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Decision-Making with Multi-Step Expert Advice on the Web

Philipp, Patrick Raoul

Abstract (englisch):
This thesis deals with solving multi-step tasks by using advice from experts, which are algorithms to solve individual steps of such tasks. We contribute with methods for maximizing the number of correct task solutions by selecting and combining experts for individual task instances and methods for automating the process of solving tasks on the Web, where experts are available as Web services.

Multi-step tasks frequently occur in Natural Language Processing (NLP) or Computer Vision, and as research progresses an increasing amount of exchangeable experts for the same steps are available on the Web. Service provider platforms such as Algorithmia monetize expert access by making expert services available via their platform and having customers pay for single executions.
Such experts can be used to solve diverse tasks, which often consist of multiple steps and thus require pipelines of experts to generate hypotheses.

We perceive two distinct problems for solving multi-step tasks with expert services: (1) Given that the task is sufficiently complex, no single pipeline generates correct solutions for all possible task instances. On ... mehr

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Volltext §
DOI: 10.5445/IR/1000093522
Veröffentlicht am 11.04.2019
Seitenaufrufe: 6
seit 11.04.2019
Downloads: 4
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Zugehörige Institution(en) am KIT Institut für Angewandte Informatik und Formale Beschreibungsverfahren (AIFB)
Publikationstyp Hochschulschrift
Jahr 2019
Sprache Englisch
Identifikator KITopen-ID: 1000093522
Verlag Karlsruhe
Umfang xiii, 218 S.
Abschlussart Dissertation
Fakultät Fakultät für Wirtschaftswissenschaften (WIWI)
Institut Institut für Angewandte Informatik und Formale Beschreibungsverfahren (AIFB)
Prüfungsdatum 13.04.2018
Referent/Betreuer PD Dr. A. Rettinger
Schlagworte Multi-Step Expert Advice, Markov Decision Process, Machine Learning, Reinforcement Learning, Natural Language Processing
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