6114683
The evidential reasoning approach for MADA under both probabilistic and fuzzy uncertainties
Abstract Many multiple attribute decision analysis (MADA) problems are characterised by both quantitative and qualitative attributes with various types of uncertainties. Incompleteness (or ignorance) and vagueness (or fuzziness) are among the most common uncertainties in decision analysis. The evidential reasoning (ER) approach has been developed in the 1990s and in the recent years to support the solution of MADA problems with ignorance, a kind of probabilistic uncertainty. In this paper, the ER approach is further developed to deal with MADA problems with both probabilistic and fuzzy uncertainties. In this newly developed ER approach, precise data, ignorance and fuzziness are all modelled under the unified framework of a distributed fuzzy belief structure, leading to a fuzzy belief decision matrix. A utility-based grade match method is proposed to transform both numerical data and qualitative (fuzzy) assessment information of various formats into the fuzzy belief structure. A new fuzzy ER algorithm is developed to aggregate multiple attributes using the information contained in the fuzzy belief matrix, resulting in an aggregated fuzzy distributed assessment for each alternative. Different from the existing ER algorithm that is of a recursive nature, the new fuzzy ER algorithm provides an analytical means for combining all attributes without iteration, thus providing scope and flexibility for sensitivity analysis and optimisation. A numerical example is provided to illustrate the detailed implementation process of the new ER approach and its validity and wide applicability.
2868377
Intelligent decision system and its application in business innovation self assessment
In this paper, it is described how a multiple criteria decision analysis software tool, the Intelligent Decision System (IDS), can be used to help business self-assessment. Following a brief outline of a model for assessing business innovation capability and the IDS software, the process of using IDS to implement different types of assessment questions is discussed. It is demonstrated that IDS is a flexible tool capable of handling different types of data in self-assessment, including uncertain and incomplete data, and providing a wide range of information including scores, performance diversity, strength and weakness profile and graphics.
15674460
A two-stage logarithmic goal programming method for generating weights from interval comparison matrices
A two-stage logarithmic goal programming (TLGP) method is proposed to generate weights from interval comparison matrices, which can be either consistent or inconsistent. The first stage is devised to minimize the inconsistency of interval comparison matrices and the second stage is developed to generate priorities under the condition of minimal inconsistency. The weights are assumed to be multiplicative rather than additive. In the case of hierarchical structures, a nonlinear programming method is used to aggregate local interval weights into global interval weights. A simple yet practical preference ranking method is investigated to compare the interval weights of criteria or rank alternatives in a multiplicative aggregation process. The proposed TLGP is also applicable to fuzzy comparison matrices when they are transformed into interval comparison matrices using @a-level sets and the extension principle. Six numerical examples including a group decision analysis problem with a group of comparison matrices, a hierarchical decision problem and a fuzzy decision problem using fuzzy comparison matrix are examined to show the applications of the proposed methods. Comparisons with other existing procedures are made whenever possible.
342298
Nonlinear information aggregation via evidential reasoning in multiattribute decision analysis under uncertainty
In many decision situations, it is inevitable to deal with both quantitative and qualitative information under uncertainty. Evidence-based reasoning within a multiple criteria decision analysis framework provides an alternative way of handling such information systematically and consistently. In this paper, the evidential reasoning (ER) approach is introduced, which is based on a recursive ER algorithm that, in essence, constitutes a nonlinear information aggregation process. To facilitate the application of the ER approach and as an indispensable part of its development, the nonlinear features of the ER information aggregation process need to be thoroughly investigated and properly understood. This forms the theme of this paper where the nonlinear features are explored by examining typical reasoning patterns in aggregating harmonic, quasi-harmonic, and contradictory decision information. This analytical investigation provides insights into the recursive nature of the ER approach as well as valuable experience that could be useful to other researchers and practitioners interested in developing and applying operation research/artificial intelligence (OR/AI)-based approaches for decision analysis under uncertainty. The analytical study is complemented by the numerical studies of two application examples. The analysis of a quality assessment problem for motor engines is aimed to show the step-by-step process of implementing the ER approach and to illustrate its nonlinear features in a real-life decision situation. The study of a more complex assessment problem in ship design is intended to demonstrate the potential of the ER approach and its supporting software for dealing with general decision problems.
