Local multi-valued logics in modular expert systems

Abstract

Abstract. In this paper we describe an approach to the problem of dealing with uncertainty by means of finite multi-valued logics in modular expert systems, and the results obtained. The modularity of the systems allows us to address two main characteristics of human problem-solving: the adaptation of general knowledge to particular problems and the dependency of the management of uncertainty on the different subtasks being implemented in the modules of the system, i.e. different modules can have different local multiple-valued logics as part of their local deductive mechanisms. Although the results obtained are general, we use, throughout the paper, examples of a medical expert system that has been designed using a modular language called MILORD-II, that implements them showing the practical interest of the theoretical concepts involved.     

A distributed expert control system for a hydrometallurgical zinc process

A distributed expert control system (DECSHZ) has been built for a hydrometallurgical zinc process, whose basic steps are leaching, purification and electrolysis. It consists of a central computer management system and three local expert control systems, one for each of the basic steps. This paper deals with the design and application of DECSHZ, especially its distributed architecture and main functions; expert control strategies based on rule models and a combination of rule models and steady-state mathematical models; system implementation; and the results of actual runs. DECSHZ has been found to provide not only a very pure product, but also significant economic benefits.     

A reasoning method for a ship design expert system

Abstract: The ship design process is a highly data‐oriented, dynamic, iterative and multi‐stage algorithm. It utilizes multiple abstraction levels and concurrent engineering techniques. Specialized techniques for knowledge acquisition, knowledge representation and reasoning must be developed to solve these problems for a ship design expert system. Consequently, very few attempts have been made to model the ship design process using an expert system approach. The current work investigates a knowledge representation–reasoning technique for such a purpose. A knowledge‐based conceptual design was developed by utilizing a prototype approach and hierarchical decompositioning. An expert system program called ALDES (accommodation layout design expert system) was developed by using the CLIPS expert system shell and an object‐oriented user interface. The reasoning and knowledge representation methods of ALDES are explained in the paper. An application of the method is given for the general arrangement design of a containership.     

Robust distributed model predictive control of linear systems with structured time-varying uncertainties

In this work, synthesis of robust distributed model predictive control (MPC) is presented for a class of linear systems subject to structured time-varying uncertainties. By decomposing a global system into smaller dimensional subsystems, a set of distributed MPC controllers, instead of a centralised controller, are designed. To ensure the robust stability of the closed-loop system with respect to model uncertainties, distributed state feedback laws are obtained by solving a min–max optimisation problem. The design of robust distributed MPC is then transformed into solving a minimisation optimisation problem with linear matrix inequality constraints. An iterative online algorithm with adjustable maximum iteration is proposed to coordinate the distributed controllers to achieve a global performance. The simulation results show the effectiveness of the proposed robust distributed MPC algorithm.     

Variable structure sliding mode control for a class of uncertain distributed parameter systems with time-varying delays

This article considers sliding mode control of a class of parabolic linear uncertain distributed parameter systems with time-varying delays. The sliding mode controller design does not contain time delay terms and drives the state trajectory of the system to the sliding manifold in finite time. A sufficient condition of asymptotic stability for the sliding motion is derived. A simulation example is presented to illustrate effectiveness of the proposed method.     

An online expert system for fault diagnosis in hydraulic systems

Fault diagnosis has become increasingly important for industrial automation and many approaches have been investigated for the online diagnostic task. This work demonstrates a new online expert system for dynamic industrial automated processes. The emphasis of this diagnostic system lies in the functions provided for fault detection, prediction, compensation and diagnosis. The system uses experiential knowledge cooperatively with scientific knowledge in a new interactive formation. It is developed in two parts using the DASYLab software for the numerical computations and the KPWin development tool for the symbolic representations. The system was developed in collaboration with the company Automation Systems S.A., which specializes in hydraulic systems, and is used for fault detection in production machines.     

Influence-based model decomposition for reasoning about spatially distributed physical systems

Many important science and engineering applications, such as regulating the temperature distribution over a semiconductor wafer and controlling the noise from a photocopy machine, require interpreting distributed data and designing decentralized controllers for spatially distributed systems. Developing effective computational techniques for representing and reasoning about these systems, which are usually modeled with partial differential equations (PDEs), is one of the major challenge problems for qualitative and spatial reasoning research.

