Applying KADS to KADS: knowledge-based guidance for knowledge engineering

Abstract: The KADS methodology (Schreiber et al. 1993; Tansley & Hayball 1993) and its successor, CommonKADS (Wielinga et al. 1992) have proved to be very useful approaches for modelling the various transformations involved between eliciting knowledge from an expert and encoding this knowledge in a computer program. These transformations are represented in a series of models. While it is widely agreed that these methods are excellent approaches from a theoretical viewpoint, the documentation provided concentrates on defining what models should be produced, with only general guidance on how the models should be produced. This has the advantage of making KADS and CommonKADS widely applicable, but it also means that considerable training and experience is required to become proficient in them. This paper reviews three projects that investigated the feasibility of producing specific guidance for certain decisions which are required when using KADS or CommonKADS to develop a knowledge-based system. Guidance was produced for the identification of the generic task addressed by a knowledge-based system; for the selection of appropriate AI techniques for implementing the analysed knowledge; and for selecting a suitable tool for implementing the system. Each set of guidance was encoded in its own knowledge-based system, which was itself developed with the assistance of KADS or CommonKADS. These projects therefore both studied and applied KADS and CommonKADS in order to produce knowledge-based guidance for knowledge engineers. The projects showed that it was feasible to produce heuristic guidance which could be understood, applied and occasionally overridden by knowledge engineers. The guidance provides reasonably experienced knowledge engineers with a framework for making the key decisions required by CommonKADS, in the same way that CommonKADS provides knowledge engineers with a framework for representing knowledge. The projects also produced some new insights about CommonKADS domain modelling and about the process of task identification.     

Developing a marketing decision model using a knowledge-based system

This paper describes using a knowledge-based system for developing a marketing decision model. The approach used in this study uses a decision table as a knowledge engineering tool. The decision table is used as a means of representing a set of decision rules to construct a developed marketing decision model. To support the modeling process, Prologa, an existing decision table engineering workbench, is used. The developed marketing decision model is used to determine the entrance time of a new product into market by utilizing knowledge-based systems. Presentation of a new product to the market at the best time will provide an advantage to competing companies and will increase their market share.     

Hardware-Software Partitioning at the Knowledge Level

Hardware-software co-design addresses the development of complex heterogeneous systems looking for the best tradeoffs among the different solutions. The basic idea is to combine the hardware and software design cycles. This article shows how knowledge-based techniques can be used to solve the hardware-software partitioning problem, the co-design task that makes the decision on the best implementation of the different components of a digital system. In particular, a fuzzy-logic-based expert system, SHAPES, has been developed based on the CommonKADS methodology. This tool takes advantage of two important artificial intelligence bases: the use of an expert's knowledge in the decision-making process and the possibility of dealing with imprecise and usually uncertain values by the definition of fuzzy magnitudes.Expert system construction has adopted a knowledge modeling approach, following the knowledge level and knowledge separation principles. This expertise model is the center of the knowledge-based system development. It is based in the problem-solving method Propose and Revise with a previous heuristic classification.     

A knowledge-based intelligent decision support system for national defense budget planning

In this paper, we present a new framework for knowledge-based intelligent decision support systems for developing a national defense budget planning. The planning procedure for and architecture of the national defense budget in Taiwan are discussed in detail. In particular, the theories and techniques of intelligent decision support are used in the yearly practical budget planning process. Based on data in the financial database and knowledge in the knowledge base, we easily adjust the beforehand budget proposal. Furthermore, a knowledge-based intelligent decision support system has been implemented and it collects a series of rules extracted from national defense experts for successful reasoning. By using forward reasoning and knowledge rules, the system can automatically change and regenerate the national defense budget plan immediately. Finally, the empirical functions of the KIDSS system are also addressed.     

Designing intelligent litigation support tools: The IKBALS perspective

In the legal domain, it is rare to find solutions to problems by simply applying algorithms or invoking deductive rules in some knowledge‐based program. Instead, expert practitioners often supplement domain‐specific knowledge with field experience. This type of expertise is often applied in the form of an analogy. This research proposes to combine both reasoning with precedents and reasoning with statutes and regulations in a way that will enhance the statutory interpretation task. This is being attempted through the integration of database and expert system technologies. Case‐based reasoning is being used to model legal precedents while rule‐based reasoning modules are being used to model the legislation and other types of causal knowledge. It is hoped to generalise these findings and to develop a formal methodology for integrating case‐based databases with rule‐based expert systems in the legal domain.     

