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team:daniel_nyga [2017/04/13 13:35] – [Teaching] nygateam:daniel_nyga [2017/05/10 16:25] – [Daniel Nyga, M.Sc. (TUM)] nyga
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-=====Daniel Nyga, M.Sc. (TUM)======+=====Dr.rer.nat. Daniel 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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-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.actioncores.org/|PRAC]]. I am also the lead developer in the projects [[http://www.pracmln.org|pracmln]] and [[http://www.actioncores.org/|PRAC]].
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-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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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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