TY  - CHAP
U1  - Konferenzveröffentlichung
A1  - Kotstein, Sebastian
A1  - Decker, Christian
T1  - Reinforcement learning for IoT interoperability
T2  - 2019 IEEE International Conference on Software Architecture companion : ICSA-C 2019 : proceedings : 25-29 March 2019, Hamburg, Germany
N2  - In this paper, an approach is introduced how reinforcement learning can be used to achieve interoperability between heterogeneous Internet of Things (IoT) components. More specifically, we model an HTTP REST service as a Markov Decision Process and adapt Q-Learning to the properties of REST so that an agent in the role of an HTTP REST client can learn the semantics of the service and, especially an optimal sequence of service calls to achieve an application specific goal. With our approach, we want to open up and facilitate a discussion in the community, as we see the key for achieving interoperability in IoT by the utilization of artificial intelligence techniques.
KW  - Internet of Things
KW  - interoperability
KW  - Q-Learning
KW  - Markov decision process
KW  - reinforcement learning
KW  - REST
Y1  - 2019
SN  - 978-1-72811-876-5
SB  - 978-1-72811-876-5
U6  - https://doi.org/10.1109/ICSA-C.2019.00010
DO  - https://doi.org/10.1109/ICSA-C.2019.00010
SP  - 11
EP  - 18
S1  - 8
PB  - IEEE
CY  - Piscataway, NJ
ER  -