scholarly journals A comparison of languages which operationalize and formalize KADS models of expertise

1994 ◽  
Vol 9 (2) ◽  
pp. 105-146 ◽  
Author(s):  
Dieter Fensel ◽  
Frank van Harmelen
Keyword(s):  
Problem Solving ◽  
Rapid Prototyping ◽  
Conceptual Model ◽  
Model Evaluation ◽  
Conceptual Models ◽  
Knowledge Engineering ◽  
Future Research ◽  
Knowledge Based Systems ◽  
Research Issues ◽  
Knowledge Based

AbstractIn the field of knowledge engineering, dissatisfaction with therapid-prototypingapproach has led to a number of more principled methodologies for the contruction of knowledge-based systems. Instead of immediately implementing the gathered and interpreted knowledge in a given implementation formalism according to the rapid-prototyping approach, many such methodologies centre around the notion of a conceptual model: an abstract, implementation independent description of the relevant problem solving expertise. A conceptual model should describe the task which is solved by the system and the knowledge which is required by it. Although such conceptual models more precisely, and operationally as a means for model evaluation. In this paper, we study a number of such formal and operational languages for specifying conceptual models. To enable a meaningful comparison of such languages, we focus on languages which are all aimed at the same underlying conceptual model, namely that from the KADS method for building KBS. We describe eight formal languages for KADS models of expertise, and compare these languages with respect to their modelling primitives, their semantics, their implementations and their applications, Future research issues in the area of formal and operational specification languages for KBS are identified as the result of studying these languages. The paper also contains an extensive bibliography of research in this area.

Verification and validation of knowledge-based systems with formal specifications

1995 ◽  
Vol 10 (4) ◽  
pp. 331-343 ◽  
Author(s):  
Pedro Meseguer ◽  
Alun D. Preece
Keyword(s):  
Conceptual Model ◽  
Formal Specification ◽  
Conceptual Models ◽  
Verification And Validation ◽  
Formal Specifications ◽  
Knowledge Based Systems ◽  
Knowledge Based ◽  
Prior Construction ◽  
Specification Techniques

AbstractThis paper examines how formal specification techniques can support the verification and validation (V&V) of knowledge-based systems. Formal specification techniques provide levels of description which support both verification and validation, and V&V techniques feed back to assist the development of the specifications. Developing a formal specification for a system requires the prior construction of a conceptual model for the intended system. Many elements of this conceptual model can be effectively used to support V&V. Using these elements, the V&V process becomes deeper and more elaborate, and it produces results of a better quality compared with the V&V activities which can be performed on systems developed without conceptual models. However, we note that there are concerns in using formal specification techniques for V&V, not least being the effort involved in creating the specifications.

scholarly journals ISSUES WITH META-KNOWLEDGE

2000 ◽  
Vol 10 (04) ◽  
pp. 549-555 ◽  
Author(s):  
TIM MENZIES ◽  
KLAUS-DIETER ALTHOFF ◽  
YANNIS KALFOGLOU ◽  
ENRICO MOTTA
Keyword(s):  
Problem Solving ◽  
Knowledge Engineering ◽  
Knowledge Based Systems ◽  
Reusable Components ◽  
Knowledge Based ◽  
Lively Debate

At the SEKE'99 conference, knowledge engineering researchers held a panel on the merits of meta-knowledge (i.e. problem solving methods and ontologies) for the development of knowledge-based systems. The original panel was framed as a debate on the merits of meta-knowledge for knowledge maintenance [21]. However, the debate quickly expanded. In the end, we were really discussing the merits of different technologies for the specification of reusable components for KBS. In this brief article we record some of the lively debate from that panel and the email exchanges it generated.

