An Anthology on the Homology of Simulation with Artificial Intelligence

Simulation and artificial intelligence (AI) can be related in the following ways: the methodological similarities, the actual and potential uses of AI concepts in simulation, expert systems in simulation environments, and the gains for AI when applying ideas from simulation. This paper highlights the personal experiences of the author by discussing, for each of the four above-mentioned areas, a topic or a computer system in which he is directly involved. To highlight the similarities, it will be shown that the three-phase simulation model can be considered as a production-system model. A natural-language understanding system that was used in the definition stage of a simulation project will provide the basis for discussing AI concepts in simulation environments. An expert system used in the development and verification stages will provide an insight into the application of expert systems in simulation modelling. An expert-system development tool which is partially based on simulation techniques will be used to outline the gains for AI.     

Research into Expert Systems to Aid Simulation Model Formulation

The widespread availability of inexpensive computing power and recent developments in machine intelligence now makes it possible to consider computer assistance in the formulation phase of a discrete-event simulation model. The goal is to speed up this process. With this in mind, a series of prototype expert systems (written in LISP and run on an APPLE II) have been produced. The first prototype system was built using the ‘MYCIN’ approach. In the second prototype system a more informal approach was used, and the resulting computer-aided system is a natural-language understanding system. This paper outlines the need for such research and discusses the system development by describing the two main models and explaining why the natural-language approach was adopted.     

Current research directions in the development of expert systems based on belief networks

An expert system is a computer program that is designed to solve problems at a level comparable to that of a human expert in a given domain. Often expert systems require a representation of uncertainty. This paper highlights some of the key developments in the history of representing uncertainty in expert systems. An uncertainty representation called belief networks is then introduced and its use in expert systems is motivated. The paper concludes with a discussion of current directions in belief network research.     

Decomposition and simulation of sequential dynamical systems

Sequential dynamical systems have been developed as a basis for a theory of computer simulation. This paper contains a generalization of this concept. The notion of morphism of sequential dynamical systems is introduced, formalizing the concept of simulating one system by another. Several examples of morphisms are given. Using the morphism concept, it is shown that every sequential dynamical system decomposes uniquely into a product of indecomposable systems.     

An online identification algorithm of unknown time-varying delay and internal multimodel control for discrete non-linear systems

In this paper, an online algorithm is proposed for the identification of unknown time-varying input delay in the case of discrete non-linear systems described by decoupled multimodel. This method relies on the minimization of a performance index based on the error between the real system and the partial internal models outputs. In addition, a decoupled internal multimodel control is proposed for the compensation of discrete non-linear systems with time-varying delay. This control scheme incorporates partial internal model controls. Each partial controller is associated to a specified operating zone of the non-linear system. The switching between these controllers is ensured by a supervisor that contains a set of local predictors. A simulation example is carried out to illustrate the significance of the proposed time-varying delay identification algorithm and the proposed internal multimodel control scheme.     

Expert systems for planning and scheduling manufacturing systems

In this paper, research and applications of expert systems in production planning and scheduling are reviewed. Components of expert systems are briefly discussed. Relationship between expert system and operations research approaches are presented. Integration of operations research and expert system techniques is explored.     

Experiences with Using Expert Systems in O.R.

Increased awareness of expert systems technology, and the availability of relevant software, has tempted many O.R. groups to investigate the expert systems approach. This paper considers the strategies open to O.R. groups interested in employing expert systems, reviews some of the relevant software, and discusses what those O.R. groups who have already investigated the use of expert systems methods are actually doing.Some of the authors' experience of developing expert systems is discussed. The development of an expert system that helps bankers analyse company accounts is presented. The use of expert system methods as vehicles for decision analysis, and the possibilities for producing systems that act as O.R. consultants are also discussed.     

Structure and logic in knowledge-based representations of systems simulation models

The paper focuses on the similarity between modelling and knowledge representation, trying to bring together the OR/Systems Science and the Artificial Intelligence views when referring to a computer system simulation, especially of the discrete-event or the network types. The models we consider are generalized activity networks with resources, including either models with a finite lifetime, such as project scheduling networks, or steady state models, such as queueing networks. By enhancing the structure of entities and states and the logic of transitions within a model specification, modularity is improved and one may adopt a more declarative approach. The relational and rule-based representation formalisms are a convenient choice for that purpose. Then, the use of knowledge bases both for the static (i.e. consultative) and the dynamic (i.e. experimental) study of the model turns up to be more natural. Moreover, the task of building an expert system for decision support on system analysis or synthesis becomes easier. The paper reports some original work in the above directions, using a logic programming approach and an associated specification methodology based on general systems concepts.     

The existence of positive solution of elliptic system by a linking theorem on product space

This paper is devoted to study the existence of nontrivial positive solution of a class of elliptic system with Dirichlet Data. By using the abstract linking theorems on product space we established in Zhao et al. (Nonlinear Anal. 49 (2002) 431) we obtain the existence of three nonnegative solutions for a class of elliptic systems and the existence of a nontrivial positive solution for the problem related to the model of competing species systems involving critical Sobolev exponents.     

Linking systems of difference sets

A linking system of difference sets is a collection of mutually related group difference sets, whose advantageous properties have been used to extend classical constructions of systems of linked symmetric designs. The central problems are to determine which groups contain a linking system of difference sets, and how large such a system can be. All previous constructive results for linking systems of difference sets are restricted to 2‐groups. We use an elementary projection argument to show that neither the McFarland/Dillon nor the Spence construction of difference sets can give rise to a linking system of difference sets in non‐2‐groups. We make a connection to Kerdock and bent sets, which provides large linking systems of difference sets in elementary abelian 2‐groups. We give a new construction for linking systems of difference sets in 2‐groups, taking advantage of a previously unrecognized connection with group difference matrices. This construction simplifies and extends prior results, producing larger linking systems than before in certain 2‐groups, new linking systems in other 2‐groups for which no system was previously known, and the first known examples in nonabelian groups.     

