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~~NOTOC~~ | ~~NOTOC~~ | ||
- | =====Daniel Nyga, M.Sc. (TUM)====== | + | =====Dr.rer.nat. Daniel Nyga====== |
- | ^ {{: | + | | {{: |
- | |::: ||Research Staff\\ \\ || | + | |::: ||Postdoctoral Researcher\\ \\ || |
|:::|Room: |1.77| | |:::|Room: |1.77| | ||
- | |:::|Tel: |--49 -421 218 64008| | + | |:::|Tel: |--49 -421 218 64010| |
|:::|Fax: |--49 -421 218 64047| | |:::|Fax: |--49 -421 218 64047| | ||
|:::|Mail: |< | |:::|Mail: |< | ||
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====About==== | ====About==== | ||
- | Before I joined | + | Daniel Nyga is a postdoctoral researcher at the Institute for Artificial Intelligence |
- | I'm working on the import | + | ====Dissertation==== |
+ | [[http:// | ||
+ | has been one of the ultimate long-standing goals in both Artificial | ||
+ | Intelligence and Robotics research. In near-future applications, | ||
+ | robotic assistants and companions | ||
+ | commands such as " | ||
+ | breakfast", | ||
+ | instructions are only vaguely formulated, complex sequences of | ||
+ | sophisticated and accurate manipulation activities need to be carried | ||
+ | out in order to accomplish | ||
+ | knowledge | ||
+ | of natural-language instructions from the Internet has garnered a lot | ||
+ | of attention within the last decade. However, natural language | ||
+ | typically massively unspecific, incomplete, ambiguous and vague and | ||
+ | thus requires powerful means for interpretation. | ||
+ | This work presents PRAC -- Probabilistic Action Cores -- an | ||
+ | interpreter for natural-language instructions which is able to resolve | ||
+ | vagueness and ambiguity | ||
+ | information pieces that are required | ||
+ | executable by a robot. To this end, PRAC formulates the problem of | ||
+ | instruction interpretation as a reasoning problem in first-order | ||
+ | probabilistic knowledge bases. In particular, the system uses Markov | ||
+ | logic networks | ||
+ | A novel framework for reasoning about unmodeled symbolic concepts is | ||
+ | introduced, which incorporates ontological knowledge from taxonomies | ||
+ | and exploits semantically similar relational structures in a domain of | ||
+ | discourse. The resulting reasoning framework thus enables more compact | ||
+ | representations of knowledge and exhibits strong generalization | ||
+ | performance when being learnt from very sparse data. Furthermore, | ||
+ | novel approach for completing directives is presented, which applies | ||
+ | semantic analogical reasoning to transfer knowledge collected from | ||
+ | thousands of natural-language instruction sheets to new situations. In | ||
+ | addition, a cohesive processing pipeline is described that transforms | ||
+ | vague and incomplete task formulations into sequences of formally | ||
+ | specified robot plans. The system is connected to a plan executive that | ||
+ | is able to execute the computed plans in a simulator. Experiments | ||
+ | conducted in a publicly accessible, browser-based web interface | ||
+ | showcase that PRAC is capable of closing the loop from | ||
+ | natural-language instructions to their execution by a robot. | ||
- | My work aims at building up action-specific knowledge bases from various knowledge sources, such as natural language, interactive computer games, observations of humans performing everyday activity or experience data of a robot. | + | ====Master' |
- | {{research:actioncore.png?w=620& | + | [[https:// |
- | Knowledge about actions | + | ====Projects==== |
+ | Daniel Nyga's research interests revolve around topics on Artificial Intelligence | ||
+ | |||
+ | He was involved | ||
+ | |||
+ | He is the lead developer in the projects [[http:// | ||
+ | |||
+ | His GitHub profile can be found [[http:// | ||
- | If you are interested in a student project in any of the above topics, please contact me via E-Mail or just drop into my office. | ||
====Fields of Interest==== | ====Fields of Interest==== | ||
* Artificial Intelligence | * Artificial Intelligence | ||
+ | * Probability Theory | ||
* Probabilistic Knowledge Processing | * Probabilistic Knowledge Processing | ||
* Machine Learning | * Machine Learning | ||
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* Data Mining/ | * Data Mining/ | ||
