In decision-theoretic planning, the problem of planning under uncertainty is formulated as a multidimensional, or factoredMDP. Traditional dynamic programming techniques are ine cient for solving factored MDPs whose state and action spaces are exponential in the number of the state and action variables, correspondingly. We focus on exploiting problems' structure imposed by variable independence...

In decision-making on optimal treatment strategies, it is of great importance to identify variables that are involved in the decision rule, i.e. those interacting with the treatment. Effective variable selection helps to improve the prediction accuracy and enhance the interpretability of the decision rule. We propose a new penalized regression framework which can simultaneously estimate the opt...

To help decision-makers better understand the decision procedure and result is a major objective of decision analysis. In linguistic decision, symbolic scheme thereby is more attractive than set-based scheme. The counted (expanded) linguistic variable (CLV/CELV) model is proposed for representing and aggregating linguistic decision information. The model is developed from the ordinary linguisti...

Multi-agent system research is concerned with the issues surrounding the performance of collections of interacting agents. A major concern, therefore, is with the design of the decision making mechanism that the individual agents employ in order to determine which actions to take to achieve their goals. An attractive and much sought after property of this mechanism is that it produces decisions...

This paper discusses variable selection for medical decision making; in particular decisions regarding when to provide treatment and which treatment to provide. Current variable selection techniques were developed for use in a supervised learning setting where the goal is optimal prediction of treatment response. These techniques often leave behind small but important interaction variables that...