An intelligent decision theoretic approach to producibility optimization in conceptual design

Product optimization involves selecting design, manufacturing, and support attributes that can produce the best system. Producibility or manufacturability is the term often used to describe the relative ease of manufacturing a product. In complex systems, productibility optimization is a very difficult process, particularly when the values of many attributes are restricted by constraints. One challenge is to develop more universal producibility metrics for the conceptual design phase when design information is limited and drawings are nondimensional. This paper develops a new method for producibility optimization in conceptual design based on a combination of both decision theoretic and expert system techniques. Decision theoretic techniques provide the means to model the design for producibility problem in a manner that can deal with risk, uncertainty, and user (or corporate) preferences, and can effectively integrate diverse factors to provide a measure of the overall worth of a design. The particular decision theoretic approach employed is based on multi-attribute utility theory. An illustrative example of the methodology is applied to the conceptual design of a structural composite part.     

A control engineering review of fuzzy systems

Many complex industrial processes cannot be satisfactorily controlled using the results of modern control theory, mainly because their precise structure is unknown. However, this is often balanced by a considerable amount of ‘engineering feel’ for the process which is difficult to quantify and utilise. Fuzzy set theory is a relatively new concept which allows this qualitativeness to be expressed rigorously and in this paper its usefulness for control is assessed. The state of the art is reviewed and reveals a surprising number of practical successes.     

Human reliability and safety evaluation of man-machine systems

The security of a plant is strongly affected by human factors. Though human reliability in a man-machine system has been studied using a probabilistic approach the same as machine components, human behavior is quite different from that of a machine. We make clear some characteristics of human reliability by experiment, and suggest a mathematical model which represents the reliability of man-machine systems by using fuzzy set theory.     

Human system interface concerns in support system design

Current research needs and future prospects in the area of support to man-machine system analysis, design, and evaluation are described. We are especially concerned with system design requirements to enable efficient and effective human system interaction. Prospects for enhanced support to the human operator, in problem solving cognitive tasks that involve planning and design as well as physiological tasks that involve controlling, through use of knowledge based systems and decision support systems, are discussed.     

Multilevel failure detection system

A fuzy multilevel failure detection system is developed for complex processes with ill-known mathematical model and poor measuring accuracy. The decomposition of the problem is made with the aid of fuzzy switches and diagnostic algorithms which are specified by a set of conditional statements. The diagnosis system is demonstrated by a real low density polyethylene reactor in full detail.     

Modeling and analysis of teams of interacting decisionmakers with bounded rationality

A methodology for analyzing and evaluating alternative organizational structures is presented. An information theoretic framework is used in which each team member is described by a two-stage model consisting of situation assessment and response selection stages as well as interconnections with the rest of the organization. The information processing and decisionmaking load of each team member and the measure of organizational performance are depicted in the performance-workload space as implicit functions of the decision strategies of each individual member. The approach to evaluating organizational structures using the methodology for analysis analysis of an organization consisting of two decisionmakers with bounded rationality.     

决策表中规则获取的不确定性研究

知识获取的不确定性主要来源于有限的分辨能力以及对于数据描述的不确定性。首先将Ｒｏｕｇｈ集理论与不确定问题中的证据理论以及模糊集合理论进行比较,然后介绍不确定性数据的模糊描述。通过引入模糊区别矩阵和扩展近似集方法延伸了Ｒｏｕｇｈ集理论,并从模糊决策表中导出合理的决策规则。     

Facilitating the use of multi-attribute utility theory in expert systems:an aid for identifying the right relative importance weights of attributes

In general, expert system applications to real cases involve making decisions, i.e. selecting a suitable action among a set of possible alternative actions. A well-known standard method for modelling decision problems is the so-called multi-attribute utility theory (MAUT), a method in which the alternatives are viewed in terms of their attributes. A set of attributes are identified and a specific value and a suitable relative importance weight are assigned to each attribute. However, it is not easy for the expert to quantify the relative importance weight of an attribute: this assignment entails a certain abstraction activity from the expert and, as is well known, experts have difficulty in providing their knowledge in abstract and general terms. In order to overcome this difficulty we propose a method for automatically inferring relative importance weights from a set of specific action sequences. An action sequence is a list in chronological order of the actions executed by the expert when facing specific cases of decision problems. Providing action sequences requires no other effort but remembering specific episodes, and this task is much easier for experts than having to directly provide precise numbers expressing relative importance weights. Moreover in many cases action sequences are already stored in suitable records. Consider, for example, the list of medical tests executed on a given patient, a list included in the patient clinical record stored in the clinical database of a hospital. On the basis of these considerations the proposed method should be useful for designers of expert systems which face problems of choosing the right action among a set of alternative actions.     

Human decision making & the symbolic search space paradigm in AI

In this paper I shall describe the symbolic search space paradigm which is the dominant model for most of AI. Coupled with the mechanisms of logic it yields the predominant methodology underlying expert systems which are the most successful application of AI technology to date. Human decision making, more precisely, expert human decision making is the function that expert systems aspire to emulate, if not surpass.Expert systems technology has not yet proved to be a decisive success — it appears to fare better in some areas of human expertise than others. As a result subdomains of human expertise are variously categorised and we shall examine a few of the suggested classification schemes. A particular line of argument explored is one which maintains that certain types of human decision making, at least, are not adequately approximated by the symbolic search space paradigm of AI. Furthermore, attempts to project this inadequate model of human decision making via implementations of expert systems will be detrimental to both our image of ourselves and the future possibilities for AI software.Finally, we examine one possible route to the realization of AI, perhaps even practical applications of AI, that is a significant alternative to the model offered by the symbolic search space paradigm.     

