Report on Research
The goal of this task is to produce a report about a research subject
in the area of Logic Programming, Inductive Logic Programming, or
Statistical Relational Learning. The report may be based on a paper,
book or book chapter, or tutorial. It should demonstrate:
- Understanding of the main concepts of the subject matter: the
student should have a solid grasp of the key concepts in the
research.
- Fundamented opinion of the research: the student should have its
own opinion of the research, not just repeat the author's
claims. The student's opinion should be well argumented.
We will value:
- discussion of related literature, and/or,
- experimental validation of the results being discussed, and/or,
- discussion on whether the work is benefitial to the student's
own research.
Evaluation
The report must be written by a single student. Each student must
choose its own subject. They must be sent by email
to Ricardo Rocha until
February 22, 2008. The report should consist of up to 25
pages, written in english and should always include:
- Student identification.
- Report Title.
- An Introduction, stating the research subject and motivating
the report.
- Conclusion
As discussed above, we will value the report on whether it shows
understanding of and critical opinion of the research. Further,
reports must be concise and clear.
Originality on the discussion of the related work, experiments with
the subject of the report, and being able to connect to one's work
will improve the score.
Examples
The following papers are good starting points:
- Efficient Access Mechanisms for Tabled Logic Programs. I. V.
Ramakrishnan, P. Rao, K. Sagonas, T. Swift and
D. S. Warren. Journal of Logic Programming, volume 38(1), pages
31-54. 1999.
- An Abstract Machine for Tabled Execution of Fixed-Order
Stratified Logic Programs. K. Sagonas and T. Swift. ACM
Transactions on Programming Languages and Systems, volume 20(3),
pages 586-634. 1998.
- ILP: Just Do It. David Page. Lecture Notes in Computer Science,
volume 1861, pages 25-40. 2000.
- A study of two sampling methods for analysing large datasets
with ILP, Ashwin Srinivasan. Data Mining and Knowledge
Discovery, volume 3(1), pages 95-123. 1999.
- Compressing Probabilistic Logic Programs, Luc de Raedt,
K. Kersting, A. Kimmig, K. Revoredo, and H. Toivonen. Machine
Learning, volume 70(2-3), pages 151-168. 2008.
- Markov Logic Networks, P Domingos and Matt Richardson. Machine
Learning, 62, 107-136, 2006.
Please do feel free to consider other works!
Good Luck!