An Anytime Algorithm for Decision Making under Uncertainty
This work addresses decision-making under uncertainty for domains where optimal policies are computationally infeasible, offering a practical incremental approach.
The authors tackled the problem of computing policies for multi-stage influence diagrams under uncertainty, presenting an anytime algorithm that incrementally builds policies from no information towards optimality, demonstrating on large decision problems that it yields valuable sub-optimal policies faster than traditional methods.
We present an anytime algorithm which computes policies for decision problems represented as multi-stage influence diagrams. Our algorithm constructs policies incrementally, starting from a policy which makes no use of the available information. The incremental process constructs policies which includes more of the information available to the decision maker at each step. While the process converges to the optimal policy, our approach is designed for situations in which computing the optimal policy is infeasible. We provide examples of the process on several large decision problems, showing that, for these examples, the process constructs valuable (but sub-optimal) policies before the optimal policy would be available by traditional methods.