A medical decision support system for disease diagnosis under uncertainty

This paper presents a decision support system (DSS) modeled by a fuzzy expert system (FES) for medical diagnosis to help physicians make better decisions. The proposed system collects comprehensive information about a disease from a group of experts. To this aim, a cross-sectional study is conducted by asking physicians’ expertise on all symptoms relevant to a disease. A fuzzy rule based system is then formed based on this information, which contains a set of significant symptoms relevant to the suspected disease. Linguistic fuzzy values are assigned to model each symptom. The input of the system is the severity level of each symptom reported by patients. The proposed FES considers two approaches to account for uncertain inputs from patients. Two case studies on kidney stone and kidney infection were conducted to demonstrate how the proposed method could be used. A group of patients were used to validate the effectiveness of the proposed expert system. The results show that the proposed fuzzy expert system is capable of diagnosing diseases with a high degree of accuracy and precision comparing to a couple of machine learning methods.     

Fuzzy modeling for intelligent decision making under uncertainty

We consider here the problem of decision making under uncertainty. We suggest an approach for the construction of decision functions which allow for the inclusion of probabilistic information as well as for the inclusion of information about the decision maker's attitude and preferences. Use is made of the fuzzy modeling technology to construct these functions from specifications provided by the decision maker.     

Multicriteria decision making under uncertainty: a visual approach

Decision problems at the strategic level tend to have multiple criteria and outcomes that are uncertain. Many of the current decision‐making tools are too simplistic to incorporate the important features. This paper considers a multicriteria decision‐making scenario in which the outcomes of the decisions, evaluated on different criteria, are uncertain. The main contribution of this paper is the presentation of a tool that enables decision makers to visualize the expected payoff and likelihood that the payoff of a decision does not fall short of a preset target value. Furthermore, it presents decision makers with a tool that shows the tradeoff between expected payoff and downside risk. A variety of solution techniques are suggested that build upon this visualization.     

Relating decision under uncertainty and multicriteria decision making models

This short overview paper points out the striking similarity between decision under uncertainty and multicriteria decision making problems, two areas which have been developed in an almost completely independent way until now. This pertains both to additive and non‐additive (including qualitative) approaches existing for the two decision paradigms. This leads to an emphasis on the remarkable formal equivalence between postulates underlying these approaches (like between the “sure‐thing principle” and mutual preferential independence of criteria). This analogy is exploited by surveying classical results as well as very recent advances. This unified view should be fruitful for a better understanding of the postulates underlying the approaches, for cross‐fertilization, and for adapting artificial intelligence uncertainty representation frameworks to preference modelling. © 2000 John Wiley & Sons, Inc.     

Formal models and algorithms for decentralized decision making under uncertainty

Over the last 5 years, the AI community has shown considerable interest in decentralized control of multiple decision makers or “agents” under uncertainty. This problem arises in many application domains, such as multi-robot coordination, manufacturing, information gathering, and load balancing. Such problems must be treated as decentralized decision problems because each agent may have different partial information about the other agents and about the state of the world. It has been shown that these problems are significantly harder than their centralized counterparts, requiring new formal models and algorithms to be developed. Rapid progress in recent years has produced a number of different frameworks, complexity results, and planning algorithms. The objectives of this paper are to provide a comprehensive overview of these results, to compare and contrast the existing frameworks, and to provide a deeper understanding of their relationships with one another, their strengths, and their weaknesses. While we focus on cooperative systems, we do point out important connections with game-theoretic approaches. We analyze five different formal frameworks, three different optimal algorithms, as well as a series of approximation techniques. The paper provides interesting insights into the structure of decentralized problems, the expressiveness of the various models, and the relative advantages and limitations of the different solution techniques. A better understanding of these issues will facilitate further progress in the field and help resolve several open problems that we identify. This work was done while S. Seuken was a graduate student in the Computer Science Department of the University of Massachusetts, Amherst.     

