An intelligent problem solving environment for designing explanation models and for diagnostic reasoning in probabilistic domains

Abstract. We describe a new knowledge acquisition tool that enabled us to develop a dialog system recommending software design patterns by asking critical questions. This assistance system is based on interviews with experts. For the interviews we adopted the repertory grid method and integrated formal concept analysis. The repertory grid method stimulates the generation of common and differentiating attributes for a given set of objects. Using formal concept analysis we can control the repertory grid procedure, minimize the required expert judgements and build an abstraction based hierarchy of design patterns, even from the judgements of different experts. Based on the acquired knowledge we semiautomatically generate a Bayesian Belief Network (BBN), that is used to conduct dialogs with users to suggest a suitable design pattern for their individual problem situation. Integrating these different methods into our knowledge acquisition tool KARaCAs enables us to support the entire knowledge acquisition and engineering process. We used KARaCAs with three design pattern experts and derived approximately 130 attributes for 23 design patterns. Using formal concept analysis we merged the three lattices and condensed them to approximately 80 common attributes.

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“…[24][25][26][27]). A BBN models this knowledge as a directed acyclic graph that represents a probability distribution.…”

Section: Knowledge Representation With Bayesian Belief Networkmentioning

confidence: 99%

KARaCAs: Knowledge Acquisition with Repertory Grids and Formal Concept Analysis for Dialog System Construction

Garbe

Janssen

Möbus

et al. 2006

Managing Knowledge in a World of Networks

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“…We conclude this section on static explanation by mentioning the system MEDICUS, developed by Folckers et al (Folckers et al, 1996;Shcroder, 1996) as a tool for the construction of explanation models in which the knowledge is complex and uncertain, as in medicine. The main contribution is that it is a shell in which the user can develop a Bayesian network for representing a certain domain, independently of his/her level of knowledge about probability.…”

Section: Assisted Construction Of Medical Bayesian Networkmentioning

confidence: 99%

A review of explanation methods for Bayesian networks

Lacave

Díez

2002

The Knowledge Engineering Review

183

132

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“…MEDICUS [4], [9] is designed as a problem solving tool for modeling uncertain domains. Diagnosis in domains of complex, fragile and uncertain knowledge is quite a difficult reasoning and problem solving task.…”

Section: Medicusmentioning

confidence: 99%

Evolution of the Hypotheses Testing Approach in Intelligent Problem Solving Environments

Willms

Möbus

2000

Intelligent Tutoring Systems

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Abstract.In this paper, we compare different realizations of the hypotheses testing approach in the IPSEs (Intelligent Problem Solving Environments) ABSYNT, PETRI-HELP and MEDICUS and introduce the changes necessary to transfer the hypotheses testing approach to the real world domain of patent applications. Patent-IT is the first IPSE to overcome the limiting aspects of fixed specifications and a black box oracle. Hypothesis Testing and the IPSE ApproachThe hypotheses testing approach is a core concept of what we call intelligent problem solving environments (IPSE) ([8], [9]) and also gives a key qualification having a beneficial influence on the student's knowledge acquisition process. The learner acquires knowledge by actively exploring a domain, creating solution proposals for problems, testing hypotheses about their correctness, during which the system analyzes the proposals and provides help and explanations, making use of an oracle or an expert knowledge base. The IPSEs we developed initially had some limiting aspects such as fixed specifications and a black box oracle. We now present the IPSE Patent-IT, which does not exhibit these limitations. This has become necessary due to the rather demanding domain of inventions and patents. The novice should learn how to transform an inventive idea into a legal patent. The IPSE approach is psychologically based on the ISP-DL theory of knowledge acquisition and problem solving [8], which is influenced by theoretical assumptions of van Lehn [13], Newell [11], Anderson [1] and Gollwitzer [5]. It briefly states that new knowledge is acquired as a result of problem solving by applying weak heuristics in response to impasses. Furthermore, existing knowledge is optimized if applied successfully. The learner encounters four distinct problem solving phases namely deliberation, resulting in marking a goal as an intention, planning how to satisfy the intention, execution of the plan and evaluation of the result. Several design principles for IPSEs [8] could be drawn from the following assumptions:• The system should not interrupt the learner but offer help on demand. According to the theory, the learner will look for and appreciate help at an impasse.• Feedback and help information should be available on request at any time, taking the actual problem solving phase of the learner into account.

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An intelligent problem solving environment for designing explanation models and for diagnostic reasoning in probabilistic domains

Cited by 6 publications

References 17 publications

KARaCAs: Knowledge Acquisition with Repertory Grids and Formal Concept Analysis for Dialog System Construction

KARaCAs: Knowledge Acquisition with Repertory Grids and Formal Concept Analysis for Dialog System Construction

A review of explanation methods for Bayesian networks

Evolution of the Hypotheses Testing Approach in Intelligent Problem Solving Environments

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