Equipping robot control programs with first-order probabilistic reasoning capabilities

Proceedings - IEEE International Conference on Robotics and Automation

Volumn , Issue , 2009, Pages 3626-3631

  Jain, Dominik ^a   Mösenlechner, Lorenz ^a   Beetz, Michael ^a  

^a TECHNICAL UNIVERSITY OF MUNICH   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

AGRICULTURAL ROBOTS; APPLICATION PROGRAMS; KNOWLEDGE REPRESENTATION; LEARNING SYSTEMS; ROBOT PROGRAMMING;

LEARNING METHODS; PROBABILISTIC INFERENCE; PROBABILISTIC KNOWLEDGE; PROBABILISTIC REASONING; REAL WORLD ENVIRONMENTS; REPRESENTATION FORMALISMS; ROBOT CONTROLLER; STATISTICAL RELATIONAL LEARNING;

ROBOTS;

EID: 77955413312     PISSN: 10504729     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ROBOT.2009.5152676     Document Type: Conference Paper

References (19)

1. L. Getoor and B. Taskar, Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning). The MIT Press, 2007.
2. G. Metta, P. Fitzpatrick, and L. Natale, “YARP: Yet Another Robot Platform,” International Journal of Advanced Robotics Systems, special issue on Software Development and Integration in Robotics, vol. 3, no. 1, 2006.
3. D. McDermott, “A Reactive Plan Language,” Yale University,” Research Report YALEU/DCS/RR-864, 1991.
4. M. Richardson and P. Domingos, “Markov Logic Networks,” Mach. Learn., vol. 62, no. 1-2, pp. 107–136, 2006.
5. J. Pearl, Markov and Bayes networks: A comparison of two graphical representations of probabilistic knowledge (CSD. University of California at Los Angeles. Computer Science Department). University of California at Los Angeles, 1986.
6. D. Jain, B. Kirchlechner, and M. Beetz, “Extending Markov Logic to Model Probability Distributions in Relational Domains,” in Proceedings of the 30th German Conference on Artificial Intelligence (KI-2007), 2007, pp. 129–143.
7. K. B. Laskey, “MEBN: A language for first-order Bayesian knowledge bases.” Artif. Intell., vol. 172, no. 2-3, pp. 140–178, 2008.
8. J. Bandouch, F. Engstler, and M. Beetz, “Accurate human motion capture using an ergonomics-based anthropometric human model,” in Proceedings of the Fifth International Conference on Articulated Motion and Deformable Objects (AMDO), 2008.
9. R. M. Fung and K.-C. Chang, “Weighing and Integrating Evidence for Stochastic Simulation in Bayesian Networks,” in UAI, 1989, pp. 209–220.
10. W. R. Gilks, Markov Chain Monte Carlo in Practice. Chapman & Hall/CRC, December 1995.
11. R. M. Fung and B. D. Favero, “Backward Simulation in Bayesian Networks,” in UAI, 1994, pp. 227–234.
12. C. Yuan and M. J. Druzdzel, “An importance sampling algorithm based on evidence pre-propagation,” in UAI, 2003, pp. 624–631.
13. M. J. Druzdzel, “SMILE: Structural Modeling, Inference, and Learning Engine and GeNIE: A Development Environment for Graphical Decision-Theoretic Models,” in AAAI/IAAI, 1999, pp. 902–903.
14. H. Poon and P. Domingos, “Sound and Efficient Inference with Probabilistic and Deterministic Dependencies.” in AAAI. AAAI Press, 2006.
15. S. Kok, P. Singla, M. Richardson, and P. Domingos, “The Alchemy system for statistical relational AI,” http://alchemy.cs.washington.edu/, 2004.
16. D. Poole, “First-order probabilistic inference,” in IJCAI, 2003, pp. 985–991.
17. R. de Salvo Braz, E. Amir, and D. Roth, “Lifted First-Order Probabilistic Inference,” in IJCAI, 2005, pp. 1319–1325.
18. P. Singla and P. Domingos, “Lifted first-order belief propagation,” in AAAI, 2008, To appear.
19. A. Müller, “Transformational planning for autonomous household robots using libraries of robust and flexible plans,” Ph.D. dissertation, Technische Universität München, 2008.