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Approaches to Knowledge Acquisition: The Instructable
- Production System Project”, Proceedings, First AAAI National Conference on Artificial Intelligence
, 1980
"... Progress in building systems that acquire knowledge from a variety of sources depends on determining certain functional requirements and ways for them to be met. Experiments have been performed with learning systems having a variety of functional components. The results of these experiments have bro ..."
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Cited by 3 (0 self)
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; reformulation (mapping) of knowledge; evaluation of behavior; and compilation to achieve efficiency and automaticity. A number of approaches to knowledge acquisition tried within the Instructable Production System (IPS) Project are sketched.* 1. The lnst ructable Production System Project The IPS project [6
I WHAT IS KNOWLEDGE ACQUISITION?
"... Expert systems are generally described by a mixture of terms that confuse implementation language with knowledge structure and the search process. This confusion makes it difficult to analyze new problems and to derive a set of knowledge ngineering principles. A rigorous, logical description of expe ..."
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framework for knowledge acquisition, particularly in the early stages for organizing the expert's vocabulary and decomposing problems.
Knowledge Acquisition for Life Counseling
"... Abstract. In this paper, we explain how highly unstructured domain knowledge can be acquired and integrated in a case-based reasoning system. We apply our approach to the life counseling domain. We introduce the two steps of our knowledge acquisition approach in such unstructured domains. The first ..."
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Abstract. In this paper, we explain how highly unstructured domain knowledge can be acquired and integrated in a case-based reasoning system. We apply our approach to the life counseling domain. We introduce the two steps of our knowledge acquisition approach in such unstructured domains. The first
Goal-Directed Knowledge Acquisition
"... Agents with incomplete knowledge of their actions can either plan around the incompleteness, ask questions of a domain expert, or learn through trial and error. We present and evaluate several approaches to formulating plans under incomplete information, using the plans to identify relevant (goaldir ..."
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(goaldirected) questions, and interleaving acting, planning, and question asking. We show that goal-directed knowledge acquisition leads to fewer questions and lower overall planning and re-planning time than naive approaches that ask many questions, or learn by trial and error. Moreover, we show
Knowledge Acquisition for Content Selection
- In Proceedings of the 6th European Workshop on Natural Language Generation
, 1997
"... An important part of building a natural-language generation (NLG) system is knowledge acquisition, that is deciding on the specific schemas, plans, grammar rules, and so forth that should be used in the NLG system. We discuss some experiments we have performed with KA for content-selection rules, in ..."
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Cited by 8 (1 self)
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An important part of building a natural-language generation (NLG) system is knowledge acquisition, that is deciding on the specific schemas, plans, grammar rules, and so forth that should be used in the NLG system. We discuss some experiments we have performed with KA for content-selection rules
The Knowledge Acquisition and Representation Language KARL
, 1995
"... The Knowledge Acquisition and Representation Language (KARL) combines a description of a knowledge-based system at the conceptual level (a so-called model of expertise) with a description at a formal and executable level. Thus, KARL allows the precise and unique specification of the functionality of ..."
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Cited by 86 (36 self)
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The Knowledge Acquisition and Representation Language (KARL) combines a description of a knowledge-based system at the conceptual level (a so-called model of expertise) with a description at a formal and executable level. Thus, KARL allows the precise and unique specification of the functionality
Temporal Knowledge Acquisition and Modeling
"... Abstract. The objectives of this paper are to present, describe, and explain the foundations and the functionalities of a temporal knowledge acquisition and modeling solution workflow, which aims at acquiring temporal knowledge from texts in order to populate a constrained object model. We are using ..."
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Cited by 1 (1 self)
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Abstract. The objectives of this paper are to present, describe, and explain the foundations and the functionalities of a temporal knowledge acquisition and modeling solution workflow, which aims at acquiring temporal knowledge from texts in order to populate a constrained object model. We
Knowledge Acquisition for Intelligent Tutoring Systems
, 1990
"... : Intelligent Tutoring Systems require three different kinds of knowledge: the domain to be taught, techniques of teaching, and characteristics of the student. As such it is worth considering the extent to which knowledge acquisition techniques for conventional expert systems can be applied, and in ..."
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Cited by 5 (3 self)
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: Intelligent Tutoring Systems require three different kinds of knowledge: the domain to be taught, techniques of teaching, and characteristics of the student. As such it is worth considering the extent to which knowledge acquisition techniques for conventional expert systems can be applied
Knowledge Acquisition for Knowledge Management
"... this paper, we propose a set of techniques to largely automate the process of KA, by using technologies based on Information Extraction (IE) , Information Retrieval and Natural Language Processing. We aim to reduce all the impeding factors mention above and thereby contribute to the wider utility of ..."
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Cited by 4 (0 self)
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of the knowledge management tools. In particular we intend to reduce the introspection of knowledge engineers or the extended elicitations of knowledge from experts by extensive textual analysis using a variety of methods and tools, as texts are largely available and in them -- we believe -- lies most
Man-machine collaboration for knowledge acquisition
, 1992
"... Both machine learning and knowledge elicitation from human experts have unique strengths and weaknesses. Man-machine collaboration for knowledge acquisition allows both knowledge acquisition techniques to be employed handin-hand. The strengths of each can alleviate the otherÕs weaknesses. This has t ..."
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Cited by 8 (7 self)
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Both machine learning and knowledge elicitation from human experts have unique strengths and weaknesses. Man-machine collaboration for knowledge acquisition allows both knowledge acquisition techniques to be employed handin-hand. The strengths of each can alleviate the otherÕs weaknesses. This has
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503,829
