Institute for Web Science and Technologies · Universität Koblenz
Institute WeST
This course is from a past or future semester. If you are looking for current courses, go to the course overview.

Seminar "Knowledge in Flux"

[go to overview]

Winter Term 2020 / 2021

Knowledge is undoubtably one of the most important components for devising intelligent systems. Representing knowledge and reasoning about it is a tough problem, let alone deal with knowledge dynamics. In this seminar, you will have contact with several topics related to knowledge dynamics such as Epistemic Logic (to represent and reason about knowledge), Doxastic Logic (to deal with beliefs) and Dynamic Logics (to deal with epistemic/doxastic changes). We will also address topics related to the theory of Belief Change: a field within AI that studies how an agent should change its knowledge in the light of new information (possibly contradictory with its current beliefs).

In this seminar, you will write a paper and make a presentation about a chosen topic related to knowledge dynamics. A suggestion of topics are listed below, but if you have interest in other topic that falls within this seminar scope, but it is not listed below, please send an e-mail to Dr. Jandson S Ribeiro to discuss about it.


If you have interest in registering on this seminar, please send an e-mail to Dr. Jandson S Ribeiro by 31st August with a list of up to three preferred topics (on preference order). This list will be used to allocate the topics to the participants. A first online meeting will be scheduled to discuss the allocation of the topics. Students shall write a short paper about the chosen topic and present a seminar in an online session. Each seminar will have around 15 minutes for presentation and 5 minutes for questions. Student shall present their own seminar, but shall also participate on the discussion of other students’ presentation (at least one session).


  1. Dynamic Doxastic Logic: [1-4]
  2. Dynamic Epistemic Logic: [5,6]
  3. Non-Monotonic Reasoning and Belief Change: [7,10]
  4. Belief Update: [11-13]
  5. Multiple Belief Change: [14-17]
  6. Belief Base Revision/Contraction: [18-22]
  7. Pseudo Contraction: [23-25]
  8. Iterative Belief Change: [26-28]
  9. Algorithms for Belief Change: [29-32]
  10. Reasoning about Actions: [33-34]
  11. Principle of Relevance: [35-37]
  12. Contraction in Horn Logic: [38-41]
  13. Argumentation in Belief Change: [42-44]
  14. Belief revision and Conditional Sentences: [45]
  15. Non-Prioritized Belief Change: [46,47]


