Abstract
Planning under uncertainty in multiagent settings is highly intractable because of history and plan space complexities. Probabilistic graphical models exploit the structure of the problem domain to mitigate the computational burden. In this article, we introduce the first parallelization of planning in multiagent settings on a CPU-GPU heterogeneous system. In particular, we focus on the algorithm for exactly solving interactive dynamic influence diagrams, which is a recognized graphical models for multiagent planning. Beyond parallelizing the standard Bayesian inference and the computation of decisions’ expected utilities, we also solve the other agents behavioral models in a parallel manner. The GPU-based approach provides significant speedup on two benchmark problems.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsNotes
- 1.
A GUI-based software application called Netus is freely available from http://tinyurl.com/mwrtlvg for designing I-DIDs.
References
Bernstein, D.S., Givan, R., Immerman, N., Zilberstein, S.: The complexity of decentralized control of markov decision processes. Math. Oper. Res. 27(4), 819–840 (2002)
Berstein, D.S., Hansen, E.A., Zilberstein, S.: Bounded policy iteration for decentralized POMDPs. In: IJCAI, pp. 1287–1292 (2005)
Chandrasekaran, M., Doshi, P., Zeng, Y., Chen, Y.: Team behavior in interactive dynamic influence diagrams with applications to ad hoc teams. In: AAMAS, pp. 1559–1560 (2014)
Chen, Y., Hong, J., Liu, W., Godo, L., Sierra, C., Loughlin, M.: Incorporating PGMs into a BDI architecture. In: Boella, G., Elkind, E., Savarimuthu, B.T.R., Dignum, F., Purvis, M.K. (eds.) PRIMA 2013. LNCS, vol. 8291, pp. 54–69. Springer, Heidelberg (2013)
Doshi, P., Zeng, Y., Chen, Q.: Graphical models for interactive POMDPs: representations and solutions. JAAMAS 18(3), 376–416 (2009)
Gal, K., Pfeffer, A.: Networks of influence diagrams: a formalism for representing agents’ beliefs and decision-making processes. JAIR 33, 109–147 (2008)
Gmytrasiewicz, P.J., Doshi, P.: A framework for sequential planning in multiagent settings. JAIR 24, 49–79 (2005)
Howard, R.A., Matheson, J.E.: Influence diagrams. In: Howard, R.A., Matheson, J.E. (eds.) The Principles and Applications of Decision Analysis. Strategic Decisions Group, Menlo Park (1984)
Jeon, H., Xia, Y., Prasanna, K.V.: Parallel exact inference on a cpu-gpgpu heterogenous system. In: ICPP, pp. 61–70 (2010)
Koller, D., Friedman, N.: Probabilistic Graphical Models: Principles and Techniques. MIT Press, Cambridge (2009)
Koller, D., Milch, B.: Multi-agent influence diagrams for representing and solving games. In: IJCAI, pp. 1027–1034 (2001)
Luo, J., Yin, H., Li, B., Wu, C.: Path planning for automated guided vehicles system via I-DIDs with communication. In: ICCA, pp. 755–759 (2011)
Pearl, J.: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann, Berlin (1998)
Sano, Y., Kadono, Y., Fukuta, N.: A performance optimization support framework for gpu-based traffic simulations with negotiating agents. In: ACAN (2014)
Smallwood, R., Sondik, E.: The optimal control of partially observable markov decision processes over a finite horizon. Oper. Res. 21, 1071–1088 (1973)
Søndberg-Jeppesen, N., Jensen, F. V., Zeng, Y.: Opponent modeling in a PGM framework. In: AAMAS, pp. 1149–1150 (2013)
Kozlov, A.V., Singh, J.P.: A parallel Lauritzen-Spiegelhalter algorithm for probabilistic inference. In: Supercomputing, pp. 320–329 (1994)
Xia, Y., Prasanna, K.V.: Parallel exact inference on the cell broadband engine processor. In: SC, pp. 1–12 (2008)
Zeng, Y., Doshi, P.: Exploiting model equivalences for solving interactive dynamic influence diagrams. JAIR 43, 211–255 (2012)
Zheng, L., Mengshoel, O.J., Chong, J.: Belief propagation by message passing in junction trees: computing each message faster using gpu parallelization. In: UAI (2011)
Acknowledgements
This research is supported in part by an ONR Grant, #N000141310870, and in part by an NSF CAREER Grant, #IIS-0845036. We thank Alex Koslov for making his implementation of a parallel Bayesian network inference algorithm available to us for reference.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Adoe, F., Chen, Y., Doshi, P. (2015). Speeding up Planning in Multiagent Settings Using CPU-GPU Architectures. In: Duval, B., van den Herik, J., Loiseau, S., Filipe, J. (eds) Agents and Artificial Intelligence. ICAART 2015. Lecture Notes in Computer Science(), vol 9494. Springer, Cham. https://doi.org/10.1007/978-3-319-27947-3_14
Download citation
DOI: https://doi.org/10.1007/978-3-319-27947-3_14
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-27946-6
Online ISBN: 978-3-319-27947-3
eBook Packages: Computer ScienceComputer Science (R0)