domains in which the original assumption-based Nancy performs poorly.




How to Cite

Fickert, M., Gu, T., Staut, L., Ruml, W., Hoffmann, J., & Petrik, M. (2020). Beliefs We Can Believe in: Replacing Assumptions with Data in Real-Time Search. Proceedings of the AAAI Conference on Artificial Intelligence, 34(06), 9827-9834.



AAAI Technical Track: Planning, Routing, and Scheduling