4,004 research outputs found
A robust dynamic classifier selection approach for hyperspectral images with imprecise label information
Supervised hyperspectral image (HSI) classification relies on accurate label information. However, it is not always possible to collect perfectly accurate labels for training samples. This motivates the development of classifiers that are sufficiently robust to some reasonable amounts of errors in data labels. Despite the growing importance of this aspect, it has not been sufficiently studied in the literature yet. In this paper, we analyze the effect of erroneous sample labels on probability distributions of the principal components of HSIs, and provide in this way a statistical analysis of the resulting uncertainty in classifiers. Building on the theory of imprecise probabilities, we develop a novel robust dynamic classifier selection (R-DCS) model for data classification with erroneous labels. Particularly, spectral and spatial features are extracted from HSIs to construct two individual classifiers for the dynamic selection, respectively. The proposed R-DCS model is based on the robustness of the classifiers’ predictions: the extent to which a classifier can be altered without changing its prediction. We provide three possible selection strategies for the proposed model with different computational complexities and apply them on three benchmark data sets. Experimental results demonstrate that the proposed model outperforms the individual classifiers it selects from and is more robust to errors in labels compared to widely adopted approaches
Optimal pilot decisions and flight trajectories in air combat
The thesis concerns the analysis and synthesis of pilot decision-making and the design of optimal flight trajectories. In the synthesis framework, the methodology of influence diagrams is applied for modeling and simulating the maneuvering decision process of the pilot in one-on-one air combat. The influence diagram representations describing the maneuvering decision in a one sided optimization setting and in a game setting are constructed. The synthesis of team decision-making in a multiplayer air combat is tackled by formulating a decision theoretical information prioritization approach based on a value function and interval analysis. It gives the team optimal sequence of tactical data that is transmitted between cooperating air units for improving the situation awareness of the friendly pilots in the best possible way. In the optimal trajectory planning framework, an approach towards the interactive automated solution of deterministic aircraft trajectory optimization problems is presented. It offers design principles for a trajectory optimization software that can be operated automatically by a nonexpert user. In addition, the representation of preferences and uncertainties in trajectory optimization is considered by developing a multistage influence diagram that describes a series of the maneuvering decisions in a one-on-one air combat setting. This influence diagram representation as well as the synthesis elaborations provide seminal ways to treat uncertainties in air combat modeling. The work on influence diagrams can also be seen as the extension of the methodology to dynamically evolving decision situations involving possibly multiple actors with conflicting objectives. From the practical point of view, all the synthesis models can be utilized in decision-making systems of air combat simulators. The information prioritization approach can also be implemented in an onboard data link system.reviewe
Uncertainty in Engineering
This open access book provides an introduction to uncertainty quantification in engineering. Starting with preliminaries on Bayesian statistics and Monte Carlo methods, followed by material on imprecise probabilities, it then focuses on reliability theory and simulation methods for complex systems. The final two chapters discuss various aspects of aerospace engineering, considering stochastic model updating from an imprecise Bayesian perspective, and uncertainty quantification for aerospace flight modelling. Written by experts in the subject, and based on lectures given at the Second Training School of the European Research and Training Network UTOPIAE (Uncertainty Treatment and Optimization in Aerospace Engineering), which took place at Durham University (United Kingdom) from 2 to 6 July 2018, the book offers an essential resource for students as well as scientists and practitioners
Decision Making under Uncertainty through Extending Influence Diagrams with Interval-valued Parameters
Influence Diagrams (IDs) are one of the most commonly used graphical
and mathematical decision models for reasoning under uncertainty. In conventional
IDs, both probabilities representing beliefs and utilities representing preferences of
decision makers are precise point-valued parameters. However, it is usually difficult
or even impossible to directly provide such parameters. In this paper, we extend
conventional IDs to allow IDs with interval-valued parameters (IIDs), and develop a
counterpart method of Copper’s evaluation method to evaluate IIDs. IIDs avoid the
difficulties attached to the specification of precise parameters and provide the
capability to model decision making processes in a situation that the precise
parameters cannot be specified. The counterpart method to Copper’s evaluation
method reduces the evaluation of IIDs into inference problems of IBNs. An algorithm
based on the approximate inference of IBNs is proposed, extensive experiments are
conducted. The experimental results indicate that the proposed algorithm can find the
optimal strategies effectively in IIDs, and the interval-valued expected utilities
obtained by proposed algorithm are contained in those obtained by exact evaluating
algorithms
Large scale emergent properties of an autocatalytic reaction-diffusion model subject to noise
The non-equilibrium dynamic fluctuations of a stochastic version of the
Gray-Scott (GS) model are studied analytically in leading order in perturbation
theory by means of the dynamic renormalization group. There is an attracting
stable fixed point at one-loop order, and the asymptotic scaling of the
correlation functions is predicted for both spatial and temporally correlated
noise sources. New effective three-body reaction terms, not present in the
original GS model, are induced by the combined interplay of the fluctuations
and nonlinearities.Comment: 13 pages, 2 figure
Argumentation as a practical foundation for decision theory
Imperial Users onl
Sequential decision making with adaptive utility
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. The theory of adaptive utility was introduced by Cyert & DeGroot [27], but had since received little attention or development. In particular, foundational issues had not been explored and no consideration had been given to the generalisation of traditional utility concepts such as value of information or risk aversion. This thesis addresses such issues. An in-depth review of the decision theory literature is given, detailing differences in assumptions between various proposed normative theories and their possible generalisations. Motivation is provided for generalising expected utility theory to permit uncertain preferences, and it is argued that in such a situation, under the acceptance of traditional utility axioms, the decision maker should seek to select decisions so asto maximise expected adaptive utility . The possible applications of the theory forsequential decision making are illustrated by some small-scale examples, including examples of relevance within reliability theory
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