Decision support for foreign investment strategy under hybrid uncertainty

Empirical evidences show that Japan-based companies moved their major operations to the USA due to the currency appreciation of Japanese Yen in 1980s. However, the multinational firms moving their operations abroad still face both the risk of foreign price and the risk of foreign exchange rate. According to the purchasing power parity (PPP) and interest rate parity (IRP), the foreign exchange rate has associations with the relative price and the relative interest rate between two countries. Therefore, when the price and the interest rate evolve stochastically as proposed by many scholars, we can anticipate the randomness of the foreign exchange rate. By integrating these three sources of risks as a hybrid uncertainty, we propose a framework for corporate valuation and investment strategy. We analytically derive corporate value for those multinationals in question and then numerically obtain the real option value by Monte Carlo simulations, based on which we investigate the optimal entry strategy. The sensitivity for corporate value, optimal entry time, and real option value are analyzed. The results suggest a decision support process for foreign investment timing strategy under the hybrid uncertainty. The managerial implication of this work is that the optimal investment strategy for the multinational firms with foreign operations depends on some market and risk factors, as well as correlations among them.     

Decision making and uncertainty management in a 3D reconstruction system

This paper presents a control structure for a general-purpose image understanding system. It addresses the high level of uncertainty in local hypotheses and the computational complexity of image interpretation. The control of vision algorithms is done by an independent subsystem that uses Bayesian networks and utility theory to compute marginal value of information and selects the algorithm with the highest value of information. It is shown that the knowledge base can be acquired using learning techniques and the value-driven approach to the selection of vision algorithms leads to performance gains.     

An optimization-model-based interactive decision support system for regional energy management systems planning under uncertainty

In this study, an interactive decision support system (UREM-IDSS) has been developed based on an inexact optimization model (UREM, University of Regina Energy Model) to aid decision makers in planning energy management systems. Optimization modeling, scenario development, user interaction, policy analysis and visual display are seamlessly integrated into the UREM-IDSS. Uncertainties in energy-related parameters are effectively addressed through the interval linear programming (ILP) approach, improving the robustness of the UREM-IDSS for real-world applications. Thus, it can be used as an efficient tool for analyzing and visualizing impacts of energy and environmental policies, regional/community sustainable development strategies, emission reduction measures and climate change in an interactive, flexible and dynamic context. The Region of Waterloo has been selected to demonstrate the applicability and capability of the UREM-IDSS. A variety of scenarios (including a reference case) have been identified based on different energy management policies and sustainable development strategies for in-depth analysis of interactions existing among energy, socio-economy, and environment in the Region. Useful solutions for the planning of energy management systems have been generated, reflecting complex tradeoffs among energy-related, environmental and economic considerations. Results indicate that the UREM-IDSS can be successfully used for evaluating and analyzing not only the effects of an individual policy scenario, but also the variations between different scenarios compared with a reference case. Also, the UREM-IDSS can help tackle dynamic and interactive characteristics of the energy management system in the Region of Waterloo, and can address issues concerning cost-effective allocation of energy resources and services. Thus, it can be used by decision makers as an effective technique in examining and visualizing impacts of energy and environmental policies, regional/community development strategies, emission reduction measures, and climate change within an integrated and dynamic framework.     

A family of dominance rules for multiattribute decision making under uncertainty

Multiattribute decision-making involves choosing from a set of alternatives each of which is evaluated along multiple criteria that reflect the dimensions of interest to the goals and values of the decision-maker. Dominance-based decision-making narrows down the focus of the decision to the Pareto optimal set. The elimination of dominated alternatives is a compelling principle of rationality since each dominated alternative is logically inferior to its dominating alternative, given the criteria of evaluation. One kind of uncertainty in multiattribute decision making arises out of noisy or inaccurate criteria evaluations. The application of the principle of dominance is not quite rational if the criteria evaluations are known to be noisy. In this paper, we see how dominance-based decision-making can be applied to multiattribute decision-making problems with uncertainty due to noisy criteria values. In particular it will be shown that, for bounded uncertainty it is possible to produce the smallest sufficient subset that is guaranteed to contain all of the nondominated alternatives, and the largest necessary subset that contains only nondominated alternatives. For unbounded uncertainty, we will see how these notions of sufficiency and necessity can be adapted to varying degrees of probabilistic assurances desired by the decision-maker, and that the varying degrees of user assurance map naturally to a family of dominance rules.     

