A knowledge acquisition program for expert systems based on personal construct psychology

“…The basic task is to establish a sound, efficient, accurate and comprehensive knowledge base to meet the expert system's requirements for problem solving (Li et al 2015, Walsh 2015. Many techniques have been developed for knowledge acquisition from domain experts (Boose 1985, Leung et al 2006, Marcus 2013, Tudorache et al 2013. In this study, we analyzed and summarized the body of knowledge by conducting literature reviews and interviewing experts by a questionnaire to analyze diseases ( Fig.…”

Section: Knowledge Acquisition and Representationmentioning

confidence: 99%

Horse-Expert: An aided expert system for diagnosing horse diseases

Qin¹,

Xiao²,

Gao³

et al. 2016

Polish Journal of Veterinary Sciences

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In contrast to the rapid development of the horse husbandry in China, the ability of horse veterinarians to diagnose diseases has not been improved and only a few domain experts have considerable expertise. At present, many expert systems have been developed for diseases diagnosis, but few for horse diseases diagnosis have been studied in depth. This paper presents the design and development of a computer-aided expert system for diagnosing horse diseases. We suggest an approach for diagnosis of horse diseases based on the analysis of diagnostic characteristics and the experiential knowledge of domain experts. It is based on using evidence-weighted uncertainty reasoning theory, which is a combination of evidence theory and an uncertainty pass algorithm of confidence factors. It enables drawing of inferences with atypical clinical signs and the uncertainty of the user's subjective understanding. It reduces the influence of subjective factors on diagnostic accuracy. The system utilizes a user friendly interface for users and requests a confidence factor from users when feedback is given to the system. Horse-Expert combines the confidence factors with weight factors assigned to clinical signs by experts during the knowledge acquisition process to make diagnostic conclusions. The system can diagnose 91 common horse diseases, and provides suggestions for appropriate treatment options. In addition, users can check the medical record through statistical charts. The system has been tested in seven demonstration areas of Xinjiang province in northwestern China. By constantly maintaining and updating the knowledge base, the system has potential application in veterinary practice.

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“…After giving concrete examples of topoi elicited thanks to actual interviews of experts, we will study thoroughly the exploitation of rating grids for topoi elicitation, as this technique is frequently used in knowledge acquisition tools such as ETS [5,6], KSS0 [43] Or AQUINAS [7,8].…”

Section: Knowledge Elicitation Techniquesmentioning

confidence: 99%

“…However the description presented below does not correspond to the pure repertory grids, as described in Kelly's book, but rather to the pragmatic interpretation adopted by knowledge acquisition tools such as ETS [5,6].…”

Section: " "If There Is a Punctual Undressing And If The Roadway Is Bmentioning

confidence: 99%

Acquisition of gradual knowledge

Dieng

Corby

Lapalut

1993

Knowledge Acquisition for Knowledge-Based Systems

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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.

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A knowledge acquisition program for expert systems based on personal construct psychology

Cited by 179 publications

References 2 publications

Knowledge Elicitation of Recognition-Primed Decision Making

Knowledge Elicitation of Recognition-Primed Decision Making

Horse-Expert: An aided expert system for diagnosing horse diseases

Acquisition of gradual knowledge

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