  1. Segerberg, K. (1995). Belief revision from the point of view of doxastic logic. Bulletin of the IGPL, 3, 535–553.
  2. Segerberg, K. (1995). Some questions about hypertheories. In S. O. Hansson, & W. Rabinowicz (Eds.), Logic for a change. Uppsala prints and preprints in philosophy (Vol. 9, pp. 136–153). Dep. of Philosophy, Uppsala University.
  3. Segerberg, K. (1996). Two traditions in the logic of belief: Bringing them together. Uppsala Prints and Preprints in Philosophy 11, Dep. of Philosophy, Uppsala University.
  4. Pacuit, E. (2013). Dynamic epistemic logic I: Modeling knowledge and belief. Philosophy Compass, 8(9), 798-814.
  5. Van Ditmarsch, H., Halpern, J. Y., Van Der Hoek, W., & Kooi, B. (2015). An introduction to logics of knowledge and belief. arXiv preprint arXiv:1503.00806.
  6. Makinson, D., & Gärdenfors, P. (1991). Relations between the logic of theory change and nonmonotonic logic. In The logic of theory change (pp. 183-205). Springer, Berlin, Heidelberg.
  7. Dix, J., & Makinson, D. (1992). The relationship between KLM and MAK models for nonmonotonic inference operations. Journal of Logic, Language and Information, 1(2), 131-140.
  8. Gärdenfors, P., & Makinson, D. (1994). Nonmonotonic inference based on expectations. Artif. Intell., 65(2), 197-245.
  9. Kraus, S., Lehmann, D., & Magidor, M. (1990). Nonmonotonic reasoning, preferential models and cumulative logics. Artificial intelligence, 44(1-2), 167-207.
  10. Fermé, E., & Rodríguez, R. (2006). DFT and belief revision. Análisis filosófico, 26(2), 373-393.
  11. Katsuno, H., & Mendelzon, A. (1992). On the difference between updating a knowledge base and revising it. In P. GaÃàrdenfors (Ed.), Belief revision. Cambridge tracts in theoretical computer science (Vol. 29, pp. 183-203). Cambridge: Cambridge University Press.
  12. Herzig, A., & Rifi, O. (1999). Propositional belief base update and minimal change. Artificial Intelligence, 115(1), 107-138.
  13. Winslett, M. (1988). Reasoning about action using a possible models approach. In AAAI (pp. 89–93).
  14. Zhang, D. (1996). Belief revision by sets of sentences. Journal of Computer Science and Technology, 11(2), 108–125.
  15. Fuhrmann, A., & Hansson, S. O. (1994). A survey of multiple contraction. Journal of Logic, Language and Information, 3, 39-74.
  16. Hansson, S. O. (2010). Multiple and iterated contraction reduced to single-step single- sentence contraction. Synthese, 173, 153-177.
  17. D. Zhang and N. Y. Foo, “Infinitary belief revision,” J. Philosophical Logic, vol. 30, no. 6, pp. 525–570, 2001.
  18. Fuhrmann, A., & Fuhrmann, A. (1997). An essay on contraction. Stanford: CSLI Publications.
  19. Hansson, S. O. (1994). Kernel contraction. Journal of Symbolic Logic, 845-859.
  20. Rott, H., & Hansson, S. O. (2014). Safe contraction revisited. In David Makinson on classical methods for non-classical problems (pp. 35-70). Springer, Dordrecht.
  21. Rott, H. (1995). Just because. Taking belief bases very seriously.. In S. O. Hansson, & W. Rabinowicz (Eds.), Logic for a change (Vol. 9, pp. 106-124). Uppsala Prints and Preprints in Philosophy. Dep. of Philosophy, Uppsala University.
  22. Williams, M.-A. (1992). Two operators for theory bases. In Proc. Australian joint artificial intelligence conference (pp. 259–265).
  23. Hansson, S. O. (1993). Changes of disjunctively closed bases. Journal of Logic, Language and Information, 2(4), 255-284.
  24. Nebel, B. (1989). A Knowledge Level Analysis of Belief Revision. KR, 89, 301-311.
  25. Ribeiro, M. M., & Wassermann, R. (2008, August). Degrees of recovery and inclusion in belief base dynamics. In Non-Monotonic Reasoning (p. 43).
  26. Aravanis, T. I., Peppas, P., & Williams, M. A. (2019, August). Observations on Darwiche and Pearl's Approach for Iterated Belief Revision. In IJCAI (pp. 1509-1515).
  27. Darwiche,A.,&Pearl,J.(1996).Onthelogicofiteratedbeliefrevision.ArtificialIntelligence, 89, 1–29.
  28. Jin, Y., & Thielscher, M. (2007). Iterated belief revision, revised. Artificial Intelligence, 171, 1-18.
  29. Hunter, A., & Agapeyev, J. GenC: A Fast Tool for Applications Involving Belief Revision.
  30. Cóbe, R., & Wassermann, R. (2015, July). Ontology Repair Through Partial Meet Contraction. In DARe@ IJCAI.
  31. Ribeiro, M. M., & Wassermann, R. (2009). Base revision for ontology debugging. Journal of Logic and Computation, 19(5), 721-743.
  32. Wassermann, R. (2000). An algorithm for belief revision. In In Proceedings of the Seventh International Conference on Principles of Knowledge Representation and Reasoning (KR2000.
  33. Baral, C., Bolander, T., van Ditmarsch, H., & McIlrath, S. (2017). Epistemic planning (Dagstuhl seminar 17231). In Dagstuhl Reports (Vol. 7, No. 6). Schloss Dagstuhl-Leibniz-Zentrum fuer Informatik.
  34. Baral, C., Gelfond, G., Pontelli, E., & Son, T. C. (2012). An action language for reasoning about beliefs in multi-agent domains. In Proceedings of the 14th international workshop on non-monotonic reasoning (Vol. 4).
  35. Parikh, R. (1999). Beliefs, belief revision, and splitting languages. Logic, language and computation, 2(96), 266-268.
  36. Kourousias, G., & Makinson, D. (2007). Parallel interpolation, splitting, and relevance in belief change. Journal of symbolic logic, 72(3), 994-1002.
  37. Ribeiro, M. M., Wassermann, R., Flouris, G., & Antoniou, G. (2013). Minimal change: Relevance and recovery revisited. Artificial Intelligence, 201, 59-80.
  38. Delgrande, J. (2008). Horn clause belief change: Contraction functions. In G. Brewka, & J. Lang (Eds.), Proceedings of the eleventh international conference on the principles of knowledge representation and reasoning (pp. 156-165). Sydney.
  39. Delgrande, J., & Wassermann, R. (2010). Horn clause contraction functions: Belief set and belief base approaches. In International conference on the principles of knowledge representation and reasoning, Toronto.
  40. Booth, R., Meyer, T., & Varzinczak, I. J. (2009). Next steps in propositional horn contraction. In Proceedings of the international joint conference on artificial intelligence (pp. 702-707).
  41. Booth, R., Meyer, T., Varzinczak, I. J. & Wassermann, R. (2010). A contraction core for horn belief change: Preliminary report. In Proceedings of the 13th international workshop on non-monotonic reasoning (NMR), Toronto.
  42. Falappa, M. A., Kern-Isberner, G., & Simari, G. R. (2009). Belief revision and argumentation theory. In G. Simari, & I. Rahwan (Eds.), Argumentation in artificial intelligence (pp. 341- 360). US: Springer.
  43. Falappa, M., Kern-Isberner, G., & Simari, G. R. (2002). Belief revision, explanations and defeasible reasoning. Artificial Intelligence, 141, 1–28.
  44. Baumann, R., & Brewka, G. (2015, June). AGM meets abstract argumentation: Expansion and revision for Dung frameworks. In Twenty-Fourth International Joint Conference on Artificial Intelligence.
  45. Kern-Isberner, G. (2004). A thorough axiomatization of a principle of conditional preservation in belief revision. Annals of Mathematics and Artificial intelligence, 40(1-2), 127-164.
  46. Fermé, E. L., & Hansson, S. O. (2001). Shielded contraction. In Frontiers in belief revision (pp. 85-107). Springer, Dordrecht.
  47. Fermé, E., Mikalef, J., & Taboada, J. (2003). Credibility-limited functions for belief bases. Journal of Logic and Computation, 13(1), 99-110.


  • Alumnus
  • B 110
  • +49 261 287-2756