Task Ontology-Based Human-Computer Interaction

2009 ◽  
pp. 950-960
Author(s):  
Kazuhisa Seta
Keyword(s):  
Problem Solving ◽  
Real World ◽  
Practical Knowledge ◽  
Building Blocks ◽  
Research Field ◽  
Knowledge Based Systems ◽  
Engineering Research ◽  
Knowledge Based ◽  
Task Ontology ◽  
Problem Solving Task

In ontological engineering research field, the concept of “task ontology” is well-known as a useful technology to systemize and accumulate the knowledge to perform problem-solving tasks (e.g., diagnosis, design, scheduling, and so on). A task ontology refers to a system of a vocabulary/ concepts used as building blocks to perform a problem-solving task in a machine readable manner, so that the system and humans can collaboratively solve a problem based on it. The concept of task ontology was proposed by Mizoguchi (Mizoguchi, Tijerino, & Ikeda, 1992, 1995) and its validity is substantiated by development of many practical knowledge-based systems (Hori & Yoshida, 1998; Ikeda, Seta, & Mizoguchi, 1997; Izumi &Yamaguchi, 2002; Schreiber et al., 2000; Seta, Ikeda, Kakusho, & Mizoguchi, 1997). He stated: …task ontology characterizes the computational architecture of a knowledge-based system which performs a task. The idea of task ontology which serves as a system of the vocabulary/concepts used as building blocks for knowledge-based systems might provide an effective methodology and vocabulary for both analyzing and synthesizing knowledge-based systems. It is useful for describing inherent problem-solving structure of the existing tasks domain-independently. It is obtained by analyzing task structures of real world problem. ... The ultimate goal of task ontology research is to provide a theory of all the vocabulary/concepts necessary for building a model of human problem solving processes. (Mizoguchi, 2003) We can also recognize task ontology as a static user model (Seta et al., 1997), which captures the meaning of problem-solving processes, that is, the input/output relation of each activity in a problem-solving task and its effects on the real world as well as on the humans’ mind.

Task Ontology-Based Human-Computer Interaction

2006 ◽  
pp. 588-596
Author(s):  
Kazuhisa Seta
Keyword(s):  
Problem Solving ◽  
Real World ◽  
Practical Knowledge ◽  
Building Blocks ◽  
Research Field ◽  
Knowledge Based Systems ◽  
Engineering Research ◽  
Knowledge Based ◽  
Task Ontology ◽  
Problem Solving Task

In ontological engineering research field, the concept of “task ontology” is well-known as a useful technology to systemize and accumulate the knowledge to perform problem-solving tasks (e.g., diagnosis, design, scheduling, and so on). A task ontology refers to a system of a vocabulary/concepts used as building blocks to perform a problem-solving task in a machine readable manner, so that the system and humans can collaboratively solve a problem based on it. The concept of task ontology was proposed by Mizoguchi (Mizoguchi, Tijerino, & Ikeda, 1992, 1995) and its validity is substantiated by development of many practical knowledge-based systems (Hori & Yoshida, 1998; Ikeda, Seta, & Mizoguchi, 1997; Izumi &Yamaguchi, 2002; Schreiber et al., 2000; Seta, Ikeda, Kakusho, & Mizoguchi, 1997). He stated: …task ontology characterizes the computational architecture of a knowledge-based system which performs a task. The idea of task ontology which serves as a system of the vocabulary/concepts used as building blocks for knowledge-based systems might provide an effective methodology and vocabulary for both analyzing and synthesizing knowledge-based systems. It is useful for describing inherent problem-solving structure of the existing tasks domain-independently. It is obtained by analyzing task structures of real world problem. ... The ultimate goal of task ontology research is to provide a theory of all the vocabulary/concepts necessary for building a model of human problem solving processes. (Mizoguchi, 2003) We can also recognize task ontology as a static user model (Seta et al., 1997), which captures the meaning of problem-solving processes, that is, the input/output relation of each activity in a problem-solving task and its effects on the real world as well as on the humans’ mind.

