To address the issue of decision support for designing and managing flexible projects and systems in the face of uncertainties, this paper integrates real options valuation, decision analysis techniques, Monte Carlo simulations and evolutionary algorithms in an evolutionary real options framework. The proposed evolutionary real options framework searches for an optimized portfolio of real options and makes adaptive plans to cope with uncertainties as the future unfolds. Exemplified through a test case, the evolutionary framework not only compares favorably with traditional fixed design approaches but also delivers considerable improvements over prevailing real options practices. 相似文献
This paper develops an automated negotiation procedure inclusive of mechanism design and agent design for bilateral multi-issue negotiations under two-sided information uncertainty. The proposed negotiation mechanism comprises a protocol called MUP (Monotonic Utility-granting Protocol) and a matching strategy called WYDIWYG (What You Display Influences What You Get). The proposed preference elicitation procedure makes the agents faithful surrogates of the user they represent while the proposed Frontier Tracking Proposal Construction Algorithm (FTPCA) makes them learn the opponent's flexibility in negotiation and respond appropriately. The mechanism design and the agent design together help in locating efficient and equitable deals quickly. The efficiency, stability, simplicity, distribution symmetry and incentive compatibility of the proposed procedure are demonstrated through negotiation simulation experiments. 相似文献
This paper proposes an adaptive robust fuzzy control scheme for path tracking of a wheeled mobile robot with uncertainties.
The robot dynamics including the actuator dynamics is considered in this work. The presented controller is composed of a fuzzy
basis function network (FBFN) to approximate an unknown nonlinear function of the robot complete dynamics, an adaptive robust
input to overcome the uncertainties, and a stabilizing control input. The stability and the convergence of the tracking errors
are guaranteed using the Lyapunov stability theory. When the controller is designed, the different parameters for two actuator
models in the dynamic equation are taken into account. The proposed control scheme does not require the accurate parameter
values for the actuator parameters as well as the robot parameters. The validity and robustness of the proposed control scheme
are demonstrated through computer simulations.
This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January
31–February 2, 2008 相似文献
This paper addresses the problem of estimating the 3D trajectory and associated uncertainty of an underwater autonomous vehicle from a set of images of the seabed taken by an onboard camera. The presented algorithms resort to the use of video mosaics and build upon previous work on image registration and visual pose estimation. The pose estimation is accomplished in two steps. Firstly, a video mosaic is created automatically, covering a region of interest of the seabed. Then, after associating a 3D referential for the mosaic, the estimation of the camera position from a new view of the scene becomes possible.
The main contribution of this paper lies on the assessment of the performance of the 3D pose algorithms. In order to do this, an image sequence with available ground-truth is used for precise error measuring. A first-order error propagation analysis is presented, relating the uncertainty in the location of the match points with the uncertainty in the pose parameters. The importance of predicting the estimate uncertainty is emphasized by the fact that it can be used for comparing algorithms and for the on-line monitoring of the vehicle trajectory reconstruction quality.
Several iterative and non-iterative pose estimation methods are discussed, differing both on the criteria being minimized and on the required information about the camera intrinsic parameters. This information ranges from the full knowledge of the parameters, to the case where they are estimated using self-calibration from an image sequence under pure rotation. The implemented pose algorithms are compared for the accuracy and estimate covariance. 相似文献
This paper considers the problem of computing an optimal policy for a Markov decision process, under lack of complete a priori
knowledge of (1) the branching probability distributions determining the evolution of the process state upon the execution
of the different actions, and (2) the probability distributions characterizing the immediate rewards returned by the environment
as a result of the execution of these actions at different states of the process. In addition, it is assumed that the underlying
process evolves in a repetitive, episodic manner, with each episode starting from a well-defined initial state and evolving
over an acyclic state space. A novel efficient algorithm for this problem is proposed, and its convergence properties and
computational complexity are rigorously characterized in the formal framework of computational learning theory. Furthermore,
in the process of deriving the aforementioned results, the presented work generalizes Bechhofer’s “indifference-zone” approach
for the ranking & selection problem, that arises in statistical inference theory, so that it applies to populations with bounded
general distributions.
Knowledge-base V&V primarily addresses the question: “Does my knowledge-base contain the right answer and can I arrive at it?” One of the main goals of our work is to properly encapsulate the knowledge representation and allow the expert to work with manageable-sized chunks of the knowledge-base. This work develops a new methodology for the verification and validation of Bayesian knowledge-bases that assists in constructing and testing such knowledge-bases. Assistance takes the form of ensuring that the knowledge is syntactically correct, correcting “imperfect” knowledge, and also identifying when the current knowledge-base is insufficient as well as suggesting ways to resolve this insufficiency. The basis of our approach is the use of probabilistic network models of knowledge. This provides a framework for formally defining and working on the problems of uncertainty in the knowledge-base.
In this paper, we examine the
project which is concerned with assisting a human expert to build knowledge-based systems under uncertainty. We focus on how verification and validation are currently achieved in
. 相似文献
Indicator Kriging (IK) is a geostatistical method that uses observation points to quantify the probabilities at which a set of thresholds are exceeded at unmeasured points. To improve IK accuracy, the interpolation process should consider its uncertainty sources. By doing this, we also maintain its ability to provide the conditional cumulative distribution function (ccdf), which is a reliable measure of local uncertainty. This study modeled two IK uncertainty sources, i.e., measurement errors attached to observation points and subjective threshold choices. Soft Indicator Kriging (SIK), which uses a soft transformation for observation points, considers the measurement errors of these two sources. To select the thresholds objectively, a genetic algorithm (GA) was performed to obtain the optimum set of thresholds related to an objective function, which minimized the mean absolute error (MAE). 相似文献