References

The implementation of LoMRF is based on the following publications:

• Domingos, P., and Lowd, D. (2009). Markov Logic: An Interface Layer for Artificial Intelligence. Synthesis Lectures on Artificial Intelligence and Machine Learning. Morgan & Claypool Publishers. (link)

• Duchi, J., Hazan, E., Singer, Y. (2011). Adaptive Subgradient Methods for Online Learning and Stochastic Optimization. The Journal of Machine Learning Research, Vol. 12, pp. 2121--2159. (link)

• Kautz, H., Selman, B., & Jiang, Y. (1997). A General Stochastic Approach to Solving Problems with Hard and Soft Constraints. In Gu, D., Du, J., & Pardalos, P. (Eds.), The Satisfiability Problem: Theory and Applications, Vol. 35 of DIMACS Series in Discrete Mathematics and Theoretical Computer Science, pp. 573-586. AMS. (link)

• Lifschitz, V. (1994). Circumscription. In Handbook of logic in Artificial Intelligence and Logic Programming, Vol. 3, pp. 297-352. (link)

• McCarthy, J. (1980). Circumscription - A Form of Non-Monotonic Reasoning. Artificial Intelligence, 13 (1-2), 27-39. (link)

• Michelioudakis E., Skarlatidis A., Paliouras G. and Artikis A. OSLa: Online Structure Learning using Background Knowledge Axiomatization. In Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD 2016).

• Nonnengart, A., & Weidenbach, C. (2001). Computing Small Clause Normal Forms. In Robinson, J. A., & Voronkov, A. (Eds.), Handbook of Automated Reasoning, pp. 335-367. Elsevier and MIT Press. (link)

• Poon, H., & Domingos, P. (2006). Sound and Efficient Inference with Probabilistic and Deterministic Dependencies. In Proceedings of the 21st AAAI Conference on Artificial Intelligence, pp. 458-463. AAAI Press. (link)

• Shavlik, J. W., & Natarajan, S. (2009). Speeding up inference in Markov Logic Networks by preprocessing to reduce the size of the resulting grounded network. In Proceedings of the 21st International Joint Conference on Artificial Intelligence (IJCAI), pp. 1951-1956. (link)

• Selman, B., Kautz, H. and Cohen, B. (1993) Local Search Strategies for Satisfiability Testing. Final version appears in Cliques, Coloring, and Satisfiability: Second DIMACS Implementation Challenge. In David S. Johnson and Michael A. Trick, (Ed.), DIMACS Series in Discrete Mathematics and Theoretical Computer Science, vol. 26, AMS. (link)

• Skarlatidis A. (2014). Event Recognition Under Uncertainty and Incomplete Data. PhD Thesis. Department of Digital Systems, University of Piraeus. (link)

• Skarlatidis A., Paliouras G., Artikis A. and Vouros G. (2015). Probabilistic Event Calculus for Event Recognition. ACM Transactions on Computational Logic, 16, 2, Article 11, pp. 11:1-11:37. (link)

• Skarlatidis A., Paliouras G., Vouros G. and Artikis. (2011) A. Probabilistic Event Calculus based on Markov Logic Networks. Proceedings of International Symposium on Rules (RuleML@BRF), Springer. (link)

• Tuyen N. Huynh and Raymond J. Mooney. (2009). Max-Margin Weight Learning for Markov Logic Networks. In Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD-09). (link)

• Tuyen N. Huynh and Raymond J. Mooney. (2011). Online Max-Margin Weight Learning for Markov Logic Networks. In Proceedings of the Eleventh SIAM International Conference on Data Mining (SDM11). (link)

• Tuyen N. Huynh and Raymond J. Mooney. (2011). Online Structure Learning for Markov Logic Networks. In Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD 2011). (link)

Other references that have been mentioned in the examples:

• Hanks S. and McDermott D. Nonmonotonic Logic and Temporal Projection. Artificial Intelligence 33.3, 379-412. 1987. (link)

• Kowalski, R. and Sergot, M. (1986). A Logic-based Calculus of Events. New Generation Computing, 4(1):67–95. (link)

• Mueller, E. T. (2008). Event Calculus. In Handbook of Knowledge Representation, volume 3 of Foundations of Artificial Intelligence, pages 671–708. Elsevier. (link)

• Shanahan, M. (1999). The Event Calculus Explained. In Wooldridge, M. and Veloso, M., editors, Artificial Intelligence Today, volume 1600 of Lecture Notes in Computer Science, pages 409–430. Springer. (link)