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Knowledge Acquisition Module for Conversation Agent

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Mak, P and Kang, BH and Sammut, C and Kadous, W (2004) Knowledge Acquisition Module for Conversation Agent. In: Pacific Knowledge Acquisition Workshop 2004, 09/08/2004 - 10/08/2004, Auckland, New Zealand.

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Abstract

The focus of traditional conversational agents is placed on natural language processing and understanding the needs of the user. These agents are typically implemented for specific domains such that domain knowledge and conversations are built manually. Domain knowledge of these agents are encoded as conversational content which causes problems is magnified by the lack of knowledge acquisition tools and, as a result, agents find it difficult to adapt to different domains and to update existing knowledge bases. The framework proposed in this paper aims to rectify this problem by building a module to handle knowledge acquisition. The module acquires knowledge through a case-based methodology called Ripple Down Rules (RDR); a technique that has been employed successfully across a host of expert systems

Item Type: Conference or Workshop Item (Paper)
Keywords: Conversation Agent, Multiple Classification Ripple Down Rules, MCRDR, Knowledge Acquisition, Learning
Publisher: School of Computing, Unviersity of Tasmania
Page Range: pp. 54-62
Date Deposited: 05 Oct 2004
Last Modified: 18 Nov 2014 03:10
URI: http://eprints.utas.edu.au/id/eprint/83
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