The Problem of Learning the Semantics of Quantifiers. - Nina Gierasimczuk (TbiLLC, 2005)

Identification through Inductive Verification. - Nina Gierasimczuk (TbiLLC, 2007)

Learning by Erasing in Dynamic Epistemic Logic. - Nina Gierasimczuk (LATA, 2009)

Can Doxastic Agents Learn? On the Temporal Structure of Learning. - Cédric Dégremont; Nina Gierasimczuk (LORI, 2009)

Learning and Teaching as a Game: A Sabotage Approach. - Nina Gierasimczuk; Lena Kurzen; Fernando R. Velázquez-Quesada (LORI, 2009)

Games for Learning: A Sabotage Approach. - Nina Gierasimczuk; Lena Kurzen; Fernando R. Velázquez-Quesada (MALLOW, 2009)

Branching Quantification v. Two-way Quantification. - Nina Gierasimczuk; Jakub Szymanik (J. Semant., 2009)

Bridging learning theory and dynamic epistemic logic. - Nina Gierasimczuk (Synth., 2009)

Invariance Properties of Quantifiers and Multiagent Information Exchange. - Nina Gierasimczuk; Jakub Szymanik (MOL, 2011)

Belief revision as a truth-tracking process. - Alexandru Baltag; Nina Gierasimczuk; Sonja Smets (TARK, 2011)

A note on a generalization of the Muddy Children puzzle. - Nina Gierasimczuk; Jakub Szymanik (TARK, 2011)

Finite identification from the viewpoint of epistemic update. - Cédric Dégremont; Nina Gierasimczuk (Inf. Comput., 2011)

Logical and psychological analysis of deductive mastermind. - Nina Gierasimczuk; Han L. J. van der Maas; Maartje E. J. Raijmakers (ESSLLI Logic & Cognition Workshop, 2012)

On the Complexity of Conclusive Update. - Nina Gierasimczuk; Dick de Jongh (Comput. J., 2013)

An Analytic Tableaux Model for Deductive Mastermind Empirically Tested with a Massively Used Online Learning System. - Nina Gierasimczuk; Han L. J. van der Maas; Maartje E. J. Raijmakers (J. Log. Lang. Inf., 2013)

Logic and Learning. - Nina Gierasimczuk; Vincent F. Hendricks; Dick de Jongh (Johan van Benthem on Logic and Information Dynamics, 2014)

Quantifier Learning: An Agent-based Coordination Model. - Dariusz Kalocinski; Nina Gierasimczuk; Marcin Mostowski (AAMAS, 2015)

Collective Learning in Games through Social Networks. - Sanne Kosterman; Nina Gierasimczuk (SocInf@IJCAI, 2015)

Learning Actions Models: Qualitative Approach. - Thomas Bolander; Nina Gierasimczuk (LORI, 2015)

On the Solvability of Inductive Problems: A Study in Epistemic Topology. - Alexandru Baltag; Nina Gierasimczuk; Sonja Smets (TARK, 2015)

Learning Action Models: Qualitative Approach. - Thomas Bolander; Nina Gierasimczuk (CoRR, 2015)

Book Review: Neil Tennant, Changes of Mind: An Essay on Rational Belief Revision. - Nina Gierasimczuk (Stud Logica, 2015)

Interactive Semantic Alignment Model: Social Influence and Local Transmission Bottleneck. - Dariusz Kalocinski; Marcin Mostowski; Nina Gierasimczuk (J. Log. Lang. Inf., 2018)

Learning to act: qualitative learning of deterministic action models. - Thomas Bolander; Nina Gierasimczuk (J. Log. Comput., 2018)

Cognitive Complexity of Logical Reasoning in Games: Automated Theorem Proving Perspective. - Katrine Bjørn Pedersen Thoft; Nina Gierasimczuk (CogSci, 2019)

A dynamic logic for learning theory. - Alexandru Baltag; Nina Gierasimczuk; Aybüke Özgün; Ana Lucia Vargas Sandoval; Sonja Smets (J. Log. Algebraic Methods Program., 2019)

Truth-Tracking by Belief Revision. - Alexandru Baltag; Nina Gierasimczuk; Sonja Smets (Stud Logica, 2019)

Learning and Modal Logic: There and Back Again. - Nina Gierasimczuk (AiML, 2020)

Learning to Act and Observe in Partially Observable Domains. - Thomas Bolander; Nina Gierasimczuk; Andrés Occhipinti Liberman (CoRR, 2021)

Inductive Inference and Epistemic Modal Logic (Invited Talk). - Nina Gierasimczuk (CSL, 2023)

Learning by Intervention in Simple Causal Domains. - Katrine Bjørn Pedersen Thoft; Nina Gierasimczuk (DaLí, 2023)

Cognitive Bias and Belief Revision. - Panagiotis Papadamos; Nina Gierasimczuk (TARK, 2023)

Convexity and Monotonicity in Language Coordination: Simulating the Emergence of Semantic Universals in Populations of Cognitive Agents. - Nina Gierasimczuk; Dariusz Kalocinski; Franciszek Rakowski; Jakub Uszynski (J. Log. Lang. Inf., 2023)

SymDQN: Symbolic Knowledge and Reasoning in Neural Network-based Reinforcement Learning. - Ivo Amador; Nina Gierasimczuk (CoRR, 2025)