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Modelling Multi-agent Three-way Decisions with Decision-theoretic Rough Sets

Yang X., Yao J. T.

Fundamenta Informaticae

2012

Vol. 115, nr 2/3

157-171

The decision-theoretic rough set (DTRS) model considers costs associated with actions of classifying an equivalence class into a particular region. With DTRS, one may make informative decisions in the form of three-way decisions. Current research mainly focuses on single agent DTRS which is too complex for making a decision when multiple agents are involved. We propose a multiagent DTRS model and express it in the form of three-way decisions. The new model seeks for synthesized or consensus decisions when there aremultiple decision preferences and criteria adopted by different agents. Various multi-agent DTRS models can be derived according to the conservative, aggressive and majority viewpoints based on the positive, negative and boundary regions made by each agent. These multi-agent decision regions are expressed by figures in the form of three-way decisions.

Game-Theoretic Rough Sets

Herbert J. P., Yao J. T.

Fundamenta Informaticae

2011

Vol. 108, nr 3/4

267-286

This article investigates the Game-theoretic Rough Set (GTRS) model and its capability of analyzing a major decision problem evident in existing probabilistic rough set models. A major challenge in the application of probabilistic rough set models is their inability to formulate a method of decreasing the size of the boundary region through further explorations of the data. To decrease the size of this region, objects must be moved to either the positive or negative regions. Game theory allows a solution to this decision problemby having the regions compete or cooperatewith each other in order to find which is best fit to be selected for the move. There are two approaches discussed in this article. First, the region parameters that define the minimum conditional probabilities for region inclusion can either compete or cooperate in order to increase their size. The second approach is formulated by having classification approximation measures compete against each other. We formulate a learning method using the GTRS model that repeatedly analyzes payoff tables created from approximationmeasures and modified conditional risk strategies to calculate parameter values.