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ProbCog: Probabilistic Cognition for Technical Systems

The ProbCog project designs and implements a statistical relational learning and reasoning system that supports efficient learning and inference in relational domains.

One of the project's achievements was the development of an open-source toolbox for statistical relational learning and reasoning, which was specifically tailored towards the needs of technical systems.

Acknowledgements

This project was partly funded by CoTeSys.

Publications

Journal Articles and Book Chapters

Knowledge Representation for Cognitive Robots (Moritz Tenorth, Dominik Jain, Michael Beetz), In Künstliche Intelligenz, Springer, volume 24, 2010. [bib] [pdf]
Towards Performing Everyday Manipulation Activities (Michael Beetz, Dominik Jain, Lorenz Mösenlechner, Moritz Tenorth), In Robotics and Autonomous Systems, Elsevier, volume 58, 2010. [bib] [pdf]
Towards Automated Models of Activities of Daily Life (Michael Beetz, Moritz Tenorth, Dominik Jain, Jan Bandouch), In Technology and Disability, IOS Press, volume 22, 2010. [bib] [pdf]

Conference Papers

Learning Probability Distributions over Partially-Ordered Human Everyday Activities (Moritz Tenorth, Fernando De la Torre, Michael Beetz), In IEEE International Conference on Robotics and Automation (ICRA), 2013. (Accepted for publication.) [bib]
Learning Organizational Principles in Human Environments (Martin Schuster, Dominik Jain, Moritz Tenorth, Michael Beetz), In IEEE International Conference on Robotics and Automation (ICRA), 2012. [bib] [pdf]
Compiling AI Engineering Models for Probabilistic Inference (Paul Maier, Dominik Jain, Martin Sachenbacher), In KI 2011: Advances in Artificial Intelligence, 34th Annual German Conference on AI, Springer, volume 7006, 2011. [bib] [pdf]
Bayesian Logic Networks and the Search for Samples with Backward Simulation and Abstract Constraint Learning (Dominik Jain, Klaus von Gleissenthall, Michael Beetz), In KI 2011: Advances in Artificial Intelligence, 34th Annual German Conference on AI, Springer, volume 7006, 2011. [bib] [pdf]
KNOWROB-MAP -- Knowledge-Linked Semantic Object Maps (Moritz Tenorth, Lars Kunze, Dominik Jain, Michael Beetz), In 10th IEEE-RAS International Conference on Humanoid Robots, 2010. [bib] [pdf]
Plan Assessment for Autonomous Manufacturing as Bayesian Inference (Paul Maier, Dominik Jain, Stefan Waldherr, Martin Sachenbacher), In KI 2010: Advances in Artificial Intelligence, 33rd Annual German Conference on AI, Springer, volume 6359, 2010. [bib]
Soft Evidential Update via Markov Chain Monte Carlo Inference (Dominik Jain, Michael Beetz), In KI 2010: Advances in Artificial Intelligence, 33rd Annual German Conference on AI, Springer, volume 6359, 2010. [bib] [pdf]
Adaptive Markov Logic Networks: Learning Statistical Relational Models with Dynamic Parameters (Dominik Jain, Andreas Barthels, Michael Beetz), In 19th European Conference on Artificial Intelligence (ECAI), 2010. [bib] [pdf]
Combining Perception and Knowledge Processing for Everyday Manipulation (Dejan Pangercic, Moritz Tenorth, Dominik Jain, Michael Beetz), In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2010. [bib] [pdf]
Perception and Probabilistic Anchoring for Dynamic World State Logging (Nico Blodow, Dominik Jain, Zoltan-Csaba Marton, Michael Beetz), In 10th IEEE-RAS International Conference on Humanoid Robots, 2010. [bib] [pdf]
Equipping Robot Control Programs with First-Order Probabilistic Reasoning Capabilities (Dominik Jain, Lorenz Mösenlechner, Michael Beetz), In IEEE International Conference on Robotics and Automation (ICRA), 2009. [bib] [pdf]
Towards Automated Models of Activities of Daily Life (Michael Beetz, Jan Bandouch, Dominik Jain, Moritz Tenorth), In First International Symposium on Quality of Life Technology -- Intelligent Systems for Better Living, 2009. [bib] [pdf]
Probabilistic Categorization of Kitchen Objects in Table Settings with a Composite Sensor (Zoltan Csaba Marton, Radu Bogdan Rusu, Dominik Jain, Ulrich Klank, Michael Beetz), In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2009. [bib] [pdf]
The Assistive Kitchen -- A Demonstration Scenario for Cognitive Technical Systems (Michael Beetz, Freek Stulp, Bernd Radig, Jan Bandouch, Nico Blodow, Mihai Dolha, Andreas Fedrizzi, Dominik Jain, Uli Klank, Ingo Kresse, Alexis Maldonado, Zoltan Marton, Lorenz Mösenlechner, Federico Ruiz, Radu Bogdan Rusu, Moritz Tenorth), In IEEE 17th International Symposium on Robot and Human Interactive Communication (RO-MAN), Muenchen, Germany, 2008. (Invited paper.) [bib] [pdf]
Extending Markov Logic to Model Probability Distributions in Relational Domains (Dominik Jain, Bernhard Kirchlechner, Michael Beetz), In KI 2007: Advances in Artificial Intelligence, 30th Annual German Conference on AI, Springer, volume 4667, 2007. [bib] [pdf]

Workshop Papers

Modeling Cognitive Frames for Situations with Markov Logic Networks (William R. Murray, Dominik Jain), In Proceedings of the 8th International NLPCS Workshop: Human-Machine Interaction in Translation, Copenhagen Studies in Language 41, Samfundslitteratur, 2011. [bib]
Diagnostic Hypothesis Enumeration vs. Probabilistic Inference for Hierarchical Automata Models (Paul Maier, Dominik Jain, Martin Sachenbacher), In Proceedings of the 22nd International Workshop on Principles of Diagnosis (DX-2011), 2011. [bib]
Knowledge Engineering with Markov Logic Networks: A Review (Dominik Jain), In DKB 2011: Proceedings of the Third Workshop on Dynamics of Knowledge and Belief, 2011. [bib] [pdf]
Markov Logic as a Modelling Language for Weighted Constraint Satisfaction Problems (Dominik Jain, Paul Maier, Gregor Wylezich), In Eighth International Workshop on Constraint Modelling and Reformulation, in conjunction with CP2009, 2009. [bib] [pdf]
Equipping Robot Control Programs with First-Order Probabilistic Reasoning Capabilities (Dominik Jain, Lorenz Mösenlechner, Michael Beetz), In Proceedings of the 1st International Workshop on Cognition for Technical Systems, 2008. [bib]

Other Publications

Probabilistic Cognition for Technical Systems: Statistical Relational Models for High-Level Knowledge Representation, Learning and Reasoning (Dominik Jain), PhD thesis, Technische Universität München, 2012. [bib]
Deliverable D5.2: The RoboEarth Language -- Language Specification (Moritz Tenorth, Michael Beetz), Technical report, FP7-ICT-248942 RoboEarth, 2010. [bib]
Bayesian Logic Networks (Dominik Jain, Stefan Waldherr, Michael Beetz), Technical report, IAS Group, Fakultät für Informatik, Technische Universität München, 2009. [bib] [pdf]
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Last edited 26.07.2011 18:22 by jain