Šošić, A. ; Zoubir, A. M. ; Koeppl, H. (2018)
A Bayesian Approach to Policy Recognition and State Representation Learning.
In: IEEE Transactions on Pattern Analysis and Machine Intelligence, 40 (6)
doi: 10.1109/TPAMI.2017.2711024
Artikel, Bibliographie
Kurzbeschreibung (Abstract)
Learning from demonstration (LfD) is the process of building behavioral models of a task from demonstrations provided by an expert. These models can be used, e.g., for system control by generalizing the expert demonstrations to previously unencountered situations. Most LfD methods, however, make strong assumptions about the expert behavior, e.g., they assume the existence of a deterministic optimal ground truth policy or require direct monitoring of the expert's controls, which limits their practical use as part of a general system identification framework. In this work, we consider the LfD problem in a more general setting where we allow for arbitrary stochastic expert policies, without reasoning about the optimality of the demonstrations. Following a Bayesian methodology, we model the full posterior distribution of possible expert controllers that explain the provided demonstration data. Moreover, we show that our methodology can be applied in a nonparametric context to infer the complexity of the state representation used by the expert, and to learn task-appropriate partitionings of the system state space.
| Typ des Eintrags: | Artikel |
|---|---|
| Erschienen: | 2018 |
| Autor(en): | Šošić, A. ; Zoubir, A. M. ; Koeppl, H. |
| Art des Eintrags: | Bibliographie |
| Titel: | A Bayesian Approach to Policy Recognition and State Representation Learning |
| Sprache: | Englisch |
| Publikationsjahr: | 1 Juni 2018 |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | IEEE Transactions on Pattern Analysis and Machine Intelligence |
| Jahrgang/Volume einer Zeitschrift: | 40 |
| (Heft-)Nummer: | 6 |
| DOI: | 10.1109/TPAMI.2017.2711024 |
| URL / URN: | https://ieeexplore.ieee.org/document/7937852 |
| Kurzbeschreibung (Abstract): | Learning from demonstration (LfD) is the process of building behavioral models of a task from demonstrations provided by an expert. These models can be used, e.g., for system control by generalizing the expert demonstrations to previously unencountered situations. Most LfD methods, however, make strong assumptions about the expert behavior, e.g., they assume the existence of a deterministic optimal ground truth policy or require direct monitoring of the expert's controls, which limits their practical use as part of a general system identification framework. In this work, we consider the LfD problem in a more general setting where we allow for arbitrary stochastic expert policies, without reasoning about the optimality of the demonstrations. Following a Bayesian methodology, we model the full posterior distribution of possible expert controllers that explain the provided demonstration data. Moreover, we show that our methodology can be applied in a nonparametric context to infer the complexity of the state representation used by the expert, and to learn task-appropriate partitionings of the system state space. |
| Fachbereich(e)/-gebiet(e): | 18 Fachbereich Elektrotechnik und Informationstechnik 18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Nachrichtentechnik > Bioinspirierte Kommunikationssysteme 18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Nachrichtentechnik 18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Nachrichtentechnik > Signalverarbeitung DFG-Sonderforschungsbereiche (inkl. Transregio) DFG-Sonderforschungsbereiche (inkl. Transregio) > Sonderforschungsbereiche Zentrale Einrichtungen Zentrale Einrichtungen > Centre for Cognitive Science (CCS) DFG-Sonderforschungsbereiche (inkl. Transregio) > Sonderforschungsbereiche > SFB 1053: MAKI – Multi-Mechanismen-Adaption für das künftige Internet DFG-Sonderforschungsbereiche (inkl. Transregio) > Sonderforschungsbereiche > SFB 1053: MAKI – Multi-Mechanismen-Adaption für das künftige Internet > C: Kommunikationsmechanismen DFG-Sonderforschungsbereiche (inkl. Transregio) > Sonderforschungsbereiche > SFB 1053: MAKI – Multi-Mechanismen-Adaption für das künftige Internet > C: Kommunikationsmechanismen > Teilprojekt C3: Inhaltszentrische Sicht |
| Hinterlegungsdatum: | 03 Mai 2016 16:59 |
| Letzte Änderung: | 15 Dez 2022 09:22 |
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