@inproceedings{31847,
  abstract     = {{The famous $k$-Server Problem covers plenty of resource allocation scenarios, and several variations have been studied extensively for decades. However, to the best of our knowledge, no research has considered the problem if the servers are not identical and requests can express which specific servers should serve them. Therefore, we present a new model generalizing the $k$-Server Problem by *preferences* of the requests and proceed to study it in a uniform metric space for deterministic online algorithms (the special case of paging).

In our model, requests can either demand to be answered by any server (*general requests*) or by a specific one (*specific requests*). If only general requests appear, the instance is one of the original $k$-Server Problem, and a lower bound for the competitive ratio of $k$ applies. If only specific requests appear, a solution with a competitive ratio of $1$ becomes trivial since there is no freedom regarding the servers' movements. Perhaps counter-intuitively, we show that if both kinds of requests appear, the lower bound raises to $2k-1$.

We study deterministic online algorithms in uniform metrics and present two algorithms. The first one has an adaptive competitive ratio dependent on the frequency of specific requests. It achieves a worst-case competitive ratio of $3k-2$ while it is optimal when only general or only specific requests appear (competitive ratio of $k$ and $1$, respectively). The second has a fixed close-to-optimal worst-case competitive ratio of $2k+14$. For the first algorithm, we show a lower bound of $3k-2$, while the second algorithm has a lower bound of $2k-1$ when only general requests appear.
    
The two algorithms differ in only one behavioral rule for each server that significantly influences the competitive ratio. Each server acting according to the rule allows approaching the worst-case lower bound, while it implies an increased lower bound for $k$-Server instances. In other words, there is a trade-off between performing well against instances of the $k$-Server Problem and instances containing specific requests. We also show that no deterministic online algorithm can be optimal for both kinds of instances simultaneously.}},
  author       = {{Castenow, Jannik and Feldkord, Björn and Knollmann, Till and Malatyali, Manuel and Meyer auf der Heide, Friedhelm}},
  booktitle    = {{Proceedings of the 34th ACM Symposium on Parallelism in Algorithms and Architectures}},
  isbn         = {{9781450391467}},
  keywords     = {{K-Server Problem, Heterogeneity, Online Caching}},
  pages        = {{345--356}},
  publisher    = {{Association for Computing Machinery}},
  title        = {{{The k-Server with Preferences Problem}}},
  doi          = {{10.1145/3490148.3538595}},
  year         = {{2022}},
}

@inproceedings{23779,
  abstract     = {{Produktentstehung (PE) bezieht sich auf den Prozess der Planung und Entwicklung eines Produkts sowie der damit verbundenen Dienstleistungen von der ersten Idee bis zur Herstellung und zum Vertrieb. Während dieses Prozesses gibt es zahlreiche Aufgaben, die von menschlichem Fachwissen abhängen und typischerweise von erfahrenen Experten übernommen werden. Da sich das Feld der Künstlichen Intelligenz (KI) immer weiterentwickelt und seinen Weg in den Fertigungssektor findet, gibt es viele Möglichkeiten für eine Anwendung von KI, um bei der Lösung der oben genannten Aufgaben zu helfen. In diesem Paper geben wir einen umfassenden Überblick über den aktuellen Stand der Technik des Einsatzes von KI in der PE. 
Im Detail analysieren wir 40 bestehende Surveys zu KI in der PE und 94 Case Studies, um herauszufinden, welche Bereiche der PE von der aktuellen Forschung in diesem Bereich vorrangig adressiert werden, wie ausgereift die diskutierten KI-Methoden sind und inwieweit datenzentrierte Ansätze in der aktuellen Forschung genutzt werden.}},
  author       = {{Bernijazov, Ruslan and Dicks, Alexander and Dumitrescu, Roman and Foullois, Marc and Hanselle, Jonas Manuel and Hüllermeier, Eyke and Karakaya, Gökce and Ködding, Patrick and Lohweg, Volker and Malatyali, Manuel and Meyer auf der Heide, Friedhelm and Panzner, Melina and Soltenborn, Christian}},
  booktitle    = {{Proceedings of the 30th International Joint Conference on Artificial Intelligence (IJCAI-21)}},
  keywords     = {{Artificial Intelligence Product Creation Literature Review}},
  location     = {{Montreal, Kanada}},
  title        = {{{A Meta-Review on Artificial Intelligence in Product Creation}}},
  year         = {{2021}},
}

@article{20683,
  author       = {{Feldkord, Björn and Knollmann, Till and Malatyali, Manuel and Meyer auf der Heide, Friedhelm}},
  journal      = {{Theory of Computing Systems}},
  pages        = {{943–984}},
  title        = {{{Managing Multiple Mobile Resources}}},
  doi          = {{10.1007/s00224-020-10023-8}},
  volume       = {{65}},
  year         = {{2021}},
}

@inbook{16461,
  author       = {{Bemmann, Pascal and Biermeier, Felix and Bürmann, Jan and Kemper, Arne and Knollmann, Till and Knorr, Steffen and Kothe, Nils and Mäcker, Alexander and Malatyali, Manuel and Meyer auf der Heide, Friedhelm and Riechers, Sören and Schaefer, Johannes Sebastian and Sundermeier, Jannik}},
  booktitle    = {{Structural Information and Communication Complexity}},
  isbn         = {{9783319720494}},
  issn         = {{0302-9743}},
  title        = {{{Monitoring of Domain-Related Problems in Distributed Data Streams}}},
  doi          = {{10.1007/978-3-319-72050-0_13}},
  year         = {{2017}},
}