Modelling Uncertainty in Agent Programming. - Johan Kwisthout; Mehdi Dastani (DALT, 2005)

Local Monotonicity in Probabilistic Networks. - Johan Kwisthout; Hans L. Bodlaender; Gerard Tel (ECSQARU, 2007)

The Computational Complexity of Monotonicity in Probabilistic Networks. - Johan Kwisthout (FCT, 2007)

The Computational Complexity of Sensitivity Analysis and Parameter Tuning. - Johan Kwisthout; Linda C. van der Gaag (UAI, 2008)

Joint attention and language evolution. - Johan Kwisthout; Paul Vogt; Pim Haselager; Ton Dijkstra (Connect. Sci., 2008)

Complexity results for enhanced qualitative probabilistic networks. - Johan Kwisthout; Gerard Tel (Int. J. Approx. Reason., 2008)

The Necessity of Bounded Treewidth for Efficient Inference in Bayesian Networks. - Johan Kwisthout; Hans L. Bodlaender; Linda C. van der Gaag (ECAI, 2010)

The computational costs of recipient design and intention recognition in communication. - Mark Blokpoel; Johan Kwisthout; Todd Wareham; Pim Haselager; Ivan Toni; Iris van Rooij (CogSci, 2011)

Ignorance is bliss: A complexity perspective on adapting reactive architectures. - Todd Wareham; Johan Kwisthout; Pim Haselager; Iris van Rooij (ICDL-EPIROB, 2011)

The Complexity of Finding kth Most Probable Explanations in Probabilistic Networks. - Johan Kwisthout; Hans L. Bodlaender; Linda C. van der Gaag (SOFSEM, 2011)

Bayesian Intractability Is Not an Ailment That Approximation Can Cure. - Johan Kwisthout; Todd Wareham; Iris van Rooij (Cogn. Sci., 2011)

Most probable explanations in Bayesian networks: Complexity and tractability. - Johan Kwisthout (Int. J. Approx. Reason., 2011)

Relevancy in Problem Solving: A Computational Framework. - Johan Kwisthout (J. Probl. Solving, 2012)

Predictive coding and the Bayesian brain: Intractability hurdles that are yet to be overcome. - Johan Kwisthout; Iris van Rooij (CogSci, 2013)

Constraints on Bayesian Explanation. - Johan Kwisthout; Iris van Rooij; Matteo Colombo; Carlos Zednik; William A. Phillips (CogSci, 2013)

Computational complexity analysis for cognitive scientists. - Iris van Rooij; Johan Kwisthout; Mark Blokpoel; Todd Wareham (CogSci, 2013)

Most Inforbable Explanations: Finding Explanations in Bayesian Networks That Are Both Probable and Informative. - Johan Kwisthout (ECSQARU, 2013)

Structure Approximation of Most Probable Explanations in Bayesian Networks. - Johan Kwisthout (ECSQARU, 2013)

Bridging the gap between theory and practice of approximate Bayesian inference. - Johan Kwisthout; Iris van Rooij (Cogn. Syst. Res., 2013)

Leaving Andy Clark's 'safe shores': Scaling predictive processing to higher cognition. - Johan Kwisthout; Maria Otworowska; Harold Bekkering; Iris van Rooij (CogSci, 2014)

Minimizing Relative Entropy in Hierarchical Predictive Coding. - Johan Kwisthout (Probabilistic Graphical Models, 2014)

Treewidth and the Computational Complexity of MAP Approximations. - Johan Kwisthout (Probabilistic Graphical Models, 2014)

What senses of agency can infants have? - Lorijn Zaadnoordijk; Sabine Hunnius; Marlene Meyer; Johan Kwisthout; Iris van Rooij (CogSci, 2015)

The developing sense of agency: Implications from cognitive phenomenology. - Lorijn Zaadnoordijk; Sabine Hunnius; Marlene Meyer; Johan Kwisthout; Iris van Rooij (ICDL-EPIROB, 2015)

Most frugal explanations in Bayesian networks. - Johan Kwisthout (Artif. Intell., 2015)

Tree-Width and the Computational Complexity of MAP Approximations in Bayesian Networks. - Johan Kwisthout (J. Artif. Intell. Res., 2015)

Causal learning in the Crib: A predictive processing formalization and babybot simulation. - Maria Otworowska; Lorijn Zaadnoordijk; Erwin de Wolff; Johan Kwisthout; Iris van Rooij (ICDL-EPIROB, 2016)

The mobile-paradigm as measure of infants' sense of agency? Insights from babybot simulations. - Lorijn Zaadnoordijk; Maria Otworowska; Johan Kwisthout; Sabine Hunnius; Iris van Rooij (ICDL-EPIROB, 2016)