A framework for knowledge-based control

A framework for knowledge-based control is proposed. The approach presented is suitable for control systems and control support of systems which have no adequate mathematical models. Thus, the control is performed by using knowledge engineering methods rather than pure mathematical control methods. The domain expert's knowledge is assumed to be encoded in the form of simple statements (facts) and special reasoning rules, which form the core of the Knowledge-Based Control System (KBCS). The control system reads the input information, and on the basis of the current state of its knowledge base, together with the application of supplied inference rules updates the knowledge base and performs the required control actions. Moreover, some inference control knowledge, reflecting the expert's way of reasoning, is to be incorporated in the KBCS. The main idea of the system consists of selecting an appropriate set of actions to be executed, with regard to the current state specification and the control goal given. An abstract mathematical model of the control process is formulated and a suitable language for knowledge representation is proposed. The reasoning scheme is discussed and the structure of the control system is outlined. A representative application example is provided.     

VCR: Virtual community recommender using the technology acceptance model and the user’s needs type

A recommender system is a kind of automated and sophisticated decision support system that is needed to provide a personalized solution in a brief form without going through a complicated search process. There have been a substantial number of studies to make recommender systems more accurate and efficient, however, most of them have a common critical limitation – these systems are used as virtual salespeople, rather than as marketing tools. A crucial reason for this phenomenon is that the models suggested by prior studies only focus on a user’s behavioral outcomes without consideration of the embedded procedure. In this study, we propose a novel recommender system based on user’s behavioral model. Our proposed system, labeled VCR—virtual community recommender, recommends optimal virtual communities for an active user by case-based reasoning (CBR) using behavioral factors suggested in the technology acceptance model (TAM) and its extended models. In addition, it refines its recommendation results by considering the user’s needs type at the point of usage. To test the usefulness of our recommendation model, we conducted two-step validation–empirical validation for the collected data set, and practical validation to investigate the actual satisfaction level of users. Experimental results showed that our model outperformed all comparative models from the perspective of user satisfaction.     

Different roles and mutual dependencies of data, information, and knowledge — An AI perspective on their integration

The unclear distinction between data, information, and knowledge has impaired their combination and utilization for the development of integrated systems. There is need for a unified definitional model of data, information, and knowledge based on their roles in computational and cognitive information processing. An attempt to clarify these basic notions is made, and a conceptual framework for integration is suggested by focusing on their different roles and frames of reference within a decision-making process. On this basis, ways of integrating the functionalities of databases, information systems and knowledge-based systems are discussed by taking a knowledge level perspective to the analysis and modeling of systems behaviour. Motivated by recent work in the area of case-based reasoning related to decision support systems, it is further shown that a specific problem solving episode, or case, may be viewed as data, information, or knowledge, depending on its role in decision making and learning from experience. An outline of a case-based system architecture is presented, and used to show that a focus on the retaining and reuse of past cases facilitates a gradual and evolutionary transition from an information system to a knowledge-based system.     

A methodology for designing systems to reason with legal cases using Abstract Dialectical Frameworks

This paper presents a methodology to design and implement programs intended to decide cases, described as sets of factors, according to a theory of a particular domain based on a set of precedent cases relating to that domain. We use Abstract Dialectical Frameworks (ADFs), a recent development in AI knowledge representation, as the central feature of our design method. ADFs will play a role akin to that played by Entity–Relationship models in the design of database systems. First, we explain how the factor hierarchy of the well-known legal reasoning system CATO can be used to instantiate an ADF for the domain of US Trade Secrets. This is intended to demonstrate the suitability of ADFs for expressing the design of legal cased based systems. The method is then applied to two other legal domains often used in the literature of AI and Law. In each domain, the design is provided by the domain analyst expressing the cases in terms of factors organised into an ADF from which an executable program can be implemented in a straightforward way by taking advantage of the closeness of the acceptance conditions of the ADF to components of an executable program. We evaluate the ease of implementation, the performance and efficacy of the resulting program, ease of refinement of the program and the transparency of the reasoning. This evaluation suggests ways in which factor based systems, which are limited by taking as their starting point the representation of cases as sets of factors and so abstracting away the particular facts, can be extended to address open issues in AI and Law by incorporating the case facts to improve the decision, and by considering justification and reasoning using portion of precedents.     

A knowledge-based decision support system for the management of parts and tools in FMS

Flexible manufacturing systems (FMS) are very complex systems with large part, tool, and information flows. The aim of this work is to develop a knowledge-based decision support system (KBDSS) for short-term scheduling in FMS strongly influenced by the tool management concept to provide a significant operational control tool for a wide range of machining cells, where a high level of flexibility is demanded, with benefits of more efficient cell utilization, greater tool flow control, and a dependable way of rapidly adjusting short-term production requirements. Development of a knowledge-based system to support the decision making process is justified by the inability of decision makers to diagnose efficiently many of the malfunctions that arise at machine, cell, and entire system levels during manufacturing. In this context, this paper proposes three knowledge-based models to ease the decision making process: an expert production scheduling system, a knowledge-based tool management decision support systems, and a tool management fault diagnosis system. The entire system has been created in a hierarchical manner and comprises more than 400 rules. The expert system (ES) was implemented in a commercial expert system shell, Knowledge Engineering System (KES) Production System (PS).     

