Recent work on Game Theory and DecisionTheoretic Rough Sets
Game Theory Rough Set Analysis Software
Source code (Java)
Game Theory and DecisionTheoretic Rough Sets
The use of parameters or thresholds to define approximation regions in probabilistic
rough set models is a key principle for using these models to analyze realword
situations. These parameters allow for the relaxation of certainty in a universe of
discourse. The following articles utilize gametheoretic approach to calculating
optimal values for these parameters will be extended in this article. Using game
theory, we are able to formulate a sequence of risk modifications that optimize the
relationship between two measures of approximation. This sequence can be thought of as
a learning method to govern the adjustment of approximation region parameters. Through
the use of loss tolerance ranges and modification of the user's notion of
classification risk, this approach can be used to establish optimal parameter values to
divide the universe given the current levels of classification risk.

J.P. Herbert, J.T. Yao, Learning Optimal Parameters
in DecisionTheoretic Rough Sets, 4th International Conference on Rough
Sets and Knowledge Technology (RSKT'09), LNAI 5589, 2009, Gold Coast, Australia.

J.T. Yao, J.P. Herbert,
A GameTheoretic Perspective on Rough Set Analysis, 2008
International Forum on Knowledge Technology (IFKT'08), Chongqing, Journal of Chongqing
University of Posts and Telecommunications, Vol. 20, No. 3, pp 291298, 2008.

J.P. Herbert, J.T. Yao,
GameTheoretic Risk Analysis in DecisionTheoretic Rough Sets,
3rd International Conference on Rough Sets and Knowledge Technology (RSKT'08), LNAI
5009, 2008, Chengdu, P.R. China, pp 132139.
Decision Making with DecisionTheoretic Rough Sets
These articles go into depth regarding the different types of decisions that a user may
attempt utilizing information gathered from decisiontheoretic rough set analysis. We
have categorized decision based on the notion of how much risk is involved. A
decision has a certain amount of ambiguity associated with it  depending on the
amount of uncertainty the information gathered entails. In addtion, we integrate the
decisiontheoretic rough set model into a Webbased Support System framework.

J.P. Herbert, J.T. Yao, Criteria for Choosing a Rough Set Model, Journal of Computers
and Mathematics with Applications, 57 (6): pp 908918, 2009.
(doi:10.1016/j.camwa.2008.10.043).

J.P. Herbert, J.T. Yao,
Rough Set Model Selection for Practical Decision Making, 4th
International Conference on Fuzzy Systems and Knowledge Discovery (FSKD'07), Aug 2427,
2007, Hainan, China.

J.T. Yao, J.P. Herbert,
Webbased Support Systems with Rough Set Analysis, Rough Sets
and Emerging Intelligent Systems Paradigms (RSEISP'07), LNAI 4585, June 2830, 2007,
Warsaw, Poland, pp 360370.