Article history: Received 7 May 2017 Received in revised form 1 July 2017 Accepted 27 July 2017 Available online 2 August 2017

There has been a surge of research interest in developing tools and analysis for Bayesian optimization, the task of finding the global maximizer of an unknown, expensive function through sequential evaluation using Bayesian decision theory. However, many interesting problems involve optimizing multiple, expensive to evaluate objectives simultaneously, and relatively little research has addresse...

We describe a formal framework for analyzing how statistical properties of natural environments and the process of natural selection interact to determine the design of perceptual and cognitive systems. The framework consists of two parts: a Bayesian ideal observer with a utility function appropriate for natural selection, and a Bayesian formulation of Darwin’s theory of natural selection. Simu...

Elicitation of an exact utility function of a decision maker is challenging. In this paper, we address the problem of ambiguity and inconsistency in utility assessments by studying a robust utility-based decision making model where the utility function belongs to a set of general increasing utility functions. We build a robust framework in which the utility function belongs to a set. This set o...

The usual approach to solving the Multiple Criteria Decision Making (MCDM) problem is by either using a weighted objective function based on each individual objective or by optimizing one objective while setting constraints on the others. These approaches try to find a point on the efficient frontier or the Pareto optimal set based on the preferences of the decision maker. Here, a new algorithm...

Deception problems are among the hardest problems to solve using ordinary genetic algorithms. Designed to simulate a high degree of epistasis, these deception problems imitate extremely difficult real world problems. [1]. Studies show that Bayesian optimization and explicit building block manipulation algorithms, like the fast messy genetic algorithm (fmGA), can help in solving these problems. ...

Decision theory does not traditionally include uncertainty over utility functions. We argue that the a person’s utility value for a given outcome can be treated as we treat other domain attributes: as a random variable with a density function over its possible values. We show that we can apply statistical density estimation techniques to learn such a density function from a database of partiall...

Decision making with adaptive utility provides a generalisation to classical Bayesian decision theory, allowing the creation of a normative theory for decision selection when preferences are initially uncertain. In this paper we address some of the foundational issues of adaptive utility as seen from the perspective of a Bayesian statistician. The implications that such a generalisation has upo...

Warranty is a powerful implement for marketing strategy that is used by manufacturersand creates satisfaction for consumers by ensuring to compensate for incorrect operation of the product. Warranty serviceresults ina cost named warranty cost for a manufacturer.This cost is a function of warranty policy, regions, and product failures pattern. Since this service coversthe cost of uncertain failu...