International Workshop on Logic, Rationality and Interaction

Logic, Rationality, and Interaction pp 40-52

Learning Actions Models: Qualitative Approach

Conference paper

DOI: 10.1007/978-3-662-48561-3_4

Part of the Lecture Notes in Computer Science book series (LNCS, volume 9394)
Cite this paper as:
Bolander T., Gierasimczuk N. (2015) Learning Actions Models: Qualitative Approach. In: van der Hoek W., Holliday W., Wang W. (eds) Logic, Rationality, and Interaction. Lecture Notes in Computer Science, vol 9394. Springer, Berlin, Heidelberg


In dynamic epistemic logic, actions are described using action models. In this paper we introduce a framework for studying learnability of action models from observations. We present first results concerning propositional action models. First we check two basic learnability criteria: finite identifiability (conclusively inferring the appropriate action model in finite time) and identifiability in the limit (inconclusive convergence to the right action model). We show that deterministic actions are finitely identifiable, while non-deterministic actions require more learning power—they are identifiable in the limit. We then move on to a particular learning method, which proceeds via restriction of a space of events within a learning-specific action model. This way of learning closely resembles the well-known update method from dynamic epistemic logic. We introduce several different learning methods suited for finite identifiability of particular types of deterministic actions.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  1. 1.DTU ComputeTechnology University of DenmarkCopenhagenDenmark
  2. 2.ILLCUniversity of AmsterdamAmsterdamThe Netherlands

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