@article{maua_campos_zaffalon_2012, 
  title={Solving Limited Memory Influence Diagrams}, 
  volume={44}, 
  DOI={10.1613/jair.3625}, 
  abstractNote={<jats:p>We present a new algorithm for exactly solving decision making problems represented as influence diagrams. We do not require the usual assumptions of no forgetting and regularity; this allows us to solve problems with simultaneous decisions and limited information. The algorithm is empirically shown to outperform a state-of-the-art algorithm on randomly generated problems of up to 150 variables and 10^64 solutions. We show that these problems are NP-hard even if the underlying graph structure of the problem has low treewidth and the variables take on a bounded number of  states, and that they admit no provably good approximation if variables can take on an arbitrary number of states.
</jats:p>}, 
  publisher={AI Access Foundation}, 
  author={Maua and Campos and Zaffalon}, 
  year={2012}, 
  month={May}
  }