You are required to read and agree to the below before accessing a full-text version of an article in the IDE article repository.

The full-text document you are about to access is subject to national and international copyright laws. In most cases (but not necessarily all) the consequence is that personal use is allowed given that the copyright owner is duly acknowledged and respected. All other use (typically) require an explicit permission (often in writing) by the copyright owner.

For the reports in this repository we specifically note that

  • the use of articles under IEEE copyright is governed by the IEEE copyright policy (available at
  • the use of articles under ACM copyright is governed by the ACM copyright policy (available at
  • technical reports and other articles issued by M‰lardalen University is free for personal use. For other use, the explicit consent of the authors is required
  • in other cases, please contact the copyright owner for detailed information

By accepting I agree to acknowledge and respect the rights of the copyright owner of the document I am about to access.

If you are in doubt, feel free to contact

Integrating Case-Based Inference and Approximate Reasoning for Decision Making under Uncertainty

Publication Type:

Conference/Workshop Paper


Proceedings of Swedish Workshop on Artificial Intelligence


This paper proposes a novel approach to decision analysis with uncertainty based on integrated case-based inference and approximate reasoning. The strength of case-based inference is utilized for building a situation dependent decision model without complete domain knowledge. This is achieved by deriving states probabilities and general utility estimates from the subset of retrieved cases and the case library given a situation in query. In particular, the derivation of state probabilities is realized through an approximate reasoning process which comprises evidence (case) combination using the Dempster-Shafer theory and Bayesian probabilistic computation. The decision model learnt from previous cases is further exploited using decision theory to identify the most promising, secured, and rational choices. We have also studied the issue of imprecise representations of utility in individual cases and explained how fuzzy decision analysis can be conducted when case specific utilities are assigned with fuzzy data.


author = {Ning Xiong and Peter Funk},
title = {Integrating Case-Based Inference and Approximate Reasoning for Decision Making under Uncertainty },
month = {May},
year = {2009},
booktitle = {Proceedings of Swedish Workshop on Artificial Intelligence},
url = {}