Expert systems in the context of multi-brain systems

In this article, we postulate the possibility of building a four-brain like expert system. For this purpose we have taken ideas from Minsky’s “Society of Mind” some concepts from Brooks and Stein’s “Brains for Bodies” project, and some mechanisms from neurophysiology. In our search for conceptualization of a minimum multi-brain like system oriented to expert systems, we found that: (a) expert systems with blackboard architecture can be seen as primitive topological approaches to multi-brain systems, (b)in our multi-brain like expert systems conception, as in many other expert systems, we feel that we have to impose our external language as their internal language and our external symbols as their internal symbols. These are the kind of entities discussed by philosophers as “What is it like to be a Bat?”. In this context our conception is still a “Batman”. True-cognitive expert systems might be produced in the future if the matters are self constructed internal languages, and its post-hoc knowledge, and (c) in the scope of multi-brain systems, expert systems are not an engineering subject of artificial intelligence (AI) but a substantive subject that goes from philosophical to neurophysiological aspects.     

Verification and validation of RANS maneuvering simulation of Esso Osaka: effects of drift and rudder angle on forces and moments

The present work covers rigorous verification and validation of a Reynolds averaged Navier–Stokes (RANS) code applied to a maneuvering problem covering the “static rudder” and “pure drift” conditions. The objectives are: (1) to apply the RANS technology together with the Chimera grid technique to compute the hydrodynamic forces acting on the bare hull and the appended hull of the tanker Esso Osaka during simple maneuvers; (2) to provide detailed information about the levels of verification and validation for the integral quantities; (3) to develop a procedure for generation of the systematically refined Chimera grids, which are used for the verification; and (4) to provide information about the trends in the forces and moments when the rudder and drift angles are varied. The flow problem is solved by the general-purpose RANS code CFDSHIP-IOWA, which is run in steady mode. The effect of the free surface is neglected and the two-equation k–ω model, models the turbulence. The verification and validation are performed by means of one of the latest approaches. It takes both the numerical and experimental uncertainties and errors into account, when the method is validated. The verification and validation of the forces and moments show that fair levels of verification and validation are established for most of the considered cases. A brief summary of the levels validation says that the bare hull results are validated at levels from 4.2% to 9.3%. For the appended hull the levels of validation for the rudder forces and the overall forces and moments range from 3.4% to 28.0% and from 6.3% to 37.2% for the “static rudder” and “pure drift” conditions, respectively. Further, it appears that even though validation is not achieved for all the cases, the method is generally capable of capturing the overall behavior of the integral quantities when the rudder and drift angles are varied.     

Assessing the suitability of judgmental auditing tasks for expert systems development: An empirical approach

The “Big 6” public accounting firms have invested considerable resources in the development of expert system (ES) for a variety of auditing tasks. However, the tasks for most existing auditing ESs appear to have been selected based on accessibility and cooperation of experts, and/or the judgmental evaluation of the developer, rather than a careful selection of the task from among a number of viable alternatives. A critical aspect of task suitability is the degree to which the characteristics of a candidate task match the capabilities of ES technology. In this study, a questionnaire was developed to obtain task-related information from practicing auditors in order to distinguish among candidate auditing tasks in terms of their suitability for ES development. Auditors in the “Big 6” public accounting firms were asked to provide ratings of the knowledge, data, and task characteristics of nine judgmental auditing tasks. The analysis of the data obtained from fifty-nine auditors revealed that the nine tasks were distinguishable in terms of their suitability for ES application. Two tasks, determining compliance with generally accepted accounting principles and audit work program development, were relatively better suited for ES application, while determination of the adequacy of an allowance and going concern evaluation were the tasks least suited for ES application. The fact that actual ES development efforts in the “Big 6” firms have emphasized the compliance and audit work program development tasks provides a degree of validation of our results.     

A structure for expert systems maintenance

An approach to the design of maintainable expert systems is presented. Central to this approach is a conceptual model in which the data and knowledge are both modelled as formal “items” in a uniform way. “Objects” are introduced as “item building” operators. The notion of the “decomposition” of items and objects provides the foundation for a single rule of normalization. This single rule applies to all items and objects, including knowledge items, and is a non-trivial generalization of the traditional normal forms for database. Coupling relationships represent the necessary maintenance paths in the conceptual model. A complete characterization of coupling relationships is given, and the value of normalization to the reduction of maintenance costs is discussed.     