Complementary case-based reasoning and competitive fuzzy cognitive maps for advanced medical decisions

This paper presents a new hybrid modeling methodology suitable for complex decision making processes. It extends previous work on competitive fuzzy cognitive maps for medical decision support systems by complementing them with case based reasoning methods. The synergy of these methodologies is accomplished by a new proposed algorithm that leads to more dependable advanced medical decision support systems that are suitable to handle situations where the decisions are not clearly distinct. The methodology developed here is applied successfully to model and test two decision support systems, one a differential diagnosis problem from the speech pathology area for the diagnosis of language impairments and the other for decision making choices in external beam radiation therapy.     

一个基于神经网络的专家系统

医疗专家系统主要使用基于知识的技术，其中的决策规则和策略来自于人类的专家。把这些知识和各种推理方法结合，可以建立一个模拟专家决策过程的系统。建立这样一个系统，需要经常与专家磋商，以获取专家的知识，因而需要大量的时间和精力。为此，本文提出直接从数据中提取有效的信息，即用神经网络提取隐含在大量数据中对医疗诊断有效的信息，继之与基于规则的知识，各种推理方法相结合，建立一个神经网络专家系统。     

A case-based reasoning approach for building a decision model

A methodology based on case-based reasoning is proposed to build a topological-level influence diagram. It is then applied to a project proposal review process. The formulation of decision problems requires much time and effort, and the resulting model, such as an influence diagram, is applicable only to one specific problem. However, some prior knowledge from the experience in modeling influence diagrams can be utilized to resolve other similar decision problems. The basic idea of case-based reasoning is that humans reuse the problem-solving experience to solve new problems.
In this paper, we suggest case-based decision class analysis (CB-DCA), a methodology based on case-based reasoning, to build an influence diagram. CB-DCA is composed of a case retrieval procedure and an adaptation procedure. Two measures are suggested for the retrieval procedure, one a fitting ratio and the other a garbage ratio. The adaptation procedure is based on decision-analytic knowledge and decision participants' domain-specific knowledge. Our proposed methodology has been applied to an environmental review process in which decision-makers need decision models to decide whether a project proposal is accepted or not. Experimental results show that our methodology for decision class analysis provides decision-makers with robust knowledge-based support.     

Decision support analysis for safety control in complex project environments based on Bayesian Networks

This paper presents a novel and systemic decision support model based on Bayesian Networks (BN) for safety control in dynamic complex project environments, which should go through the following three sections. At first, priori expert knowledge is integrated with training data in model design, aiming to improve the adaptability and practicability of model outcome. Then two indicators, Model Bias and Model Accuracy, are proposed to assess the effectiveness of BN in model validation, ensuring the model predictions are not significantly different from the actual observations. Finally we extend the safety control process to the entire life cycle of risk-prone events in model application, rather than restricted to pre-accident control, but during-construction continuous and post-accident control are included. Adapting its reasoning features, including forward reasoning, importance analysis and background reasoning, decision makers are provided with systematic and effective support for safety control in the overall work process. A frequent safety problem, ground settlement during Wuhan Changjiang Metro Shield Tunnel Construction (WCMSTC), is taken as a case study. Results demonstrate the feasibility of BN model, as well as its application potential. The proposed model can be used by practitioners in the industry as a decision support tool to increase the likelihood of a successful project in complex environments.     

A model for “crisp reasoning” with fuzzy sets

In the setting of a general type of fuzzy algebras, this paper deals with a new theoretic view on the commonsense reasoning, consisting of a kind of Popper's search for conjectures and refutations. It is supposed that the reasoning is done in natural language, but only with nonambiguous precise and imprecise terms, respectively, represented by crisp and fuzzy sets. © 2012 Wiley Periodicals, Inc.     

Decision support for “messy” problems

Problems that are non-quantitative and not bound to a narrow knowledge domain have been served unsatisfactorily by decision support and expert systems. Alternative techniques that address this type of problem are explained here using two key concepts: problem type dependent process support and domain related knowledge. Process support refers to the program steps and the data items useful in finding the solution. Domain related knowledge is knowledge drawn from a specific domain, yet through abstraction applicable to a wider range of problems. Results of preliminary empirical analyses suggest that both concepts are useful.     

Knowledge capitalisation through case bases and knowledge engineering for road safety analysis

New generation knowledge-based systems should be fully integrated into their environment, by exploiting existing information sources, and should be flexible and easily extensible. This article describes the architecture of an organisational memory (OM) for road safety analysis. Starting from the design of a knowledge-based system, we show how we address knowledge capitalisation issues through the building of an OM. We present its main components and describe how knowledge engineering techniques can be exploited to build and enrich it. We then describe the major task that exploits the OM as decision support for site analysis. We also explain how domain knowledge can be exploited and capitalised using case-based reasoning and collaborative work.