In this paper we develop an algorithm to optimise a nonlinear utility function of multiple objectives over the integer efficient set. Our approach is based on identifying and updating bounds on the individual objectives as well as the optimal utility value. This is done using already known solutions, linear programming relaxations, utility function inversion, and integer programming. We develop...

The last five years have seen a surge in interest in the use of techniques from Bayesian decision theory to address problems in AI. Decision theory provides a normative framework for representing and reasoning about decision problems under uncertainty. Within the context of this framework, researchers in uncertainty in the AI community have been developing computational techniques for building ...

the problems of sequential change-point have several important applications in quality control, signal processing, and failure detection in industry and finance. we discuss a bayesian approach in the context of statistical process control: at an unknown time $tau$, the process behavior changes and the distribution of the data changes from p0 to p1. two cases are considered: (i) p0 and p1 are fu...

Bayesian optimization depends on solving a global optimization of a acquisition function. However, the acquisition function can be extremely sharp at high dimension having only a few peaks marooned in a large terrain of almost flat surface. Global optimization algorithms such as DIRECT are infeasible at higher dimensions and gradient-dependent methods cannot move if initialized in the flat terr...

NO FREE LUNCH, BAYESIAN INFERENCE, AND UTILITY: A DECISION-THEORETIC APPROACH TO OPTIMIZATION Christopher Kenneth Monson Department of Computer Science Doctor of Philosophy Existing approaches to continuous optimization are essentially mechanisms for deciding which locations should be sampled in order to obtain information about a target function’s global optimum. These methods, while often eff...

the differential information literature in economics and game theory? Why has the those posteriors are derived by updating the common prior. Perhaps the most world can be generated by conditioning by Bayes rule. If there are many. Find a weak perfect bayesian equilibrium (strategies and beliefs) where the be arbitrary (and thus don't have to be according to Bayesian updating) but I am under Bro...

Taking into account competitive markets, manufacturers attend more customer’s personalization. Accordingly, build-to-order systems have been given more attention in recent years. In these systems, the customer is a very important asset for us and has been paid less attention in the previous studies. This paper introduces a new build-to-order problem in the supply chain. This study focuses on bo...

This study presents an approach to use Bayesian belief networks in various optimization tasks in resource and environmental management. A belief network is constructed to work parallel to a deterministic model, and it is used to update conditional probabilities associated with different components of the model. The propagation of probabilistic information occurs in two directions in the network...

This paper proposes an unconstrained optimization approach to the design and upgrade of sensor networks. The proposition is motivated by the fact that current methods require threshold values for different instrumentation network attributes, which are in turn very difficult to determine. In fact, these attributes are, after all, related to the process economics and therefore should not be arbit...