Expert systems and economic policy analysis: An application to OCS auction leasing

In this paper we argue that expert systems can be powerful tools for modelling microeconomic systems, including both individual decision making and the coordination of individual agents in a resource allocation mechanism. Using the fact that expert systems are essentially computerized versions of decision processes, we illustrate how they can be viewed as generalized process models of decision-making. We argue that the expert system approach is beneficial because it allows a policy analyst to explore the implication of policy alternatives without having to incur the generally prohibitive cost of field implementation studies. Further, enables the incorporation and updating of decision strategies and qualitative information, which human experts typically use but which is not amenable to pure mathematical modelling.One particular microeconomic system we suggest could be modelled as an expert system is the OCS offshore oil lease auction process. Moreover, we argue that constructing such an expert system model would require the development of two integrated expert systems: one for the auction process and subsequent resource allocation and the other to model the individual bidding behavior of the auction participants. We set out the structure of the auction expert system in some detail and discuss rules of thumb used by bidders inferred from our empirical research on past OCS auctions.Such an expert system of an auction leasing process could provide benefits to both bidders (e.g., oil companies) and the auctioneer (e.g., the Department of the Interior) as well. Bidders, by trying different strategies against different hypothesized strategies by their opponents could use such an integrated expert system to improve their bidding performances. The auctioneer, on the other hand, could test the efficiency of various proposed auction institutions under different assumptions about bidding behavior. In some circumstances, it might be desirable to even automate the auction process with a network coordinating the expert systems used by the individual firms and a computerized auctioneer.     

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.     

Purpose-Based Expert Finding in a Portfolio Management System

Most of the research in the area of expert finding focuses on creating and maintaining centralized directories of experts' profiles, which users can search on demand. However, in a distributed multiagent-based software environment, the autonomous agents are free to develop expert models or model fragments for their own purposes and from their viewpoints. Therefore, the focus of expert finding is shifting from the collection at one place as much data about a expert as possible to accessing on demand from various agents whatever user information is available at the moment and interpreting it for a particular purpose. This paper outlines purpose-based expert modeling as an approach for finding an expert in a multiagent portfolio management system in which autonomous agents develop expert agent models independently and do not adhere to a common representation scheme. This approach aims to develop taxonomy of purposes that define a variety of context-dependent user modeling processes, which are used by the users' personal agents to find appropriate expert agents to advise users on investing strategies.     

基于专家系统的网络故障管理系统的设计

故障管理是网络管理中最基本的内容之一。网络出现故障时,智能故障定位是实现网络故障管理自动化的关键。结合网络管理与专家系统的知识,提出了一种基于专家系统的网络故障管理系统结构;着重介绍了此系统的几个重要功能模块,并说明使用专家系统进行网络故障管理的优点。结果表明,该系统很好地发挥了专家系统的智能性,能够快速准确地诊断出网络系统的故障,并能提出有效的重构方案,完全适合于网络系统的故障诊断。     

Automated network troubleshooting knowledge acquisition

Troubleshooting knowledge acquisition is a notorious network maintenance expert systems development bottleneck. We present an improved methodology to generate automatically a skeleton of network troubleshooting knowledge base given the data about network topology, test costs, and network component failure likelihood. Our methodology uses AO ^* search where a suitable modification of the Huffman code procedure is found to be an admissible heuristic. Our heuristic uses synergistically information about both component failure rates and test costs while relaxing topology constraints. The resulting expert system (XTAR) minimizes expected troubleshooting cost faster and learns better troubleshooting techniques during its operation.     

Should we be surprised at expert system success ?

Abstract. Expert systems proliferate. They are now a usable and used tool in a variety of organizations. However, little research has been undertaken on the quantification of success or failure of expert system projects. By and large the only test carried out for expert system validation is one of comparison of the conclusions of the system with those of an expert or team of experts. While questioning the validity, this paper accepts that this method is being used and analyses whether or not the results obtained from this type of test should be a surprise. That is, what sort of results should be expected and to what extent do the reported ones live up to these expectations? Earlier research from the field of human information processing casts doubts on the success claims made for current expert systems. The implications of this research are then analysed in order to explore the possible redeeming features of expert systems which are not taken into account in such comparisons. The use of linear models as comparators for expert systems may prove beneficial.     

Expert maintenance systems in telecommunication networks

Our survey of some 40 network maintenance expert systems reveals theri main shortcoming, which is the difficulty to acquire troubleshooting knowledge both when initializing the expert system and after its deployment. Additionally, the state-of-the-art troubleshooting expert systems do not optimize troubleshooting cost. We present theAO ^* algorithm to generate a network troubleshooting expert system which minimizes the expected troubleshooting cost and learns better troubleshooting techniques during its operation.     

Expert systems: An application in flexible manufacturing

Flexible manufacturing systems (FMS) are needed to provide manufacturing operations with the capability to adjust, in real time, to changes in the manufacturing environment. Realization of the goals of flexible manufacturing is governed by the ability of the FMS to maintain adequate information on the factory to assist in generating scenarios from product planning to operations and performance. This leads to a view where the factory is represented as an integrated information system. To facilitate the analysis of information requirements and the design of information systems for flexible manufacturing, an expert support system (ESS) which can be used to model and study the various structures is described. This ESS uses the information cell model to build these information structures. Petri net representations of these structures and their interactions are then constructed. The ESS may now be used to exercise these models and study their performance using time and cost measures.     

