Bayesian Reinforcement Learning has generated substantial interest recently, as it provides an elegant solution to the exploration-exploitation trade-off in reinforcement learning. However most investigations of Bayesian reinforcement learning to date focus on the standard Markov Decision Processes (MDPs). Our goal is to extend these ideas to the more general Partially Observable MDP (POMDP) fr...

Influence diagrams and decision trees represent the two most common frameworks for specifying and solving decision problems. As modeling languages, both of these frameworks require that the decision analyst specifies all possible sequences of observations and decisions (in influence diagrams, this requirement corresponds to the constraint that the decisions should be temporarily linearly ordere...

The purpose of this research is to discuss Japanese financial instability and both private life insurance and public life insurance (Kampo) demand. At the same time, we also consider Kampo’s main role and what Kampo should be as an insurance service provider in the future. From empirically estimating private life insurance and Kampo demand functions using household-level data provided by the Po...

Diffusion models have been used traditionally in marketing for capturing the lifecycle dynamics of a new product, for forecasting the demand for a new product, and as a decision aid in making pre-launch, launch and post-launch strategic choices. Since their entrance into marketing, diffusion models have become increasingly complex. This complexity has been driven by the need to enhance the fore...

We treat the Bayesian decision problem, mainly the twocategory case. A three layered neural network, having a logistic output unit and a small number of hidden layer units, can approximate the a posteriori probability in L-norm, without knowing the type of the probability distribution before learning, if the log ratio of the a posteriori probabilities is a polynomial of low degree as in the cas...

The problem of finding an optimal variable ordering for Binary Decision Diagrams (BDD) or Multi-Valued Decision Diagrams (MDD) is widely known to be NP-Complete. This paper presents a survey of static heuristic techniques applied to ordering the variables of the BDD/MDD under construction in order to minimize the overall size of the resulting decision diagram.

Induction of decision rules within the dominance-based rough set approach to the multiple-criteria sorting decision problem is discussed in this paper. We introduce an algorithm called DOMLEM that induces a minimal set of generalized decision rules consistent with the dominance principle. An extension of this algorithm for a variable consistency model of dominance based rough set approach is al...

OBJECTIVE To conduct a systematic review of randomised trials of patient decision aids in improving decision making and outcomes. DESIGN We included randomised trials of interventions providing structured, detailed, and specific information on treatment or screening options and outcomes to aid decision making. Two reviewers independently screened and extracted data on several evaluation crite...

An industrial ethane thermal cracking reactor was modeled assuming a molecular mechanism for the reaction kinetics coupled with material, energy, and momentum balances of the reactant-product flow along the reactor. To carry out the multi-objective optimization for two objectives such as conversion and ethylene selectivity, the elitist non-dominated sorting genetic algorithm was used. The Paret...