The Parameterized Complexity of Approximate Inference in Bayesian Networks. - Johan Kwisthout (Probabilistic Graphical Models, 2016)

Perception is in the Details: A Predictive Coding Account of the Psychedelic Phenomenon. - Sarit Pink-Hashkes; Iris van Rooij; Johan Kwisthout (CogSci, 2017)

Community-based influence maximization in social networks under a competitive linear threshold model. - Arastoo Bozorgi; Saeed Samet; Johan Kwisthout; Todd Wareham (Knowl. Based Syst., 2017)

Finding Dissimilar Explanations in Bayesian Networks: Complexity Results. - Johan Kwisthout (BNCAI, 2018)

Approximate inference in Bayesian networks: Parameterized complexity results. - Johan Kwisthout (Int. J. Approx. Reason., 2018)

Rational analysis, intractability, and the prospects of 'as if'-explanations. - Iris van Rooij; Cory D. Wright; Johan Kwisthout; Todd Wareham (Synth., 2018)

Cognition and intractability: a guide to classical and parameterized complexity analysis. - Iris van Rooij; Mark Blokpoel; Johan Kwisthout; Todd Wareham (Cambridge University Press, 2019)

A spiking neural algorithm for the Network Flow problem. - Abdullahi Ali; Johan Kwisthout (CoRR, 2019)

Processing of Prediction Errors in Mentalizing Areas. - Lieke Heil; Olympia Colizoli; Egbert Hartstra; Johan Kwisthout; Stan van Pelt; Iris van Rooij; Harold Bekkering (J. Cogn. Neurosci., 2019)

A Spiking Neuron Implementation of Genetic Algorithms for Optimization. - Siegfried Ludwig; Joeri Hartjes; Bram Pol; Gabriela Rivas; Johan Kwisthout (BNAIC/BENELEARN (Selected Papers), 2020)

From Models of Cognition to Robot Control and Back Using Spiking Neural Networks. - Stefan Iacob; Johan Kwisthout; Serge Thill (Living Machines, 2020)

On the computational power and complexity of Spiking Neural Networks. - Johan Kwisthout; Nils Donselaar (NICE, 2020)

Certainly Strange: A Probabilistic Perspective on Ignorance. - Erwin de Wolff; Iris van Rooij; Johan Kwisthout (CogSci, 2021)

Explainable AI Using MAP-Independence. - Johan Kwisthout (ECSQARU, 2021)

Parameterized Completeness Results for Bayesian Inference. - Hans L. Bodlaender; Nils Donselaar; Johan Kwisthout (PGM, 2022)

Speeding up approximate MAP by applying domain knowledge about relevant variables. - Johan Kwisthout (PGM, 2022)

Parameterized Complexity Results for Bayesian Inference. - Hans L. Bodlaender; Nils Donselaar; Johan Kwisthout (CoRR, 2022)

Benchmarking energy consumption and latency for neuromorphic computing in condensed matter and particle physics. - Dominique J. Kösters; Bryan A. Kortman; Irem Boybat; Elena Ferro; Sagar Dolas; Roberto de Austri; Johan Kwisthout; Hans Hilgenkamp; Theo Rasing; Heike Riel; Abu Sebastian; Sascha Caron; et al. (CoRR, 2022)

From representations in predictive processing to degrees of representational features. - Danaja Rutar; Wanja Wiese; Johan Kwisthout (Minds Mach., 2022)

Bayesian Integration of Information Using Top-Down Modulated WTA Networks. - Otto van der Himst; Leila Bagheriye; Johan Kwisthout (CoRR, 2023)

Motivating explanations in Bayesian networks using MAP-independence. - Johan Kwisthout (Int. J. Approx. Reason., 2023)

Neural Population Decoding and Imbalanced Multi-Omic Datasets for Cancer Subtype Diagnosis. - Charles Theodore Kent; Leila Bagheriye; Johan Kwisthout (BIOSTEC (1), 2024)

Solving Minimum Spanning Tree Problem in Spiking Neural Networks: Improved Results. - Simon Janssen; Stijn Groenen; Simon Reichert; Johan Kwisthout (ICONS, 2024)

Preface. - Johan Kwisthout; Silja Renooij (PGM, 2024)

Cancer Subtype Identification Through Integrating Inter and Intra Dataset Relationships in Multi-Omics Data. - Mark Peelen; Leila Bagheriye; Johan Kwisthout (IEEE Access, 2024)

Speeding up approximate MAP by applying domain knowledge about relevant variables. - Johan Kwisthout; Andrew Schroeder (CoRR, 2024)

Advancements in Real-Time Oncology Diagnosis: Harnessing AI and Image Fusion Techniques. - Leila Bagheriye; Johan Kwisthout (CoRR, 2025)