Training intelligent agents in the semantic web era: The golf advisor agent
Agent training techniques study methods to embed empirical, inductive knowledge representations into intelligent agents, in dynamic, recursive or semi-automated ways, expressed in forms that can be used for agent reasoning. This paper investigates how data-driven rule-sets can be transcribed into ontologies, and how semantic web technologies as OWL can be used for representing inductive systems for agent decision-making. The method presented avoids the transliteration of data-driven knowledge into conventional if-then-else systems, rather demonstrates how inferencing through description logics and Semantic Web inference engines can be incorporated into the training process of agents that manipulate categorical and/or numerical data.
I. N. Athanasiadis, Training intelligent agents in the semantic web era: The golf advisor agent, IEEE Intl Conf on Web Intelligence and Intelligent Agent Technology - Workshops (WI-IAT 2007), 2007, IEEE Press, doi:10.1109/WIIATW.2007.4427637.
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