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Jain, Dominik
Researcher
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Table of ContentsResearch TopicsCurrent Research Projects Teaching Inf. IX(Current Term) Publications |
Research Topics
- Knowledge Representation
- Probabilistic Reasoning
- Statistical Relational Learning
- Probabilistic Models
- Logical Models
- Parameter Learning
Current Research Projects
- ProbCog : ProbCog designs and implements a statistical relational learning system that supports efficient learning and inference in relational domains, focusing mainly on probabilistic logical models. Based on a common data model, we integrate various representation formalisms and have successfully extended the expressiveness and practical applicability of existing approaches. ProbCog thus provides a coherent probabilistic framework that enables cognitive technical systems to deal with a high degree of uncertainty and complexity in a variety of application contexts, including adaptive plan generation for under-specified tasks and heuristic generation.
Teaching General
Student projects on offer:
- Learning Probabilistic Models of Football Games [Bachelor/Master/Diploma thesis]
The probabilistic interpretation of actions and situations in football games is a highly interesting problem. Statistical relational learning methods may provide the tools that are necessary to answer questions such as: What actions are likely to be chosen by a certain player (or a generic player occupying a certain position) given the various features of a situation; and how will these actions be parameterized? Will the actions be successful? How can we determine from the sequence of actions and situations what is generally going on in a football game (build up, attack phase, counter-attack, etc.)? Can we build qualitative models that rank failed actions by considering the causes of failure appropriately? We have a large collection of semantically interpreted football games (about 20 games from last season's Bayern München games) that could serve as the basis for a number of exciting experiments. - Inference in Mixed Networks [Bachelor/Master/Diploma thesis]
Mixed networks are probabilistic models that combine probabilistic components (i.e. a graphical model such as a Bayesian network) with explicit deterministic constraints. The goal of the project is to implement exact inference algorithms that have previously been proposed (and perhaps to improve upon them) and to compare them against approximate methods. If the student sees fit, he/she is free to devise new algorithms for inference in mixed networks in cooperation with me as their advisor. - Learning to Choose a Way of Thinking [Bachelor/Master/Diploma thesis]
The performance of inference algorithms for statistical relational models can vary widely depending on the structure of the model and the evidence it is combined with for the task of inference. Different circumstances may lead to greatly reduced performance or even render an algorithm inapplicable that may otherwise be the best choice. The goal of the project is to analyse instantiations of statistical relational models and to propose a set of features with which an informed decision on the choice of an ideal inference procedure could be made using AI methods - and to propose a suitable parameterization of the method given time constraints.
Past projects:
- Learning Action Models from Trajectory Data (Andreas Barthels, Interdisciplinary Project)
- Maximum a Posteriori Inference in Markov logic Networks for Room Planning (Ralf Wernicke, Bachelor Thesis)
- Applying Markov Logic as a Modelling Language for Weighted CSPs in the Context of Room Planning Optimization (Gregor Wylezich, Bachelor Thesis)
Past courses:
- SS2009: Artificial Intelligence (seminar), Knowledge Representation in Artificial Intelligence (seminar)
- WS2008: Techniques in Artificial Intelligence (tutorial)
- SS2008: Applications of Knowledge-Based Techniques: Knowledge Representation (tutorial)
- WS2007: Techniques in Artificial Intelligence (tutorial)
- SS2007: Discrete Probability Theory (tutorial)
Selected Publications
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Probabilistic Categorization of Kitchen Objects in Table Settings with a Composite Sensor, 2009, Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS),(BibTeX)
For the full list of my Publications please see the
Publications
section.