Sliding mode coordination control for multiagent systems with underactuated agent dynamics

In this paper, we develop a new integrated coordinated control and obstacle avoidance approach for a general class of underactuated agents. We use graph-theoretic notions to characterise communication topology in the network of underactuated agents as determined by the information flow directions and captured by the graph Laplacian matrix. Obstacle avoidance is achieved by surrounding the stationary as well as moving obstacles by elliptical or other convex shapes that serve as stable periodic solutions to planar systems of ordinary differential equations and using transient trajectories of those systems to navigate the agents around the obstacles. Decentralised controllers for individual agents are designed using sliding mode control approach and are only based on data communicated from the neighbouring agents. We demonstrate the efficacy of our theoretical approach using an example of a system of wheeled mobile robots that reach and maintain a desired formation. Finally, we validate our results experimentally.     

The design process of expert systems development: some concerns

Abstract: A discussion is presented of why some expert systems that organizations have developed are not successful. The concept of design process plays a very significant role at the outset of the expert system development process. This concept has not been the subject of much debate and attention in expert systems development. From the author's point of view, one of the main issues is how the designer (knowledge engineer) thinks about the design process. In general, the designer's process is influenced by the knowledge engineer's conception. This paper endeavors to disclose some of the main factors related to the knowledge engineer's conception of the design process and an attempt is made to put forward a conceptual model of the expert system design process. This conceptual model is an initial step towards a successful implementation of expert system projects.     

The rise and 'fall' of expert systems in medicine

This paper represents a personal perspective on the last 13 years of research in medical artificial intelligence and expert systems, and describes the progress of the discipline from its inception to its current status. The challenges faced by researchers are examined, and it is argued that (despite the claims) theoretical and technical limitations are not the major barriers to the successful implementation of medical artificial intelligence and expert systems, but rather more complex professional and organizational issues are at stake.     

An Architectural Modelfor Intelligent Network Management

1 IntroductionThe explosion in size and complexity of today's computer network means that NetworkManagement (NM) is a critical factor. The ever growing demand for distributed applicationscoupled with increasing corporate globalization often leads to users requiring constant network availability as the network becomes the primary strategic resource. The currellt NMbecomes increasingly inadequate, since the networks to be managed are heterogeneous anddynamic, and manapemellt functions, such as…     

Fault Prediction and Compensation Functions in a Diagnostic Knowledge-Based System for Hydraulic Systems

Fault prediction and fault compensation are beneficial for the production technology and give a new dimension to fault diagnosis in technical systems. The overall goal of this paper is the presentation of fault prediction and fault compensation procedures as they are studied, implemented and embedded in a real time expert system. This expert system detects and diagnoses faults in hydraulic systems. For this purpose dynamic modelling information, on-line sensor information, special features of the domain of hydraulic systems and expert systems technology are used co-operatively.     

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.     

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.     

新型网络管理系统模型及其专家系统

现代计算机网络管理是一项复杂而又繁重的工作,它需要大量的信息并运用操作者的判断与技能.由于产品厂商不同而导致的异种网络环境以及高质量的用户服务需求,这种网络管理的复杂性还在不断增加.文章提出了一个进行网络智能管理(intelligent management of network,简称IMN)的新型系统模型.在IMN体系结构中,用专家系统处理特定问题,建立一个智能故障管理系统;用网段或网域中的智能代理建立网络管理与应用和用户需求的密切关系;给出了一个管理员模型,用于控制各子系统,协调各项管理任务.IMN系     

基于可拓方法的智能策略生成器的研究

提出了解决矛盾问题的智能型集成化可拓策略生成器的设计思路和总体结构框架。系统设计基于综合集成方法的基本思想,遵循信息组织和可拓决策知识的特点,将物元分析法、专家系统、决策支持系统、神经网络有机地组织集成起来,从而快速、灵活地为决策者提供从知识获取、判断推理到创造性思维、策略生成的智能决策支持环境。     

A Rule-based Extension to the C++ Language

In rule-based systems, sets of rules are applied to known information to generate new, consequential information. The majority of the large number of existing systems¹ has been produced with the objective of ease of use, enforcing an English-like syntax in the rules and hiding the control methods from the developer. The method adopted in the current work aims to provide rule-based facilities while maintaining the flexibility and implementation efficiency of a conventional language. This is achieved by extending the features of C++ to permit the development of rule-based systems whose control is decided by the developer. The design can be tailored to the application unrestricted by the normal constraints of the rule-based system, and a framework is made available for the development of multiple expert systems for use in blackboard system applications. This paper describes the approach adopted in the current work, the operation of the system and describes two example implementations. © 1997 John Wiley & Sons, Ltd.     

Finite-time coordination in multiagent systems using sliding mode control approach

Finite-time stability in dynamical systems theory involves systems whose trajectories converge to an equilibrium state in finite time. In this paper, we use the notion of finite-time stability to apply it to the problem of coordinated motion in multiagent systems. Specifically, we consider a group of agents described by fully actuated Euler–Lagrange dynamics along with a leader agent with an objective to reach and maintain a desired formation characterized by steady-state distances between the neighboring agents in finite time. We use graph theoretic notions to characterize communication topology in the network determined by the information flow directions and captured by the graph Laplacian matrix. Furthermore, using sliding mode control approach, we design decentralized control inputs for individual agents that use only data from the neighboring agents which directly communicate their state information to the current agent in order to drive the current agent to the desired steady state. Sliding mode control is known to drive the system states to the sliding surface in finite time. The key feature of our approach is in the design of non-smooth sliding surfaces such that, while on the sliding surface, the error states converge to the origin in finite time, thus ensuring finite-time coordination among the agents in the network. In addition, we discuss the case of switching communication topologies in multiagent systems. Finally, we show the efficacy of our theoretical results using an example of a multiagent system involving planar double integrator agents.