故障诊断专家系统中知识库组织和维护技术的研究

在开发化工过程实时故障诊断专家系统的基础上,本文讨论了知识库的组织和维护技术,对知识表示和知识库结构进行了详细介绍。在知识库维护方面,采用面向对象的方法描述领域知识,并实现知识获取和转换;对于知识校验,提出一种基于有向图的知识校验方法。以润滑油生产过程为例,讨论了系统的知识校验过程。本文讨论的技术和策略已应用于润滑油过程实时故障诊断专家系统。实际应用表明,该技术和策略保证了知识库和专家系统的稳定和高效率。     

A Delphi-based approach to developing expert systems with the cooperation of multiple experts

Knowledge acquisition has been a critical bottleneck in building knowledge-based systems. In past decades, several methods and systems have been proposed to cope with this problem. Most of these methods and systems were proposed to deal with the acquisition of domain knowledge from single expert. However, as multiple experts may have different experiences and knowledge on the same application domain, it is necessary to elicit and integrate knowledge from multiple experts in building an effective expert system. Moreover, the recent literature has depicted that “time” is an important parameter that might significantly affect the accuracy of inference results of an expert system; therefore, while discussing the elicitation of domain expertise from multiple experts, it becomes an challenging and important issue to take the “time” factor into consideration. To cope with these problems, in this study, we propose a Delphi-based approach to eliciting knowledge from multiple experts. An application on the diagnosis of Severe Acute Respiratory Syndrome has depicted the superiority of the novel approach.     

Building modular legal knowledge systems

A practical legal knowledge system must be versatile and capable of supporting many fundamentally different aspects of the lawyer's work. In this paper a general framework for building knowledge systems from distinct but communicating modules is proposed. There are currently three different kinds of modules: computation, data storage, and environmental interaction. These modules include inference engines, database managers, and hypertext. Several strategies for coupling these modules are discussed. This framework is used to construct expert systems for two distinct domains: a legal expert system for labour law and a real-time expert system for process control in a pulp plant. The legal application shows that this system can be used to construct an “intelligent library”, guiding the user to relevant documents, rather than merely retrieving documents on request.     

Interviewer's toolbox

Knowledge elicitation is the life blood of the expert system. Qualifications for a knowledge engineer (KE) to elicit knowledge are, “You must be good with people and possess good interview skills.” While this is true, there are some techniques the KE can develop to better meet these qualifications. Understanding the expert and the problem of expression gives insight into the person while standard interview techniques and cognitive interviewing techniques build interviewing skills.     

Exploring models for semantic category verification

Many artificial intelligence tasks, such as automated question answering, reasoning, or heterogeneous database integration, involve verification of a semantic category (e.g. “coffee” is a drink, “red” is a color, while “steak” is not a drink and “big” is not a color). In this research, we explore completely automated on-the-fly verification of a membership in any arbitrary category which has not been expected a priori. Our approach does not rely on any manually codified knowledge (such as WordNet or Wikipedia) but instead capitalizes on the diversity of topics and word usage on the World Wide Web, thus can be considered “knowledge-light” and complementary to the “knowledge-intensive” approaches. We have created a quantitative verification model and established (1) what specific variables are important and (2) what ranges and upper limits of accuracy are attainable. While our semantic verification algorithm is entirely self-contained (not involving any previously reported components that are beyond the scope of this paper), we have tested it empirically within our fact seeking engine on the well known TREC conference test questions. Due to our implementation of semantic verification, the answer accuracy has improved by up to 16% depending on the specific models and metrics used.     

A framework for building knowledge-bases under uncertainty

Managing uncertainty during the knowledge engineering process from elicitation to validation and verification requires a flexible, intuitive, and semantically sound knowledge representation. This is especially important since this process is typically highly interactive with the human user to add, update, and maintain knowledge. In this paper, we present a model of knowledge representation called Bayesian Knowledge-Bases (BKBs). It unifies a ‘if-then’ style rules with probability theory. We also consider the computational efficiency of reasoning over BKBs. We can show that through careful construction of the knowledge-base, reasoning is computationally tractable and can in fact be polynomial-time. BKBs are currently fielded in the PESKI intelligent system development environment.     

Expertise transfer, knowledge elicitation, and delayed recall in a design context

This paper describes a new method for knowledge elicitation that may contribute to effective expertise transfer from human experts to knowledge-based systems. The method was applied to knowledge transfer in an aerospace design context. Knowledge was transferred directly from an expert designer to both expert and novice “receivers” of information. Transfer occurred in a natural way, without intervention from a knowledge engineer. To evaluate the process, the information receivers were required to recall the transmitted knowledge after a seven week delay. Results suggest that this method can be effective for expertise transfer and can indicate desirable characteristics for knowledge-based systems which aim to be adaptable to users' differing levels of competence.     

Modelling the Stereovision-Correspondence-Analysis task by Lateral Inhibition in Accumulative Computation problem-solving method

Recently, the Algorithmic Lateral Inhibition (ALI) method and the Accumulative Computation (AC) method have proven to be efficient in modelling at the knowledge level for general-motion-detection tasks in video sequences. More precisely, the task of persistent motion detection has been widely expressed by means of the AC method, whereas the ALI method has been used with the objective of moving objects detection, labelling and further tracking. This paper exploits the current knowledge of our research team on the mentioned problem-solving methods to model the Stereovision-Correspondence-Analysis (SCA) task. For this purpose, ALI and AC methods are combined into the Lateral Inhibition in Accumulative Computation (LIAC) method. The four basic subtasks, namely “LIAC 2D Charge-Memory Calculation”, “LIAC 2D Charge-Disparity Analysis” and “LIAC 3D Charge-Memory Calculation” in our proposal of SCA are described in detail by inferential CommonKADS schemes. It is shown that the LIAC method may perfectly be used to solve a complex task based on motion information inherent to binocular video sequences.     

