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This paper studies robust knapsack problems, for which solutions are, up to a certain point, immune to data uncertainty. We complement the works found in the literature where uncertainty affects only the profits or only the weights of the items by studying the complexity and approximation of the general setting with uncertainty regarding both the profits and the weights, for multiple objective functions...
It is desirable to represent and infer uncertain knowledge under multidimensional situations due to the wide applications of multidimensional data. Bayesian network (BN) is a generally accepted framework for representing and inferring probabilistic causalities among random variables. In this paper, by adopting the notation of classification, we use an extended augmented naive Bayesian network, called...
Mobile robots that interact with humans in an intuitive way must be able to follow directions provided by humans in unconstrained natural language. In this work we investigate how statistical machine translation techniques can be used to bridge the gap between natural language route instructions and a map of an environment built by a robot. Our approach uses training data to learn to translate from...
Creation of an effective metrics and estimation program is an important but daunting step for the maturing software development organization This paper outlines a roadmap for implementing a process that establishes a program that will reap a large portion of the benefits early in the process with a minimum of implementation effort and cost This process includes a mechanism to improve software estimation...
This paper proposes a novel detection scheme based on Dempster-Shafer (D-S) evidence theory in multiple-input multiple-output (MIMO) systems. The focal-element-set (FES) characterizes the uncertainty contained in the decision statistics, and the corresponding basic-probability-assignment (BPA) of FES is the likelihood measure, acting as the soft-decisions of the transmit signals. The uncertainty can...
We make an improvement on McCahon and Lee's algorithm for two-machine flow shop problem with uncertain processing time represented with fuzzy number. Especially, the scheme used in McCahon and Lee's algorithm for ranking fuzzy processing times is modified to calculate better the minimum makespan. Example and extensive simulation results are presented to show the improved performance on finding optimal...
We discuss methods for solving steady state process optimization problems under parametric uncertainty. The problem is formulated as a two-stage optimization problem (TSOP) which is inherently multiextremal and nondifferentiable. An indirect approach (split and bound method, SB) has been developed to address the nondifferentiability issue. The SB method iteratively solves for lower and upper bounds...
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