Reconstructing Medical Problem Solving Competence: MACCORD

1993 ◽  
Vol 32 (04) ◽  
pp. 326-338
Author(s):  
B. Petkoff ◽  
H. Mannebach ◽  
S. Kirkby ◽  
D. Kraus
Keyword(s):  
Problem Solving ◽  
Clinical Medicine ◽  
Medical Knowledge ◽  
Medical Problem ◽  
Knowledge Based Systems ◽  
Levels Of Abstraction ◽  
Knowledge Based ◽  
Methodological Principles ◽  
Problem Solving Behavior ◽  
Medical Reasoning

AbstractThe building of medical knowledge-based systems involves the reconstruction of methodological principles and structures within the various subdomains of medicine. ACCORD is a general methodology of knowledge-based systems, and MACCORD its application to medicine. MACCORD represents the problem solving behavior of the medical expert in terms of various types of medical reasoning and at various levels of abstraction. With MACCORD the epistemic and cognitive processes in clinical medicine can be described in formal terminology, covering the entire diversity of medical reasoning. MACCORD is close enough to formalization to make a significant contribution to the fields of medical knowledge acquisition, medical didactics and the analysis and application of medical problem solving methods.

Altering the SightPlan knowledge-based systems

1992 ◽  
Vol 6 (1) ◽  
pp. 19-37 ◽  
Author(s):  
I. D. Tommelein ◽  
B. Hayes-Roth ◽  
R. E. Levitt
Keyword(s):  
Problem Solving ◽  
Knowledge Bases ◽  
Knowledge Based Systems ◽  
Knowledge Domain ◽  
Task Knowledge ◽  
Knowledge Based ◽  
Site Layout ◽  
Different Types ◽  
Control Knowledge ◽  
Construction Site Layout

SightPlan refers to several knowledge-based systems that address construction site layout. Five different versions were implemented and their components of expertise are described here. These systems are alterations of one another, differing either in the problems they solve, the problem-solving methods they apply, or the tasks they address. Because they share either control knowledge, domain concepts, or heuristics, and such knowledge is implemented in well-defined modular knowledge bases, these systems could easily re-use parts of one another. Experiments like those presented here may clarify the role played by different types of knowledge during problem solving, enabling researchers to gain a broader understanding of the generality of the domain and task knowledge that is embedded in KBSs and of the power of their systems.

Explaining reasoning: an overview of explanation in knowledge-based systems

1991 ◽  
Vol 6 (1) ◽  
pp. 1-19 ◽  
Author(s):  
Richard W. Southwick
Keyword(s):  
Problem Solving ◽  
Annotated Bibliography ◽  
User Requirements ◽  
Knowledge Based Systems ◽  
Reasoning Process ◽  
Knowledge Based ◽  
Problem Solving Process ◽  
System Context

AbstractThere seems to be general agreement amongst those involved in KBS research that in order to be useful, a system must be able to explain its reasoning to a user. This paper reviews the development of explanation facilities in knowledge-based systems. It differentiates between explanation as a problem-solving process, and that which explains a reasoning process. This review concentrates on the latter, identifying and giving examples of three categories of reasoning explanation.We then look at user requirements for explanation. What makes an explanation useful depends on the expectations of a user, which in turn depends on such issues as user background and system context. Several techniques are examined that have been applied to the problem of producing explanations that are appropriately structured and conveyed.Finally, we discuss some of the work that has been done in describing theories of human discourse and explanation, and some issues that will become increasingly important for future explanation systems.An extensive annotated bibliography is provided.

