Explanation in Second Generation Expert Systems

Swartout, William; Moore, Johanna D.

doi:10.1007/978-3-642-77927-5_24

Cited by 101 publications

(71 citation statements)

References 25 publications

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“…Even if expert systems of first generation are now abandoned, there are important results that we can yet reuse, such as the base for new explanations proposed in [13,14]. Thus, beyond the need to make context explicit, first in the reasoning to explain, and, second, in the explanation generation, the most challenging finding is that lines of reasoning and explanation are distinct and interact together, the line of explanation being able to modify the line of reasoning [1].…”

Section: Explanations In Kbssmentioning

confidence: 99%

Explaining for developing a shared context in collaborative design

Brézillon

2009

2009 13th International Conference on Computer Supported Cooperative Work in Design

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Section: Explanations In Kbssmentioning

confidence: 99%

Explaining for developing a shared context in collaborative design

Brézillon

2009

2009 13th International Conference on Computer Supported Cooperative Work in Design

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“…The problem of generating explanations of machine reasoning is not new; the literature review in Swartout and Moore [33] points out that researchers studying explanation for expert systems quickly agreed that the data structures of the expert system had to be designed with explanation in mind. Swartout and Moore advocated building a high-level knowledge base containing facts about the domain and problem-solving strategies, and using an automatic program writer to build an expert system from this specification.…”

Section: Domain-independent Architecturementioning

confidence: 99%

Teaching Negotiation Skills through Practice and Reflection with Virtual Humans

et al. 2006

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Public reporting burden for the collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Although the representation of physical environments and behaviors will continue to play an important role in simulation-based training, an emerging challenge is the representation of virtual humans with rich mental models (e.g., including emotions, trust) that interact through conversational as well as physical behaviors. The motivation for such simulations is training soft skills such as leadership, cultural awareness, and negotiation, where the majority of actions are conversational, and the problem solving involves consideration of the emotions, attitudes, and desires of others.The educational power of such simulations can be enhanced by the integration of an intelligent tutoring system to support learners' understanding of the effect of their actions on virtual humans and how they might improve their performance. In this paper, we discuss our efforts to build such virtual humans, along with an accompanying intelligent tutor, for the domain of negotiation and cultural awareness.

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“…At the turn of the century, we find the issue discussed again in the context of knowledge-based systems [10,11]. Recently, we can see a renewed focus in CBR on this track of research.…”

Section: Why Bother To Explain?mentioning

confidence: 99%

Mapping Goals and Kinds of Explanations to the Knowledge Containers of Case-Based Reasoning Systems

Roth–Berghofer

Cassens

2005

Case-Based Reasoning Research and Development

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Abstract. Research on explanation in Case-Based Reasoning (CBR) is a topic that gains momentum. In this context, fundamental issues on what are and to which end do we use explanations have to be reconsidered. This article presents a prelimenary outline of the combination of two recently proposed classifications of explanations based on the type of the explanation itself and user goals which should be fulfilled. Further on, the contribution of the different knowledge containers for modeling the necessary knowledge is examined.

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Explanation in Second Generation Expert Systems

Cited by 101 publications

References 25 publications

Explaining for developing a shared context in collaborative design

Explaining for developing a shared context in collaborative design

Teaching Negotiation Skills through Practice and Reflection with Virtual Humans

Mapping Goals and Kinds of Explanations to the Knowledge Containers of Case-Based Reasoning Systems

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