* Automated Learning/ | * Automated Learning/ | ||
- | * Natural-Language Understanding | + | * Natural-language understanding |
====Teaching==== | ====Teaching==== | ||
+ | * AI: Knowledge Acquisition and Representation ([[https:// | ||
+ | * Master Seminar: Data Mining and Data Analytics ([[http:// | ||
+ | * AI: Knowledge Acquisition and Representation ([[https:// | ||
+ | * AI: Knowledge Acquisition and Representation ([[https:// | ||
+ | * Foundations of Artificial Intelligence ([[https:// | ||
+ | * AI: Knowledge Acquisition and Representation ([[https:// | ||
+ | * Foundations of Artificial Intelligence ([[https:// | ||
+ | * AI: Knowledge Acquisition and Representation ([[https:// | ||
* Foundations of Artificial Intelligence ([[https:// | * Foundations of Artificial Intelligence ([[https:// | ||
- | * Technical Cognitive Systems (Lecture & Tutorial, | + | * Technical Cognitive Systems (Lecture & Tutorial, |
- | * Techniques in Artificial Intelligence (Tutorial, | + | * Techniques in Artificial Intelligence (Tutorial, |
- | * Discrete Probability Theory (Tutorial, | + | * Discrete Probability Theory (Tutorial, |
+ | |||
====Supervised Theses==== | ====Supervised Theses==== | ||
+ | * Lifelong Learning of First-order Probabilistic Models for Everyday Robot Manipulation (Master' | ||
+ | * Scaling Probabilistic Completion of Robot Instructions through Semantic Information Retrieval (Master' | ||
+ | * To see what no robot has seen before - Recognizing objects based on natural-language descriptions (Master' | ||
* Web-enabled Learning of Models for Word Sense Disambiguation (Bachelor Thesis, Stephan Epping) | * Web-enabled Learning of Models for Word Sense Disambiguation (Bachelor Thesis, Stephan Epping) | ||
* Grounding Words to Objects: A Joint Model for Co-reference and Entity Resolution Using Markov Logic Networks for Robot Instruction Processing (Diploma Thesis, Florian Meyer) | * Grounding Words to Objects: A Joint Model for Co-reference and Entity Resolution Using Markov Logic Networks for Robot Instruction Processing (Diploma Thesis, Florian Meyer) | ||
- | |||
- | ====Open Positions==== | ||
- | Studentische Hilfskraft im Bereich Wissensrepräsentation und | ||
- | Sprachverstehen für intelligente autonome Roboter gesucht. | ||
- | |||
- | Im Rahmen des europäischen Forschungsprojektes RoboHow.Cog [1,2] | ||
- | werden Methoden erforscht, um Wissen aus unterschiedlichen Quellen | ||
- | (z.B. Videos, Text, Computerspiele und kinästhetische Demonstration) | ||
- | miteinander zu verknüpfen, | ||
- | selbstständig ihr Repertoire an Fähigkeiten (wie z.B. die | ||
- | Zubereitung von Pfannkuchen) zu erweitern. | ||
- | |||
- | Die Arbeitsgruppe für Künstliche Intelligenz (ai.uni-bremen.de) der | ||
- | Uni Bremen sucht ab sofort eine studentische Hilfskraft für die | ||
- | Entwicklung und Integration von probabilistischen Methoden der KI, | ||
- | die es autonomen Haushaltsrobotern ermöglichen, | ||
- | Instruktionen aus Rezepten im World Wide Web zu verstehen und | ||
- | auszuführen. | ||
- | |||
- | Die HiWi-Tätigkeit kann auch als Ausgangspunkt für weitere Bachelor-, | ||
- | Diplom- oder Masterarbeiten dienen. | ||
- | |||
- | Aufgaben: | ||
- | * Implementierung einer Schnittstelle für das Robot Operating System (ROS), | ||
- | * Anbindung der Wissensbasis an das Ausführungsmodul des Roboters | ||
- | * Unterstützung der wissenschaftlichen Mitarbeiter bei der Erweiterung | ||
- | und Integration der Roboterplattform PR2. | ||
- | |||
- | Kompetenzen: | ||
- | * Informatikstudium (Bachelor, Master oder Diplom) | ||
- | * Grundkenntnisse im Bereich Künstliche Intelligenz | ||
- | * Grundkenntnisse im Bereich Wahrscheinlichkeitstheorie | ||
- | * Grundkenntnisse im Bereich Maschinelles Lernen | ||
- | * Programmierkenntnisse in den Sprachen Python u. Java | ||
- | |||
- | Ausgeschriebene Stundenzahl: | ||
- | |||
- | [1] www.robohow.eu\\ | ||
- | [2] http:// | ||
- | |||
- | |||
====== Publications ====== | ====== Publications ====== | ||
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< | < | ||
</ | </ | ||
+ | |||
+ |
Prof. Dr. hc. Michael Beetz PhD
Head of Institute
Contact via
Andrea Cowley
assistant to Prof. Beetz
ai-office@cs.uni-bremen.de
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