---
title: "A POMDP Approach to Influence Diagram Evaluation"
authors:
  - "Eric A. Hansen"
  - "Jinchuan Shi"
  - "Arindam Khaled"
doi: ""
url: "https://ijcai.org/proceedings/2016/443"
pdf: "https://ijcai.org/proceedings/2016/0443.pdf"
venue: "Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence"
year: 2016
pages: "3124-3131"
track: ""
keywords:
---

# A POMDP Approach to Influence Diagram Evaluation

Eric A. Hansen, Jinchuan Shi, Arindam Khaled

*Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence*, pages 3124-3131.

## Abstract

We propose a node-removal/arc-reversal algorithm for influence diagram evaluation that includes reductions that allow an influence diagram to be solved by a generalization of the dynamic programming approach to solving partially observable Markov decision processes (POMDPs). Among its potential advantages, the algorithm allows a more flexible ordering of node removals, and a POMDP-inspired approach to optimizing over hidden state variables, which can improve the scalability of influence diagram evaluation in solving complex, multi-stage problems. It also finds a more compact representation of an optimal strategy.