By Smith, A.; Whalen, J.; Reitmann, D.; Barnard, E.; Moldauer, P.; deVilliers, J.; Argonne National Lab.; United States. Dept. of Energy. Office of Scientific and Technical Information
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It may learn from a repository of information (such as a database) or it may learn from its own experience. Building a practical autonomous learning agent that can acquire and update its knowledge by itself is not yet practical; we do not yet understand enough about the cognitive process of learning. Therefore, a more practical approach is to develop an interactive learning agent that can interact with an expert. Such an agent can perform most of the functions of the knowledge engineer. It allows the expert to communicate expertise in a way familiar to himlher and is responsible for building, updating and reorganizing the knowledge base.
Also, importing concepts and features from previously developed ontologies, although very appealing is actually quite hard to accomplish. We are therefore planning to develop methods where the modeling process and the agent provide more guidance in identifying the knowledge pieces to import. Finally, we need to develop a more powerful and natural approach to hint specification by the expert. The current types of allowable hints do not constrain the search for explanations enough, and some of them are not very intuitive for the expert.
Problem-solving models for generation of task-specific knowledge acquisition tools. In Cuena J. ) Knowledge-Oriented Software Design, Elsevier, Amsterdam. , and Hendler, J. 1997. Efficient Management of Very Large Ontologies. In Proc. , AAAI Press. Tecuci, G. 1998. Building Intelligent Agents: An Apprenticeship Multistrategy Learning Theory, Methodology, Tool and Case Studies. London, England, Academic Press. , Marcu, D. and Bowman, M. 1999. An Integrated Shell and Methodology for Rapid Development of Knowledge-Based Agents.