Models of Expertise in Knowledge Acquisition

Breuker, Joost; Wielinga, B.J.

doi:10.1016/b978-0-444-87321-7.50015-6

Cited by 85 publications

(40 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Let us briefly recall that KADS [11,10,42,49] advises the knowledge engineer to build a model of the expertise, this model being described with four levels: the levels domain, inference, task and strategy. In order to help the knowledge engineer to build the expertise model corresponding to his application, KADS offers him a library of interpretation models: such models offer predefined descriptions of the levels inference, task and strategy, for different problem classes.…”

Section: Topoi and Acquisition Methodsmentioning

confidence: 99%

Acquisition of gradual knowledge

Dieng

Corby

Lapalut

1993

Knowledge Acquisition for Knowledge-Based Systems

View full text Add to dashboard Cite

Abstract. Topoi are gradual inference rules, often used by experts in several problem classes: they can be exploited at various phases of a knowledge-based system life cycle. They can be studied at the two levels distinguished by Newelh the knowledge level and the symbol level. Some knowledge elicitation techniques such as rating grids and some knowledge acquisition methods such as KADS and KOD can be exploited in order to facilitate the acquisition of topoi. At the symbol level, different representations and implementations of topoi can be proposed and topoi can be formalized through several qualitative physics formalisms. IntroductionThe notion of topoi 1 stems from Anscombre and Ducrot's argumentation theory [2]. A topos is a gradual inference rule such as "The more P, the more Q", "The less P, the less Q", ~'The more P, the less Q" or "The less P, the more Q", where P and Q are properties. The first two topoi are called positive topoi and the last two negative topoi. Several researchers studied the interest of such gradual knowledge for argumentation in linguistics [12,41] and for artificial intelligence: in particular, in the framework of knowledge-based systems (KBS), some researchers studied thoroughly the use of topoi for knowledge acquisition [23,27] Let us give a brief description of such research. In [12,41], the authors propose a topoi-based theory of argumentation in natural language. Topoi are seen as gradual rules that are applied to some elements of the signification of a phrase, and help to determine the argumentative orientation of the concerned utterance. A speaker chooses the topoi he supposes shared by his interlocutors. Topoi play an important role for representing expertise. In [41], the author distinguishes descriptive topoi and heuristic topoi and exploits the notion of topoi for explanations of an expert system. In [40], the author relies on the possibility theory and on fuzzy sets in order to reason on gradual rules. The dynamic gradual behaviour of a KBS is represented through a topoi base in [18]. The notion of conditional topoi is introduced in order to avoid conflicts between apparently contradictory topoi. The topoi base is exploited for specification and validation 1 The term "topoi" was chosen as a tribute to Aristotle's topoi.

show abstract

Section: Topoi and Acquisition Methodsmentioning

confidence: 99%

Acquisition of gradual knowledge

Dieng

Corby

Lapalut

1993

Knowledge Acquisition for Knowledge-Based Systems

View full text Add to dashboard Cite

show abstract

“…In recent years, many methodologies have therefore been proposed, providing systematic methods to be followed in building an expert system. Examples of such methodologies are KADS [3] and KEATS [15,16]. Some of these methodologies include a set of software tools which help the knowledge engineer in building a specific application, mainly by assisting in the analysis of the problem domain.…”

Section: Development Of the Hepar System 21 Knowledge Acquisition Anmentioning

confidence: 99%

Refinement of the HEPAR expert system: tools and techniques

Lucas

1994

Artificial Intelligence in Medicine

View full text Add to dashboard Cite

Methods and tools for the static and dynamic verification and validation of software systems are common place in the field of software engineering. In the field of expert systems, where it is more difficult to ensure that a system meets the specifications and expectations than in traditional software engineering, such tools are generally not available. In this paper, the need for more support of the development process by method and tools is illustrated by the approach taken in building the HEPAR system, a rule-based expert system that can be used as a supportive tool in the diagnosis of disorders of the liver and biliary tract. At a certain stage in the development of this system an incremental development methodology has been adopted, in which implementation of parts of the expert system was followed by dynamic validation. For this purpose, a collection of software tools were implemented as extensions to a rule-based expert-system shell. These tools provide valuable information about the effects of modification of the HEPAR knowledge base, and indicate places in the knowledge base for refinement. It is believed that similar software tools may prove helpful in the development of other expert systems as well.

show abstract

“…The model structure and building block perspective of KADS plays different roles in the approaches of [Fensel], Pfeifer et al ([Rothenfluh]), [Shadbolt], and 4 For an introduction to the KADS terminology, cf. Janetzko and Strube ([Janet92]).…”

Section: Biases Of the Knowledge Engineermentioning

confidence: 99%