Title: The Simplest Kind of Learning Abstract: In this talk I will focus on finite identifiability which, in a certain order of things, can be considered to be the simplest kind of learning. To finitely identify a concept means to be able to recognise it with certainty after receiving some finite amount of data consistent with this concept. Such a finite sample that suffices for finite identification is called definite finite tell-tale set (DFTT). I will start with the characterisations of finite identifiability provided in 1992 by Mukouchi and, independently, by Lange and Zeugmann. These characterisations are in the style of Gold’s identifiability and Angluin’s finite tell-tales. In the main part of the talk I will recall the results obtained (for positive data) by Gierasimczuk and de Jongh (2013), which where developed further (for the case of complete data) by de Jongh and Vargas Sandoval (2018). Even though this concept of learning is very simple, it turned out to be very useful as a starting point in understanding long-term dynamics of updates in Dynamic (Epistemic) Logic, for which a discrete and exact approach to learning is very adeqaute (see Gierasimczuk 2009, Dégremont and Gierasimczuk 2011). Finite identifiability has also proved instrumental in setting up the new framework for exact learning of action models in Dynamic Epistemic Logic (Bolander and Gierasimczuk 2018). References: - Mukouchi, Y.: Characterization of finite identification. In: AII’92: Proceedings of the International Workshop on Analogical and Inductive Inference, Springer-Verlag (1992) 260–267. - Lange, S., Zeugmann, T.: Types of monotonic language learning and their characterization. In: COLT’92: Proceedings of the 5th Annual ACM Conference on Computational Learning Theory, ACM (1992) 377–390. - Gierasimczuk, N., Learning by Erasing in Dynamic Epistemic Logic, A.H. Dediu, A.M. Ionescu, and C. Martin-Vide (Eds.): LATA 2009, LNCS 5457, pp. 362-373, 2009. - Gierasimczuk, N., Bridging Learning Theory and Dynamic Epistemic Logic, Synthese 169 (2009), pp. 371-384. - Dégremont, C., Gierasimczuk, N.: Finite identification from the viewpoint of epistemic update. Information and Computation 209(3) (2011) 383–396. - Gierasimczuk, N. and de Jongh, D., On the Complexity of Conclusive Update, The Computer Journal 56(3):365-377, 2013. - Bolander, T. and Gierasimczuk, N., Learning to Act: Qualitative Learning of Deterministic Action Models, Journal of Logic and Computation, Volume 28, Issue 2, 2018, Pages 337-365. - De Jongh, D., and Vargas Sandoval A., Finite Identification with Positive and with Complete Data, International Tbilisi Symposium on Logic, Language, and Computation, TbiLLC 2018: Language, Logic, and Computation, pp 42-63.