Evaluation of workbenches which support the CommonKADS methodology

1995 ◽  
Vol 10 (3) ◽  
pp. 269-300 ◽  
Author(s):  
John K. C. Kingston ◽  
Jim G. Doheny ◽  
Ian M. Filby
Keyword(s):  
Knowledge Engineering ◽  
Good Knowledge ◽  
Knowledge Based Systems ◽  
Considerable Effort ◽  
Case Tools ◽  
Knowledge Based ◽  
Specific Support ◽  
Modelling Techniques ◽  
Software Engineers ◽  
Automated Support

AbstractThe KADS methodology and its successor, CommonKADS, have gained a reputation for being useful approaches to building knowledge-based systems in a manner which is both systematic and well documented. However, these methods require considerable effort to use them completely. It has been suggested that automated support for KADS or CommonKADS users, in the form of “knowledge engineering workbenches”, could be very useful. These tools would provide computerised assistance to knowledge engineers in organising and representing knowledge, in a similar fashion to the support which CASE tools provide for software engineers. To provide support for KADS or CommonKADS, the workbenches should provide specific support for the modelling techniques recommended by these methods, which are very detailed in the representation and analysis stages of knowledge engineering. A good knowledge engineering workbench should also be easy to use, should be robust and reliable, and should generate output in a presentable format.This paper reports on an evaluation of two commercially available workbenches for supporting the KADS approach: KADS Tool from ILOG and Open KADS Tool from Bull. This evaluation was carried out by AIAI as part of the CATALYST project, funded by the European Community's ESSI programme, which aimed to introduce CommonKADS to two technology-oriented companies. Information is also presented on two other workbenches: the CommonKADS workbench (which will soon become commercially available) and the VITAL workbench. The results show various strengths and weaknesses in each tool.

scholarly journals Associative/Dissociative Cognitive Strategies in Sustained Physical Activity: Literature Review and Proposal for a Mindfulness-Based Conceptual Model

2010 ◽  
Vol 24 (2) ◽  
pp. 127-156 ◽  
Author(s):  
Paul Salmon ◽  
Scott Hanneman ◽  
Brandon Harwood
Keyword(s):  
Physical Activity ◽  
Clinical Practice ◽  
Conceptual Model ◽  
Sustained Attention ◽  
Athletic Performance ◽  
Conceptual Models ◽  
Cognitive Activity ◽  
Cognitive Strategies ◽  
Future Research ◽  
Sustained Performance

We reviewed and summarize the extant literature on associative/dissociative cognitive strategies used by athletes and others in circumstances necessitating periods of sustained attention. This review covers studies published since a prior publication by Masters and Ogles (1998), and, in keeping with their approach, offers a methodological critique of the literature. We conclude that the distinction between associative and dissociative strategies has outlived its usefulness since initially proposed in an earlier era of ground-breaking research by Morgan and Pollock (1977) that was influenced to some extent by psychodynamic thinking. In recent years there has been an evolutionary shift in concepts of sustained attention toward mindfulness—moment-by-moment attention—that has had a significant impact on conceptual models and clinical practice in diverse areas including stress management, psychotherapy, and athletic performance. We propose that future research on cognitive activity in sustained performance settings be embedded in a mindfulness-based conceptual model.

Future Research Directions of Software Engineering and Knowledge Engineering

2015 ◽  
Vol 25 (02) ◽  
pp. 415-421 ◽  
Author(s):  
Haiping Xu
Keyword(s):  
Software Engineering ◽  
Development Process ◽  
Knowledge Engineering ◽  
Future Research ◽  
Software Systems ◽  
Research Directions ◽  
Knowledge Based ◽  
Intelligent Software ◽  
Current Trends ◽  
Future Research Directions

Software Engineering (SE) and Knowledge Engineering (KE) are closely related disciplines with goals of turning the development process of software systems and knowledge-based systems, respectively, into engineering disciplines. In particular, they together can provide systematic approaches for engineering intelligent software systems more efficiently and cost-effectively. As there is a large overlap between the two disciplines, the interplay is vital for both to be successful. In this paper, we divide the intersection of SE and KE into three subareas, namely Knowledge-Supported Software Engineering (KSSE), Engineering Knowledge as a Software (EKaaS), and Intelligent Software System Engineering (ISSE). For each subarea, we describe the challenges along with the current trends, and predict the future research directions that may have the most potential for success.

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