Computer-aided design of structures using LISP

An experimental knowledge-based expert system, called SSPG. is presented for design of stiffened steel plate girders, using the LISP programming language. Employing previously acquired knowledge of the optimum span-to-web depth ratios, SSPG is capable of yielding a “practical” minimum weight design without performing a costly formal optimization. This research indicates the effectiveness of the LISP language for computer-aided design of structures where not only subjective knowledge and heuristics are needed but also substantial numerical computations are involved.     

Expert systems in manufacturing: Promises and perils

Expert systems play a key role as a tool to enhance productivity, improve quality, increase profits and capture expertise in many business and industrial environments. Although the potential of expert systems is well-understood, it has been a challenging, and often elusive, task to make an expert system operational in the “real-world” environment. One of the major reasons why many expert systems fail to be integrated into the operational environment is a lack of knowledge and techniques on how to test an expert system. The implications of an inadequately-tested expert system in a manufacturing environment are serious. In this paper, we take a look at the many applications of expert systems in manufacturing and utilize a three-pronged approach to develop robust and reliable expert systems.     

Maybe A “Knowledge Culture” Isn't Always so Important after All!

THE PREVAILING WISDOM, AND a common “best practice,” of knowledge management (KM) is that a primary determinant of success in getting people to submit their most valuable personal knowledge to a repository is the existence of a “knowledge culture” in the organization.     

Maintenance tools for knowledge-based systems: the MAKE project

One of the major obstacles to the routine exploitation of knowledge-based and expert systems, is the difficulty of validating the knowledge base, and of maintaining it in a state which reflects current knowledge. This is of particular importance for systems based on law or regulations, where it is vital that the knowledge base be a true reflection of the legal position, and where there is a constant stream of changes to the correct legal position. Maintenance Assistance for Knowledge Engineers (MAKE) is a project designed to explore these issues, and to build a set of tools which will support the validation and maintenance of knowledge bases deriving from regulations. These tools include facilities to examine the structural features of the knowledge base, so as to guard against redundancy, nonprovability and contradiction; facilities to identify parts of the knowledge base jeopardised by changes in the domain, or in the understanding of the domain; and facilities to perform a variety of “house keeping” tasks. The paper firstly analyses the different types of change that may be required to maintain the knowledge base, and then proceeds to describe the set of tools developed in the MAKE project to accomodate these changes.     

VERIFICATION OF QUALITATIVE PROPERTIES OF RULE-BASED EXPERT SYSTEMS

Frequently, expert systems are developed to operate in dynamic environments where they must reason about time-varying information and generate hypotheses, conclusions, and process inputs that can drastically influence the environment within which they operate. For instance, expert systems used for fault diagnosis and fault accommodation in nuclear power plants reason over sensor data and operator inputs, form fault hypotheses, make recommendations pertaining to safe process operation, and in crisis situations, could generate command inputs to the process to help maintain safe operation. Clearly, there is a pressing need to verify and certify that such expert systems are dependable in their operation and can reliably maintain adequate performance levels. In this article we develop a mathematical approach to verifying qualitative properties of rule-based expert systems that operate in dynamic and uncertain environments. First, we provide mathematical models for the expert system (including the knowledge-base and inference engine) and for the mechanisms for interfacing to the user inputs and the dynamic process. Next, using these mathematical models, we show that while the structure and interconnection of information in the knowledge base influence the expert system's ability to react appropriately in a dynamic environment, the qualitative properties of the full knowledge-base/inference engine loop must be considered to fully characterize an expert system's dynamic behavior. To illustrate the verification approach, we show how to model and analyze the qualitative properties of rule-based expert systems that solve a water-jug filling problem and a simple process control problem. Finally, in our concluding remarks, we highlight some limitations of our approach and provide some future directions for research.     

Optimizing agent-based meeting scheduling through preference estimation

Meeting scheduling is a routine task that needs to be performed quite regularly and frequently within any organization. Unfortunately, this task can be quite tedious and time-consuming, potentially requiring a several rounds of negotiations among many people on the meeting date, time and place before a meeting can finally be confirmed. The objective of our research is to create an agent-based environment within which meeting scheduling can be performed and optimized. For meeting scheduling, we define optimality as the solution that has the highest average preference level among all the possible choices. Our model tries to mimic real life in that an individual's preferences are not made public. Without complete information, traditional optimal algorithms, such as A^* will not work. In this paper, we present a novel “preference estimation” technique that allows us to find optimal solutions to negotiations problems without needing to know the exact preference models of all the meeting participants beforehand. Instead, their preferences are “estimated” and built on the fly based on observations of their responses during negotiation. Another unique contribution is the use of “preference rules” that allow preferences to change dynamical as scheduling decisions are made. This mimics changing preferences as schedule gets filled. This paper uses two negotiation algorithms to compare the effect of “preference estimation”—one that is based on negotiation through relaxation and the other that extends this with preference estimations. Simulations were then performed to compare these algorithms.