This paper introduces a novel approach to decentralized control design, influence-based model decomposition, and applies it in the context of thermal regulation. Influence-based model decomposition uses a decentralized model, called an influence graph, as a key data abstraction representing influences of controls on distributed physical fields. It serves as the basis for novel algorithms for control placement and parameter design for distributed systems with large numbers of coupled variables. These algorithms exploit physical knowledge of locality, linear superposability, and continuity, encapsulated in influence graphs representing dependencies of field nodes on control nodes. The control placement design algorithms utilize influence graphs to decompose a problem domain so as to decouple the resulting regions. The decentralized control parameter optimization algorithms utilize influence graphs to efficiently evaluate thermal fields and to explicitly trade off computation, communication, and control quality. By leveraging the physical knowledge encapsulated in influence graphs, these control design algorithms are more efficient than standard techniques, and produce designs explainable in terms of problem structures.     

具有状态和测量时滞不确定系统的鲁棒H∞状态估计

考虑一类已知状态和测量时滞且范数有界参数不确定连续时间系统的鲁棒H∞状态估计问题.这个问题解的充分条件由二个代数Riccati不等式给出,它可以保证存在一个渐近稳定状态估计器使得对于所有不确定性从外界干扰到输出估计误差的传递函数满足指定的H∞指标.以上这些结果可以推广到一类未知状态和测量时滞且范数有界参数不确定连续系统的鲁棒H∞状态估计问题,对于已知状态和测量时滞系统,所得状态估计器与参数不确定性无关,而与时滞有关.对于未知状态和测量时滞系统,其状态估计器不仅与参数不确定性无关,而且与时滞也无关.     

Hybrid expert systems: A survey of current approaches and applications

This paper is a statistical analysis of hybrid expert system approaches and their applications but more specifically connectionist and neuro-fuzzy system oriented articles are considered. The current survey of hybrid expert systems is based on the classification of articles from 1988 to 2010. Present analysis includes 91 articles from related academic journals, conference proceedings and literature reviews. Our results show an increase in the number of recent publications which is an indication of gaining popularity on the part of hybrid expert systems. This increase in the articles is mainly in neuro-fuzzy and rough neural expert systems’ areas. We also observe that many new industrial applications are developed using hybrid expert systems recently.     

State estimation for a class of discrete nonlinear systems with randomly occurring uncertainties and distributed sensor delays

In this paper, the state estimation problem is investigated for a class of discrete nonlinear systems with randomly occurring uncertainties and distributed sensor delays. The norm-bounded uncertainties enter into the system in a randomly way, and such randomly occurring uncertainties (ROUs) obey certain Bernoulli distributed white noise sequence with known conditional probability. By constructing a new Lyapunov–Krasovskii functional, sufficient conditions are proposed to guarantee the convergence of the estimation error for all discrete time-varying delays, ROUs and distributed sensor delays. Subsequently, the explicit form of the estimator parameter is derived by solving two linear matrix inequalities (LMIs) which can be easily tested by using standard numerical software. Finally, a simulation example is given to illustrate the feasibility and effectiveness of the proposed estimation scheme.     

离散不确定系统分布式鲁棒融合滤波器设计

针对一类离散多传感器动态模型的不确定系统,将鲁棒滤波理论与数据融合技术相结合,基于参数依赖Lyapunov函数,研究该离散系统的鲁棒融合滤波器设计问题。在集中式鲁棒融合滤波器的基础上,探讨了分布式加权融合滤波器的设计方法,通过仿真实验比较了鲁棒融合滤波器的性能。结果表明,利用该分布式加权融合算法,不仅对于解决当系统模型中存在参数不确定性时的滤波问题有较好的鲁棒性能和较低的计算量,而且在多传感器系统中对于满足不同精度鲁棒融合滤波器的设计需要具有较大的灵活性。     

带有无穷分布时滞的不确定系统的鲁棒H_∞滤波器设计

针对一类在系统的状态方程与可测输出中都包含有非线性无穷分布时滞的参数不确定系统,提出一种新颖的鲁棒H∞滤波器的设计方法.设计的鲁棒H∞滤波器可以保证对于带有时变且范数有界的参数不确定性的滤波误差系统是渐近稳定的,并且满足所给定的H∞性能指标.鲁棒H∞滤波器可以通过线性矩阵不等式方法获得.通过一个数值的例子验证了该方法的有效性.     

卫星系统作战效能评估辅助DSS研究

卫星系统作战效能评估是开发、使用和优化卫星系统的基础。从系统的角度探讨了卫星系统效能评估的方法论，建立并讨论了包括效能度量维、系统建模维和效能评估方法维的系统效能评估三维结构,在此基础上，提出了卫星系统作战效能评估辅助DSS的概念和结构，构建了卫星系统作战效能评估辅助DSS的模型体系，为科学、有效地进行卫星系统作战效能评估提出了新的途径，最后指出了深化效能评估的几个关键问题。     

Knowledge acquisition for expert systems in accounting and financial problem domains