14141173
Distributed network simulations using the dynamic simulation backplane
Presents an approach for creating distributed, component-based simulations of communication networks by interconnecting models of sub-networks drawn from different network simulation packages. This approach supports the rapid construction of simulations for large networks by reusing existing models and software, and fast execution using parallel discrete event simulation techniques. A dynamic simulation backplane is proposed that provides a common format and protocol for message exchange, and services for transmitting data and synchronizing heterogeneous network simulation engines. In order to achieve plug-and-play interoperability, the backplane uses existing network communication standards and dynamically negotiates among the participant simulators to define a minimal subset of required information that each simulator must supply, as well as other optional information. The backplane then automatically creates a message format that can be understood by all participating simulators and dynamically creates the content of each message by using callbacks to the simulation engines. We describe our approach to interoperability as well as an implementation of the backplane. We present results that demonstrate the proper operation of the backplane by distributing a network simulation between two different simulation packages, ns2 and GloMoSim. Performance results show that the overhead for the creation of the dynamic messages is minimal. Although this work is specific to network simulations, we believe our methodology and approach can be used to achieve interoperability in other distributed computing applications as well.
11019838
Intelligent Decision Support for Family Financial Planning
In today’s financial markets characterized by constantly changing tax laws and increasingly complex transactions, the demand for family financial planning (FFP) services is rising dramatically. However, the current trend to develop advisory systems that focus mainly on the financial or investment side fails to consider the whole picture of FFP. Separating financial or investment advice from legal and accounting advice may result in conflicting advice or important omissions that could lead to users suffering financial loss. In this paper, we propose a conceptual model for FFP decision-making process, followed by a novel architecture to support an aggregated FFP decision process by utilizing intelligent-agents and Web-services technology. A prototype system for supporting FFP decision is presented to demonstrate the advances of the proposed Web-service multi-agents-based system architecture and business value.
8023010
PROSPECT-PSPP: an automatic computational pipeline for protein structure prediction
Knowledge of the detailed structure of a protein is crucial to our understanding of the biological functions of that protein. The gap between the number of solved protein structures and the number of protein sequences continues to widen rapidly in the post-genomics era due to long and expensive processes for solving structures experimentally. Computational prediction of structures from amino acid sequence has come to play a key role in narrowing the gap and has been successful in providing useful information for the biological research community. We have developed a prediction pipeline, PROSPECT-PSPP, an integration of multiple computational tools, for fully automated protein structure prediction. The pipeline consists of tools for (i) preprocessing of protein sequences, which includes signal peptide prediction, protein type prediction (membrane or soluble) and protein domain partition, (ii) secondary structure prediction, (iii) fold recognition and (iv) atomic structural model generation. The centerpiece of the pipeline is our threading-based program PROSPECT. The pipeline is implemented using SOAP (Simple Object Access Protocol), which makes it easier to share our tools and resources. The pipeline has an easy-to-use user interface and is implemented on a 64-node dual processor Linux cluster. It can be used for genome-scale protein structure prediction. The pipeline is accessible at http://csbl.bmb.uga.edu/protein_pipeline.
9746543
Design an Interoperable Mobile Agent System Based on Predicate Transition Net Models
Mobile agents provide an effective and flexible style to develop advanced distributed systems. In order to promote interoperability and ensure the quality of mobile agent systems, it is necessary to formalize software architecture of mobile agent systems. In this paper, we not only define the software architecture of interoperable mobile agent systems using predicate transition nets, but also analyze the interoperability between agents. In addition, a formal model-driven design method to develop mobile agent systems is described with examples. Our method naturally integrates formal methods and practical approaches in the agent system design phase. The method can be used to develop other complex software systems as well.
2839172
A framework of fuzzy diagnosis
Fault diagnosis has become an important component in intelligent systems, such as intelligent control systems and intelligent elearning systems. Reiter's diagnosis theory, described by first-order sentences, has been attracting much attention in this field. However, descriptions and observations of most real-world situations are related to fuzziness because of the incompleteness and the uncertainty of knowledge, e.g., the fault diagnosis of student behaviors in the elearning processes. In this paper, an extension of Reiter's consistency-based diagnosis methodology, fuzzy diagnosis, has been proposed, which is able to deal with incomplete or fuzzy knowledge. A number of important properties of the fuzzy diagnoses schemes have also been established. The computing of fuzzy diagnoses is mapped to solving a system of inequalities. Some special cases, abstracted from real-world situations, have been discussed. In particular, the fuzzy diagnosis problem, in which fuzzy observations are represented by clause-style fuzzy theories, has been presented and its solving method has also been given. A student fault diagnostic problem abstracted from a simplified real-world elearning case is described to demonstrate the application of our diagnostic framework.