TY - GEN
T1 - Context-aware information agents for the automotive domain using Bayesian Networks
AU - Ablaßmeier, Markus
AU - Poitschke, Tony
AU - Reifinger, Stefan
AU - Rigoll, Gerhard
PY - 2007
Y1 - 2007
N2 - To reduce the workload of the driver due to the increasing amount of information and functions, intelligent agents represent a promising possibility to filter the immense data sets. The intentions of the driver can be analyzed and tasks can be accomplished autonomously, i.e. without interference of the user. In this contribution, different adaptive agents for the vehicle are realized: For example, the fuel agent determines its decisions by Bayesian Networks and rule-based interpretation of context influences and knowledge. The measured variables which affect the driver, the system, and the environment are analyzed. In the context of a user study the relevance of individual measured variables was evaluated. On this data basis, the agents were developed and the corresponding networks were trained. During the evaluation of the effectiveness of the agents it shows that the implemented system reduces the number of necessary interaction steps and can relieve the driver. The evaluation shows that the intentions are interpreted to a high degree correctly.
AB - To reduce the workload of the driver due to the increasing amount of information and functions, intelligent agents represent a promising possibility to filter the immense data sets. The intentions of the driver can be analyzed and tasks can be accomplished autonomously, i.e. without interference of the user. In this contribution, different adaptive agents for the vehicle are realized: For example, the fuel agent determines its decisions by Bayesian Networks and rule-based interpretation of context influences and knowledge. The measured variables which affect the driver, the system, and the environment are analyzed. In the context of a user study the relevance of individual measured variables was evaluated. On this data basis, the agents were developed and the corresponding networks were trained. During the evaluation of the effectiveness of the agents it shows that the implemented system reduces the number of necessary interaction steps and can relieve the driver. The evaluation shows that the intentions are interpreted to a high degree correctly.
UR - http://www.scopus.com/inward/record.url?scp=38149019161&partnerID=8YFLogxK
U2 - 10.1007/978-3-540-73345-4_64
DO - 10.1007/978-3-540-73345-4_64
M3 - Conference contribution
AN - SCOPUS:38149019161
SN - 9783540733447
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 561
EP - 570
BT - Human Interface and the Management of Information
PB - Springer Verlag
T2 - Symposium on Human Interface 2007
Y2 - 22 July 2007 through 27 July 2007
ER -