一种智能入侵检测系统设计与模拟实现

针对目前大多数的入侵检测系统存在的局限性,提出一种较完善的入侵检测模型,将专家系统和神经网络技术同时应用于入侵检测系统中.设计专家系统模块检测已知攻击,设计神经网络模块实现未知攻击的检测,提高了检测准确性.同时在神经网络模块应用PCA方法降低入侵数据维数,提高检测效率.仿真实验验证,该设计能有效降低入侵检测系统的漏报率和误报率.     

Is CAD/CAM ready for AI?

In this paper we explore the possibility of merging CAD/CAM with Artificial Intelligence technology. A brief tutorial on expert systems is presented. Several observations are noted which link existing expert system applications with various CAD/CAM problems. In conclusion, these existing expert system applications may provide guidance for implementing AI solutions to certain CAD/CAM problems.     

Rule Induction in Investment Appraisal

Rule induction is a machine learning technique which has been used in knowledge acquisition for expert systems. This paper explores its use for the interpretation of the results from simulation of investment models in order to identify the major risks.     

IDES: influence diagram based expert system

Influence diagrams have been used effectively in applied decision analysis to model complex systems, identify probabilistic dependence and characterize the flow of information. Their graphical representation and intuitive framework are particularly effective in representing knowledge from experts with diverse backgrounds and varying degrees of technical proficiency. They allow both a symbolic representation of the system interrelationships and a quantitative measure that can be of discrete or continuous functional form. By exploiting this abstraction hierarchy, successive degrees of specification can be made by several individuals, each encoding his or her expert knowledge of the problem and bounds on critical parameters. It is proposed that an interactive computer program that automates this influence diagram technology would provide an excellent tool for building expert systems. This paper describes such a modeling tool: the IDES (Influence Diagram Based Expert System) developed at the University of California at Berkeley as a modeling tool for building expert systems requiring reasoning with uncertain or incomplete information. The Diagnostician's Problem is presented as a tutorial for describing the IDES solution procedure.     

Using an Expert System to Monitor an Automatic Stock Control System

The theory of the control of stock is well understood, and a large number of firms use automatic stock control and ordering techniques as a major part of their inventory control systems. However, the effectiveness of such automatic stock control systems depends on the correct selection of system parameters for each item in the inventory and on the accurate reporting of information. It is not unusual for errors to be made in both the selection of parameters and the entry of data, and regular failures of either can cause severe problems in stock control. It is generally difficult for a human stock controller to discover errors quickly because of the large number of items that is usually found in a company inventory. This paper describes the use of an expert system to examine stock records and diagnose anomalies in the data provided or parameters applied to individual items in a stock control system. The expert system ‘reasons’ from symptoms such as reports from the forecasting system or high stock levels, to defects in the stock model used or data-collection processes, and from there to possible remedial action. Results are presented for runs of the expert system on a simulated stock control system.     

A Problem-driven Approach to Expert System Development

Expert system technology can fairly be described as high profile at present. Yet its track record as a useful method for tackling problems is subject to widely differing views. This paper considers the domain of production planning and control, which has been described as a good application area for expert systems a domain where OR has had little success. The authors argue that mainstream expert system methodology is exploratory rather than problem-driven and thus is not suited to the domain. A problem-driven approach to expert system development is presented, an approach which makes use of soft systems methodology. The reasons for such an approach within production planning and control are discussed, and the use of soft systems methodology within the approach is reflected on.     

Experiences in Developing an Expert System for MBA Admissions

An expert system to assist in admitting students onto a part-time postgraduate course has been developed through three phases, using an expert systems shell. The first phase was an unstructured approach, which demonstrated the feasibility of the system but led to difficulties in development. The second phase followed a simplified systems analysis and resulted in a more comprehensive and structured system. However, its operation was cumbersome and unfriendly. The third phase involved developing a mathematical model of the decision-maker's judgement and led to a simpler, more effective and easier to use system.     

SIMULEX — a multiattribute DSS to solve rescheduling problems

Knowledge-based systems (KBS) can help to make simulation available to a large group of users. We want to exemplify this by describing a decision support system (DSS) for short term rescheduling in manufacturing called SIMULEX. It couples expert systems, simulation, and a multiattribute decision making (MADM) procedure to assist the production manager. After an introduction to simulation as a problem solving tool, the current problems in production control and the goals of the project are described. Then, the various components of SIMULEX are explained in some detail. Some results and a short outlook conclude the article.     

Integrated decision support for aviation safety inspectors

This paper presents a systems viewpoint for developing an advanced decision support system for aircraft safety inspectors. Research results from a Federal Aviation Administration (FAA) sponsored project to use neural network and expert systems technology to analyze aircraft maintenance databases are summarized. One of the main objectives of this research is to define more refined “alert” indicators for national comparison purposes that can signal potential problem areas by aircraft type for safety inspector consideration.

Integration aspects are addressed on two levels: (1) integration of the various technical components of the decision support system, and (2) integration of the decision support system with individual behavior, management systems and organizational structure, as well as corporate culture across both formal and informal dimensions. The paper summarizes the creation of strategic “inspection profiles” for aging aircraft and reliability curve fitting for structural components both based upon using neural network technology. Also, the potential use of a model-based expert system to facilitate field inspection diagnostics is presented. Finally, a framework for developing an intelligent decision system to support aircraft safety inspections is proposed that links expert systems, neural networks, as well as a paradigm of the decision making process typically used in unstructured situations.