An approach to fuzzy multiattribute decision making under uncertainty

It seems that there is little investigation on fuzzy multiattribute decision making (FMADM) problems under uncertainty, which are of important to scientific researches and real life applications. FMADM problems under uncertainty are investigated in this paper. Novel mathematical programming models are constructed for FMADM problems under uncertainty, and corresponding solving methods are proposed. The approach proposed in this paper may reflect both subjective judgment and objective information. Moreover, pairwise chain comparison methods for determination of relative membership degrees and weights are also proposed. Feasibility and effectiveness of the models and approach proposed in this paper are illustrated with a numerical example.     

Simulating engineering systems under uncertainty and optimal decision making

A new technology (technique) that helps construct a mathematical model of a complex engineering system by optimal decision making based on it is given. To construct the model of an engineering system, methods of regressive analysis are used to transform the initial (experimental) data into a vector (multiobjective) mathematical programming problem. To solve it, methods are presented that rely on criteria normalization and principle of guaranteed result. The technique of constructing models of engineering systems, methods of solving the vector mathematical programming problem and optimal decision making are demonstrated by the test examples in Matlab.     

Context-aware decision making under uncertainty for voice-based control of smart home

This paper presents a framework to build home automation systems reactive to voice for improved comfort and autonomy at home. The focus of this paper is on the context-aware decision process which must reason from uncertain facts inferred from real sensor data. This framework for building context aware systems uses a hierarchical knowledge model so that different inference modules can communicate and reason with same concepts and relations. The context-aware decision module is based on a Markov Logic Network, a recent approach which make it possible to benefit from formal logical representation and to model uncertainty of this knowledge. In this work, uncertainty of the decision model has been learned from data. Although some expert systems are able to deal with uncertainty, the Markov Logic Network approach brings a unified theory for dealing with logical entailment, uncertainty and missing data. Moreover, the ability to use a priori knowledge and to learn weights and structure from data make this model appealing to address the challenge of adaptation of expert systems to new applications. Finally, the framework has been implemented in an on-line system which has been evaluated in a real smart home with real naive users. Results of the experiment show the interest of context-aware decision making and the advantages of a statistical relational model for the framework.     

Model-based multi-objective decision making under deep uncertainty from a multi-method design lens

Several approaches within the exploratory modelling literature—each with strengths and limitations—have been introduced to address the complexity and uncertainty of decision problems. Recent model-based approaches for decision making emphasise the advantage of mixing approaches from different areas in leveraging the strengths of each. This article shows how a multi-method lens to the design of decision-making approaches can better address different characteristics of multi-objective decision problems under deep uncertainty. The article focuses on interactions between two broad areas in model-based decision making: exploratory modelling and multi-objective optimisation. The article reviews this literature using a specific multi-method lens to analyse previous researches and to identify the knowledge gap. The article then addresses this gap by demonstrating a multi-method approach for designing adaptive robust solutions. The suggested approach uses a Pareto optimal search from multi-objective optimisation for enumerating alternative solutions. It also uses Robust Decision Making and Dynamic Adaptive Policy Pathways approaches from exploratory modelling for analysing the robustness of enumerated solutions in the face of many future scenarios. A hypothetical case study is used to illustrate how the approach can be applied. The article concludes that a new lens from a multi-method design perspective is needed on exploratory modelling to provide practical guidance into how to combine exploratory modelling techniques, to shed light on exiting knowledge gaps and to open up a range of potential combinations of exiting approaches for leveraging the strengths of each.     

