Advertisement

Lifted Temporal Maximum Expected Utility

  • Marcel GehrkeEmail author
  • Tanya Braun
  • Ralf Möller
Conference paper
  • 1.3k Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11489)

Abstract

The dynamic junction tree algorithm (LDJT) efficiently answers exact filtering and prediction queries for temporal probabilistic relational models by building and then reusing a first-order cluster representation of a knowledge base for multiple queries and time steps. To also support sequential online decision making, we extend the underling model of LDJT with action and utility nodes, resulting in parameterised probabilistic dynamic decision models, and introduce meuLDJT to efficiently solve the exact lifted temporal maximum expected utility problem, while also answering marginal queries efficiently.

References

  1. 1.
    Apsel, U., Brafman, R.I.: Extended lifted inference with joint formulas. In: Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence, pp. 11–18. AUAI Press (2011)Google Scholar
  2. 2.
    Braun, T., Möller, R.: Lifted junction tree algorithm. In: Friedrich, G., Helmert, M., Wotawa, F. (eds.) KI 2016. LNCS (LNAI), vol. 9904, pp. 30–42. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-46073-4_3CrossRefGoogle Scholar
  3. 3.
    Gehrke, M., Braun, T., Möller, R.: Lifted dynamic junction tree algorithm. In: Chapman, P., Endres, D., Pernelle, N. (eds.) ICCS 2018. LNCS (LNAI), vol. 10872, pp. 55–69. Springer, Cham (2018).  https://doi.org/10.1007/978-3-319-91379-7_5CrossRefGoogle Scholar
  4. 4.
    Gehrke, M., Braun, T., Möller, R., Waschkau, A., Strumann, C., Steinhäuser, J.: Lifted maximum expected utility. In: Koch, F., et al. (eds.) AIH 2018. LNCS (LNAI), vol. 11326, pp. 131–141. Springer, Cham (2019).  https://doi.org/10.1007/978-3-030-12738-1_10CrossRefGoogle Scholar
  5. 5.
    Murphy, K.P.: Dynamic bayesian networks: representation, inference and learning. Ph.D. thesis, University of California, Berkeley (2002)Google Scholar
  6. 6.
    Nath, A., Domingos, P.: A language for relational decision theory. In: Proceedings of the International Workshop on Statistical Relational Learning (2009)Google Scholar
  7. 7.
    Sanner, S., Kersting, K.: Symbolic dynamic programming for first-order POMDPs. In: Proceedings of the Twenty-Fourth AAAI, pp. 1140–1146. AAAI Press (2010)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Institute of Information SystemsUniversity of LübeckLübeckGermany

Personalised recommendations