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team:daniel_nyga [2015/10/07 10:55] – [Teaching] nygateam:daniel_nyga [2017/05/10 16:24] – [About] nyga
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 ====About==== ====About====
-Before I joined the Institute for Artificial Intelligence, I studied Computer Science at Technische Universität München, where I received my Master's degree in 2010 (with distinction). In February 2011 I started my PhD supervised by Prof. Michael Beetz at the //Intelligent Autonomous Systems// group at TUM, which I am now continuing at the IAI, University of Bremen.+Before I joined the Institute for Artificial Intelligence, I studied Computer Science at Technische Universität München, where I received my Master's degree in 2010 (with distinction). In 2011 I started my PhD supervised by Prof. Michael Beetz at the //Intelligent Autonomous Systems// group at TUM, which I have finished at the Institute for Artificial Intelligence, University of Bremen, with my thesis on the [[http://nbn-resolving.de/urn:nbn:de:gbv:46-00105882-13|Interpretation of Natural-language Robot Instructions: Probabilistic Knowledge Representation, Learning, and Reasoning]]
  
 I'm working on the import of action-specific knowledge from the World Wide Web into the knowledge bases of our mobile robots. In particular, my current research focuses on understanding natural language, in order to enable a robot to autonomously acquire new high-level skills by querying web pages such as eHow.com or wikiHow.com. I'm working on the import of action-specific knowledge from the World Wide Web into the knowledge bases of our mobile robots. In particular, my current research focuses on understanding natural language, in order to enable a robot to autonomously acquire new high-level skills by querying web pages such as eHow.com or wikiHow.com.
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 {{research:actioncore.png?w=620&h=65&t=1357297411}} {{research:actioncore.png?w=620&h=65&t=1357297411}}
  
-Knowledge about actions and objects is represented as //Probabilistic Robot Action Cores (PRAC)//, which can be thought of generic event patterns that enable a robot to infer important information that is missing in an original natural-language instruction. PRAC models are represented in //Markov Logic Networks//, a powerful knowlegde represenation formalism combing first-order logic and probability theory.+Knowledge about actions and objects is represented as //Probabilistic Action Cores (PRAC)//, which can be thought of generic event patterns that enable a robot to infer important information that is missing in an original natural-language instruction. PRAC models are represented in //Markov Logic Networks//, a powerful knowlegde represenation formalism combing first-order logic and probability theory.
  
-I am involved in the European research projects [[http://www.youtube.com/watch?v=qQG3CkH27qc#t=118|RoboHow]] ([[http://www.robohow.org]]) and [[http://www.acat-project.eu|ACAT]].+I was involved in the European research projects [[http://www.youtube.com/watch?v=qQG3CkH27qc#t=118|RoboHow]] ([[http://www.robohow.org]]) and [[http://www.acat-project.eu|ACAT]].
  
-I am also the lead developer in the projects [[http://www.pracmln.org|pracmln]] and [[http://www.open-ease.org/nlp-overview/|PRAC]]. +I am also the lead developer in the projects [[http://www.pracmln.org|pracmln]] and [[http://www.actioncores.org/|PRAC]].
- +
-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. +
  
  
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 ====Teaching==== ====Teaching====
 +  * Master Seminar: Data Mining and Data Analytics ([[http://ai.uni-bremen.de/teaching/datamining_ss17|SS2017]])
 +  * AI: Knowledge Acquisition and Representation ([[https://ai.uni-bremen.de/teaching/le-ki2-ws16|WS2016/17]]) (Lecturer)
   * AI: Knowledge Acquisition and Representation ([[https://ai.uni-bremen.de/teaching/le-ki2-ws15|WS2015/16]]) (Lecturer)   * AI: Knowledge Acquisition and Representation ([[https://ai.uni-bremen.de/teaching/le-ki2-ws15|WS2015/16]]) (Lecturer)
   * Foundations of Artificial Intelligence ([[https://ai.uni-bremen.de/teaching/le-ai-ss15|SS2015]]) (Tutorial/Co-Lecturer)   * Foundations of Artificial Intelligence ([[https://ai.uni-bremen.de/teaching/le-ai-ss15|SS2015]]) (Tutorial/Co-Lecturer)
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   * Techniques in Artificial Intelligence (Tutorial,  at TUM) ([[https://ias.cs.tum.edu/teaching/ws2011/240927786|WS2011/12]])   * Techniques in Artificial Intelligence (Tutorial,  at TUM) ([[https://ias.cs.tum.edu/teaching/ws2011/240927786|WS2011/12]])
   * Discrete Probability Theory (Tutorial, at TUM) ([[http://www14.in.tum.de/lehre/2011SS/dwt/|SS2011]])   * Discrete Probability Theory (Tutorial, at TUM) ([[http://www14.in.tum.de/lehre/2011SS/dwt/|SS2011]])
 + 
 ====Supervised Theses==== ====Supervised Theses====
 +  * Lifelong Learning of First-order Probabilistic Models for Everyday Robot Manipulation (Master's Thesis, Marc Niehaus)
 +  * Scaling Probabilistic Completion of Robot Instructions through Semantic Information Retrieval (Master's Thesis, Sebastian Koralewski)
   * To see what no robot has seen before - Recognizing objects based on natural-language descriptions (Master's Thesis, Mareike Picklum)   * To see what no robot has seen before - Recognizing objects based on natural-language descriptions (Master's Thesis, Mareike Picklum)
   * 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) 
-   +  
 ====== Publications ====== ====== Publications ======
  
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 <author>nyga</author> <author>nyga</author>
 </bibtex> </bibtex>
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Prof. Dr. hc. Michael Beetz PhD
Head of Institute

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