Multiple-attribute decision making under uncertainty: the evidential reasoning approach revisited

In multiple-attribute decision making (MADM) problems, one often needs to deal with decision information with uncertainty. During the last decade, Yang and Singh (1994) have proposed and developed an evidential reasoning (ER) approach to deal with such MADM problems. Essentially, this approach is based on an evaluation analysis model and Dempster's rule of combination in the Dempster-Shafer (D-S) theory of evidence. This paper reanalyzes the ER approach explicitly in terms of D-S theory and then proposes a general scheme of attribute aggregation in MADM under uncertainty. In the spirit of such a reanalysis, previous ER algorithms are reviewed and two other aggregation schemes are discussed. Theoretically, it is shown that new aggregation schemes also satisfy the synthesis axioms, which have been recently proposed by Yang and Xu (2002) for which any rational aggregation process should grant. A numerical example traditionally examined in published sources on the ER approach is used to illustrate the discussed techniques.     

On the methods of decision making under uncertainty with probability information

By considering the decision maker's attitude of profit and risk, we propose an alternative selection method that can include the methods of decision making under ignorance and decision making under risk as special cases. An index to measure the decision maker's risk‐averse degree is proposed. With a given optimistic level of profit and risk, the evaluation results of the alternatives can be obtained with a geometric ordered weighted average (OWA) operator and a basic defuzzification distribution (BADD) neat OWA operator. Some properties of these two kinds of OWA operator in the problem of decision making under uncertainty are also proposed. © 2004 Wiley Periodicals, Inc. Int J Int Syst 19: 1217–1238, 2004.     

The use of ordered weighted averaging method for decision making under uncertainty

A decision making under uncertainty (DMUU) prevails at the outset and often evolves into a decision making under partial uncertainty as information on the states of nature, for example, a probability distribution, is advanced. Many methods have emerged for solving the DMUU problems, which includes the classical decision criteria and the domain criterion. Yager (1988) introduced a new approach, the so‐called ordered weighted averaging (OWA) as a viable method for solving the DMUU problems. The OWA weights to be used in the aggregation are generated under the degree of optimism provided by a decision maker and then combined with the reordered payoffs to produce aggregated payoffs for each strategy. The reordering process, one of the characterizing features of the OWA method, enables us to perform various types of aggregations including maximax, maximin, and Hurwicz‐α index in conjunction with the generated weights. The OWA method obviously extends the Hurwicz approach by taking into account the tradeoffs among the entire payoffs while the Hurwicz approach considers a tradeoff only between the two extremes, the maximum and the minimum payoffs. In this paper, we examine the features of the OWA method in light of Milnor's set of requirements for reasonable decision criteria, thus providing a solid methodological foundation for the DMUU. The OWA method can also be used to solve a group DMUU problem by exploiting individual decision results in the situation when the use of a fuzzy majority is advocated.     

基于扩展原理的混合型证据推理不确定决策方法

提出一种基于扩展原理的混合证据推理不确定决策模型.通过α截集将同一决策问题中各属性使用的精确数、区间数和模糊数等异构评估信度统一分解为区间结构,采用区间证据推理方法求解各隶属度下的效用区间,并按隶属度次序重组方案效用;化简模糊数质心公式,并用于模糊定量评估的信度计算和方案模糊效用的排序;最后,通过具体实例验证了所提出方法的有效性和可行性.将该方法在算例中的适用情况进行比较和分析,结果表明所提出的方法具有良好的适应性.     

Consensus-based decision support for multicriteria group decision making

Consensus decision making is complex and challenging in multicriteria group decision making due to the involvement of several decision makers, the presence of multiple, and often conflicting criteria, and the existence of subjectiveness and imprecision in the decision making process. To ensure effective decisions being made, the interest of all the decision makers usually represented by the degree of consensus in the decision making process has to be adequately considered. This paper presents a consensus-based approach for effectively solving the multicriteria group decision making problem. The subjectiveness and imprecision of the decision making process is adequately handled by using intuitionistic fuzzy numbers. An interactive algorithm is developed for consensus building in the group decision making process. A decision support system framework is presented for improving the effectiveness of the consensus building process. An example is presented for demonstrating the applicability of the proposed approach for solving the multicriteria group decision making problem in real world situations.