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~~NOTOC~~ | ~~NOTOC~~ | ||
- | =====Daniel Nyga====== | + | =====Dr.rer.nat. |
- | ^ {{: | + | | {{: |
- | |::: ||Research Staff\\ \\ || | + | |::: ||Postdoctoral Researcher\\ \\ || |
|:::|Room: |1.77| | |:::|Room: |1.77| | ||
- | |:::|Tel: |--49 -421 218 64039| | + | |:::|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 the Intelligent Autonomous Systems Group I studied Computer Science at TUM. I'm currently working on the import of knowledge from the world wide web into the knowledge base of our mobile robots. In particular, my current research focuses on understanding natural-language, | ||
- | My work aims at building up action-specific knowledge bases from various knowledge sources, such as natural | + | Daniel Nyga is a postdoctoral researcher |
- | data of a robot: | + | |
- | {{people:nyga:actioncore.png?w=700& | + | ====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 "set the table for dinner", | ||
+ | breakfast", | ||
+ | instructions are only vaguely formulated, complex sequences of | ||
+ | sophisticated and accurate manipulation activities need to be carried | ||
+ | out in order to accomplish the respective tasks. The acquisition of | ||
+ | knowledge about how to perform these activities from huge collections | ||
+ | of natural-language instructions from the Internet has garnered a lot | ||
+ | of attention within the last decade. However, natural language is | ||
+ | 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 in natural language and infer missing | ||
+ | information pieces that are required to render an instruction | ||
+ | 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 as a carrier formalism for encoding uncertain knowledge. | ||
+ | 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. | ||
- | Knowledge about actions and objects is represented as //Probabilistic Robot Action Cores (PRAC)//, which can be thought | + | ====Master' |
+ | |||
+ | [[https://ai.uni-bremen.de/_media/team/ | ||
+ | |||
+ | ====Projects==== | ||
+ | Daniel Nyga's research interests revolve around topics on Artificial Intelligence and Data Science in general, as well as Machine Learning, Data Mining and Pattern Recognition techniques. In particular, he is interested in probabilistic graphical and relational knowledge representation, | ||
+ | |||
+ | 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/Knowledge Processing | + | * Artificial Intelligence |
+ | * Probability Theory | ||
+ | * Probabilistic | ||
* Machine Learning | * Machine Learning | ||
* Statistical Relational Learning | * Statistical Relational Learning | ||
* Data Mining/ | * Data Mining/ | ||
* Automated Learning/ | * Automated Learning/ | ||
- | * Natural-Language Understanding | + | * Natural-language understanding |
====Teaching==== | ====Teaching==== | ||
+ | * AI: Knowledge Acquisition and Representation ([[https:// | ||
+ | * Foundations of Artificial Intelligence ([[https:// | ||
+ | * 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) |
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