Paper Info
Title
Training Intelligent Agents in the Semantic Web Era: The Golf Advisor Agent
Abstract
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.
Year
DOI
Venue
2007
10.1109/WI-IATW.2007.51
Web Intelligence/IAT Workshops
Keywords
Field
DocType
intelligent agent,semantic web,knowledge representation,description logic
Data mining,Intelligent agent,Knowledge representation and reasoning,Information retrieval,Semantic Web Stack,Computer science,Semantic Web,Agent architecture,Semantic analytics,Social Semantic Web,Semantic Web Rule Language
Conference
ISBN
Citations 
PageRank 
0-7695-3028-1
1
0.41
References 
Authors
4
1
Name
Order
Citations
PageRank
Ioannis N. Athanasiadis135244.44