Beitrag in einem Tagungsband
Lifelong Learning and Collaboration of Smart Technical Systems in Open-Ended Environments - Opportunistic Collaborative Interactive Learning
Details zur Publikation
Autor(inn)en: | Bahle, G.; Calma, A.; Leimeister, J.; Lukowicz, P.; Oeste-Reiß, S.; Reitmaier, T.; Schmidt, A.; Sick, B.; Stumme, G.; Zweig, K. |
Herausgeber: | Kounev, Samuel; Giese, Holger; Liu, Jie |
Verlag: | Curran Associates |
Verlagsort / Veröffentlichungsort: | Red Hook, New York |
Publikationsjahr: | 2016 |
Seitenbereich: | 315-324 |
Buchtitel: | 2016 IEEE International Conference on Autonomic Computing (ICAC) |
DOI-Link der Erstveröffentlichung: |
Zusammenfassung, Abstract
Today, so-called "smart" or "intelligent" systems heavily rely on machine learning techniques to adjust their behavior by means of sample data (e.g., sensor observations). But, it will be more and more complicated or even impossible to provide those data at design-time of that system. As a consequence, these systems have to learn at run-time. Moreover, these systems will have to self-organize their learning processes. They have to decide which information or knowledge source they use at which time, depending on the quality of the information or knowledge they collect, the availability of these sources, the costs of gathering the information or knowledge, etc. With this article, we propose opportunistic collaborative interactive learning (O-CIL) as a new learning principle for future, even "smarter" systems. O-CIL will enable a "lifelong" or "neverending" learning of such systems in open-ended (i.e., time-variant) environments, based on active behavior and collaboration of such systems. Not only these systems collaborate, also humans collaborate either directly or indirectly by interacting with these systems. The article characterizes O-CIL, summarizes related work, sketches research challenges, and illustrates O-CIL with some preliminary results.
Today, so-called "smart" or "intelligent" systems heavily rely on machine learning techniques to adjust their behavior by means of sample data (e.g., sensor observations). But, it will be more and more complicated or even impossible to provide those data at design-time of that system. As a consequence, these systems have to learn at run-time. Moreover, these systems will have to self-organize their learning processes. They have to decide which information or knowledge source they use at which time, depending on the quality of the information or knowledge they collect, the availability of these sources, the costs of gathering the information or knowledge, etc. With this article, we propose opportunistic collaborative interactive learning (O-CIL) as a new learning principle for future, even "smarter" systems. O-CIL will enable a "lifelong" or "neverending" learning of such systems in open-ended (i.e., time-variant) environments, based on active behavior and collaboration of such systems. Not only these systems collaborate, also humans collaborate either directly or indirectly by interacting with these systems. The article characterizes O-CIL, summarizes related work, sketches research challenges, and illustrates O-CIL with some preliminary results.
Schlagwörter
aca itegpub pub, asc
, collaborative, crowdsourcing pub, engineering pub, gba smart, gst pub, interactive, jml pub, kzw cepub collaboration, learning opportunistic, learning pub, plu lifelong, Soe pub, systems pub, tre pub
