Recent work on Game Theory and Decision-Theoretic Rough Sets

Game Theory Rough Set Analysis Software

Source code (Java)

Game Theory and Decision-Theoretic 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 real-word situations. These parameters allow for the relaxation of certainty in a universe of discourse. The following articles utilize game-theoretic 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.
  1. J.P. Herbert, J.T. Yao, Learning Optimal Parameters in Decision-Theoretic Rough Sets, 4th International Conference on Rough Sets and Knowledge Technology (RSKT'09), LNAI 5589, 2009, Gold Coast, Australia.

  2. J.T. Yao, J.P. Herbert, A Game-Theoretic 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 291-298, 2008.

  3. J.P. Herbert, J.T. Yao, Game-Theoretic Risk Analysis in Decision-Theoretic Rough Sets, 3rd International Conference on Rough Sets and Knowledge Technology (RSKT'08), LNAI 5009, 2008, Chengdu, P.R. China, pp 132-139.

Decision Making with Decision-Theoretic Rough Sets

These articles go into depth regarding the different types of decisions that a user may attempt utilizing information gathered from decision-theoretic 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 decision-theoretic rough set model into a Web-based Support System framework.
  1. J.P. Herbert, J.T. Yao, Criteria for Choosing a Rough Set Model, Journal of Computers and Mathematics with Applications, 57 (6): pp 908-918, 2009. (doi:10.1016/j.camwa.2008.10.043).

  2. 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 24-27, 2007, Hainan, China.

  3. J.T. Yao, J.P. Herbert, Web-based Support Systems with Rough Set Analysis, Rough Sets and Emerging Intelligent Systems Paradigms (RSEISP'07), LNAI 4585, June 28-30, 2007, Warsaw, Poland, pp 360-370.