A forward–backward Monte Carlo method for solving influence diagrams

Although influence diagrams are powerful tools for representing and solving complex decision-making problems, their evaluation may require an enormous computational effort and this is a primary issue when processing real-world models. We shall propose an approximate inference algorithm to deal with very large models. For such models, it may be unfeasible to achieve an exact solution. This anytime algorithm returns approximate solutions which are increasingly refined as computation progresses, producing knowledge that offers insight into the decision problem.