A learning fuzzy algorithm for motion planning of mobile robots

A sensor-based fuzzy algorithm is proposed to navigate a mobile robot in a 2-dimensional unknown environment filled with stationary polygonal obstacles. When the robot is at the starting point, vertices of the obstacles that are visible from the robot are scanned by the sensors and the one with the highest priority is chosen. Here, priority is an output fuzzy variable whose value is determined by fuzzy rules. The robot is then navigated from the starting point to the chosen vertex along the line segment connecting these two points. Taking the chosen vertex as the new starting point, the next navigation decision is made. The navigation process will be repeated until the goal point is reached.In implementation of fuzzy rules, the ranges of fuzzy variables are parameters to be determined. In order to evaluate the effect of different range parameters on the navigation algorithm, the total traveling distance of the robot is defined as the performance index first. Then a learning mechanism, which is similar to the simulated annealing method in the neural network theory, is presented to find the optimal range parameters which minimize the performance index. Several simulation examples are included for illustration.     

Sequential decision-theoretic models and expert systems

Sequential decision models are an important component of expert systems since, in general, the cost of acquiring information is significant and there is a trade-off between the cost and the value of information. Many expert systems in various domains (business, engineering, medicine etc.), needing costly inputs that are not known until the system operates, have to face this problem. In the last decade the field of sequential decision models based on decision theory (sequential decision-theoretic models) have become more and more important due to both the continuous progress made by research in Bayesian networks and the availability of modern powerful tools for building Bayesian networks and for probability propagation. This paper provides readers (especially knowledge engineers and expert system designers) with a unified and integrated presentation of the disparate literature in the field of sequential decision-making based on decision theory, in order to improve comprehensibility and accessibility. Besides the presentation of the general theory, a view of sequential diagnosis as an instance of the general concept of sequential decision-theoretic models is also shown.     

A feature construction approach for genetic iterative rule learning algorithm

This paper presents a proposal that introduces the use of feature construction in a fuzzy rule learning algorithm. This is done by means of the combination of two different approaches together with a new learning strategy. The first of these two approaches consists of using relations in the antecedent of fuzzy rules while the second one employs functions in the antecedent of that rules. Thus, the method we propose tries to integrate these two models so that, using a learning strategy that allows us to start learning more general rules and finish the process learning more specific ones, we are able to increase the amount of information extracted from the initial variables. The experimental results show that the proposed method obtains a good trade-off among accuracy, interpretability and time needed to get the model in relation to the rest of algorithms using feature construction involved in the comparison.     

Model of the human observer and decision maker—Theory and validation

A model of the human decision maker observing a dynamic system is presented. The decision process is described in terms of classical sequential decision theory by considering the hypothesis that an abnormal condition has occurred by means of a generalized likelihood ratio test. For this, a sufficient statistic is provided by the innovation sequence which is the result of the perception and information processing submodel of the human observer. On the basis of only two model parameters the model predicts the decision speed/accuracy trade-off and various attentional characteristics. A preliminary test of the model for single variable failure detection tasks resulted in a very good fit of the experimental data. In a formal validation programme a variety of multivariable failure detection tasks was investigated. A very good overall agreement between the model and experimental results showed the predictive capability of the model. In addition, the specific effect of almost all task variables (number, bandwidth and mutual correlation of display variables and various failure characteristics) was accurately predicted by the model.     

GP-COACH: Genetic Programming-based learning of COmpact and ACcurate fuzzy rule-based classification systems for High-dimensional problems

In this paper we propose GP-COACH, a Genetic Programming-based method for the learning of COmpact and ACcurate fuzzy rule-based classification systems for High-dimensional problems. GP-COACH learns disjunctive normal form rules (generated by means of a context-free grammar) coded as one rule per tree. The population constitutes the rule base, so it is a genetic cooperative-competitive learning approach. GP-COACH uses a token competition mechanism to maintain the diversity of the population and this obliges the rules to compete and cooperate among themselves and allows the obtaining of a compact set of fuzzy rules. The results obtained have been validated by the use of non-parametric statistical tests, showing a good performance in terms of accuracy and interpretability.     

模糊专家理论在炮兵指挥控制系统中的运用

针对炮兵指挥控制系统在作战训练中遇到的问题,在科学地分析了影响效果和相关因素的基础上,依据模糊专家理论的方法和原理,设计了相关的运用决策框架,并提出了运用规则。     

D-S证据理论在航迹关联问题中的应用

基于模糊综合决策的思想,对传统的加权方法进行基于物理意义上的改进,设计新的加权准则,利用D-S证据理论提出一种新的分布式航迹关联算法,通过仿真进行分析,并与模糊综合决策方法进行了特定的比较,结果表明:基于证据理论的航迹关联准则,能够达到比较满意的关联效果,体现了证据理论在解决不确定性问题上的优良特性。     

Hybrid artificial intelligence approach to urban planning

Knowledge-based modeling and implementation of the various urban planning processes represent an intensive research area. This paper presents a hybrid artificial intelligence system using a knowledge-based approach, neural networks and fuzzy logic that automates the decision-making process in urban planning. The system is used for developing urban development alternatives based on real-world data. Results show that, by integrating knowledge-based systems, artificial neural networks and fuzzy systems, the system achieves improvements in the implementation of each respective system as well as an increase in the breadth of functionality within the application. With this approach, the best of three technologies can be compiled together to solve complex urban problems. We discuss the structure of the combined technologies, as well as providing examples of its application in the field of urban development.