Article
Refine
Year of publication
Document Type
- Article (3149) (remove)
Language
- English (3149) (remove)
Has Fulltext
- no (3149) (remove)
Keywords
- avalanche (5)
- Earthquake (4)
- LAPS (4)
- field-effect sensor (4)
- frequency mixing magnetic detection (4)
- CellDrum (3)
- Heparin (3)
- capacitive field-effect sensor (3)
- hydrogen peroxide (3)
- magnetic nanoparticles (3)
- snow (3)
- tobacco mosaic virus (TMV) (3)
- Bacillus atrophaeus (2)
- Chemometrics (2)
- Drinfeld modules (2)
- Empirical process (2)
- Field-effect sensor (2)
- Goodness-of-fit test (2)
- Hot S-parameter (2)
- IR spectroscopy (2)
Institute
- Fachbereich Medizintechnik und Technomathematik (1299)
- INB - Institut für Nano- und Biotechnologien (484)
- Fachbereich Chemie und Biotechnologie (454)
- Fachbereich Elektrotechnik und Informationstechnik (400)
- IfB - Institut für Bioengineering (388)
- Fachbereich Energietechnik (354)
- Fachbereich Luft- und Raumfahrttechnik (240)
- Fachbereich Maschinenbau und Mechatronik (142)
- Fachbereich Wirtschaftswissenschaften (105)
- Fachbereich Bauingenieurwesen (64)
- Solar-Institut Jülich (41)
- ECSM European Center for Sustainable Mobility (23)
- Sonstiges (21)
- Institut fuer Angewandte Polymerchemie (20)
- Freshman Institute (17)
- MASKOR Institut für Mobile Autonome Systeme und Kognitive Robotik (14)
- Fachbereich Gestaltung (12)
- Nowum-Energy (12)
- Fachbereich Architektur (9)
- ZHQ - Bereich Hochschuldidaktik und Evaluation (5)
In this paper we present an extension of the action language Golog that allows for using fuzzy notions in non-deterministic argument choices and the reward function in decision-theoretic planning. Often, in decision-theoretic planning, it is cumbersome to specify the set of values to pick from in the non-deterministic-choice-of-argument statement. Also, even for domain experts, it is not always easy to specify a reward function. Instead of providing a finite domain for values in the non-deterministic-choice-of-argument statement in Golog, we now allow for stating the argument domain by simply providing a formula over linguistic terms and fuzzy uents. In Golog’s forward-search DT planning algorithm, these formulas are evaluated in order to find the agent’s optimal policy. We illustrate this in the Diner Domain where the agent needs to calculate the optimal serving order.
Data-driven prediction and prevention of extreme events in a spatially extended excitable system
(2015)
Das Drallrohr
(2005)