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- W7785909 abstract "Developing knowledge-based software agents to intelligently perform complex real world tasks is time consuming and expensive. The most costly part of developing these agents is knowledge acquisition; extracting knowledge from human experts and encoding it for the software agents. Research in areas such as learning by instruction and learning by experimentation have explored a variety of approaches to applying machine-learning algorithms to the knowledge-acquisition problem. However, there has been relatively little research in learning by observation. As a knowledge source, observation demands less of the expert than instruction while, unlike experimentation, still basing the learned knowledge on expert behavior. However, it is not obvious how to apply an inductive learning algorithm to a trace of the sensors and parameters available to the expert and his or her actions in response in order to extract the underlying task knowledge. This research presents a framework that provides the necessary infrastructure to learn a hierarchy of task-performance operators from observation traces annotated with goal transition information. This framework defines a standard representation for task-performance knowledge and describes the components necessary to extract this knowledge from the observation traces. One instance of a learning-by-observation system, called KnoMic (Knowledge Mimic), is developed within the framework and evaluated in three domains. This evaluation demonstrates that most aspects of task-performance knowledge can be learned from observation and characterizes the aspects that require additional knowledge sources. One example of an additional knowledge source is explored by using learning by instruction to detect and correct many types of errors in the knowledge learned by KnoMic." @default.
- W7785909 created "2016-06-24" @default.
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- W7785909 date "2000-01-01" @default.
- W7785909 modified "2023-09-28" @default.
- W7785909 title "Learning task-performance knowledge through observation" @default.
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