Since the mid-1980s, expert systems have been developed for a variety of problems in accounting and finance. The most commonly cited problems in developing these systems are the unavailability of the experts and knowledge engineers and difficulties with the rule extraction process. Within the field of artificial intelligence, this has been called the ‘knowledge acquisition’ (KA) problem and has been identified as a major bottleneck in the expert system development process. Recent empirical research reveals that certain KA techniques are significantly more efficient than others in helping to extract certain types of knowledge within specific problem domains. This paper presents a mapping between these empirical studies and a generic taxonomy of expert system problem domains. To accomplish this, we first examine the range of problem domains and suggest a mapping of accounting and finance tasks to a generic problem domain taxonomy. We then identify and describe the most prominent KA techniques employed in developing expert systems in accounting and finance. After examining and summarizing the existing empirical KA work, we conclude by showing how the empirical KA research in the various problem domains can be used to provide guidance to developers of expert systems in the fields of accounting and finance.     

Output feedback variable structure control for linear systems with uncertainties and disturbances

This paper proposes a dynamic output feedback variable structure controller for linear MIMO systems with mismatched and matched norm-bounded uncertainties and matched nonlinear disturbances. The proposed controller consists of nonlinear and linear parts, similar to the unit vector type of controller. The nonlinear part of the new controller takes care only of matched uncertainties and disturbances and, on the other hand, the linear part with full dynamics completely handles mismatched uncertainties. Designing such a linear part leads to use a Lyapunov function associated with the full states, which achieves the global stability against the mismatched uncertainties. The resulting criteria are, furthermore, converted into solvable ones, by using the so-called cone complementary linearization algorithm for bi-convex problems.     

具有状态和测量时滞不确定系统的鲁棒H∞状态估计

考虑一类已知状态和测量时滞且范数有界参数不确定连续时间系统的鲁棒H_∞状态估计问题. 这个问题解的充分条件由二个代数Riccati不等式给出, 它可以保证存在一个渐近稳定状态估计器使得对于所有不确定性从外界干扰到输出估计误差的传递函数满足指定的H_∞指标. 以上这些结果可以推广到一类未知状态和测量时滞且范数有界参数不确定连续系统的鲁棒H_∞状态估计问题, 对于已知状态和测量时滞系统, 所得状态估计器与参数不确定性无关, 而与时滞有关. 对于未知状态和测量时滞系统, 其状态估计器不仅与参数不确定性无关, 而且与时滞也无关.     

Evaluation of expert systems for automatic shape recognition by ultrasound

This paper presents an evaluation of several types of expert systems in automatic object recognition for robotic manipulators, using ultrasound. In fact, rule-based expert systems and probabilistic expert systems have been compared in a calculation of prismatic body orientation and object shape recognition. Furthermore, both types of expert systems were used to distinguish different piece shapes and to detect object position in a real scenario using a robotic manipulator. Information for the automatic recognition system is provided to the expert system by means of the ultrasonic signal coming back from the illuminated object, which is captured by only one receiver placed on the robot grip. Subsequently, in order to reduce the number of parameters to work with, a parametric method for characterisation of this signal is presented. This has been done by calculating several geometric parameters from the signal envelope. Afterwards, a study of the probability distribution function for each parameter provides the necessary information for the expert system to carry out the distinction between the different objects of interest. In this way, it permits the establishment of a comparison among different expert system types for automatic shape recognition using ultrasounds.     

A different paradigm for expert systems: an introduction to logic programming and related knowledge representation issues

Abstract: Current expert system technology is 30 years old. Expert system shells find their origins in the work of early expert systems, most notably MYCIN which was developed at Stanford in the mid-1970s. Even Prolog programmers are settling for less robust reasoning power. The logic programming community (from which both expert systems and Prolog arose) has made notable advances since those times. These advances are lacking from current expert system technology. The advances include a well-developed theory of multiple forms of negation, an understanding of open domains and the closed world assumption, default reasoning with exceptions, reasoning with respect to time (i.e. a solution to the frame problem, and introspection with regard to previous beliefs), reasoning about actions, introspection, and maintaining multiple views of the world simultaneously.
The contribution of this paper is to discuss these developments in a singular, integrated, practical, digestible manner. Some of these ideas exist in a variety of papers spread across decades. They also exist in the minds of a very small community of researchers. Some of these ideas are unpublished. The presentation in this paper is from a different point of view, and intended to be more comprehensive and pedagogical. The presentation is also intended to be accessible to a much wider audience. Both the synthesis and the simplicity of this presentation are absent from the literature.     

路径问题在专家系统推理机中的提出和解决

提出了引用路径问题的解决方法来设计专家系统的推理机,旨在降低推理的复杂度,提高推理效率。并将这种方法应用于具体案例中,取得了良好的效果。