Point-based Dynamic Programming for DEC-POMDPs - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Conference Papers Year : 2006

Point-based Dynamic Programming for DEC-POMDPs

Daniel Szer
  • Function : Author
  • PersonId : 830433


We introduce point-based dynamic programming (DP) for decentralized partially observable Markov decision processes (DEC-POMDPs), a new discrete DP algorithm for planning strategies for cooperative multi-agent systems. Our approach makes a connection between optimal DP algorithms for partially observable stochastic games, and point-based approximations for single-agent POMDPs. We show for the first time how relevant multi-agent belief states can be computed. Building on this insight, we then show how the linear programming part in current multi-agent DP algorithms can be avoided, and how multi-agent DP can thus be applied to solve larger problems. We derive both an optimal and an approximated version of our algorithm, and we show its efficiency on test examples from the literature.
Fichier principal
Vignette du fichier
aaai2006.pdf (103.08 Ko) Télécharger le fichier

Dates and versions

inria-00104443 , version 1 (06-10-2006)


  • HAL Id : inria-00104443 , version 1


Daniel Szer, François Charpillet. Point-based Dynamic Programming for DEC-POMDPs. 21st National Conference on Artificial Intelligence - AAAI'2006, Jun 2006, Boston/USA. ⟨inria-00104443⟩
96 View
115 Download


Gmail Facebook X LinkedIn More